REMOVAL OF METALS IN COMBINED TREATMENT SYSTEMS
J.VJ. Patterson, et al
Pritzker Department of Environmental Engineering
Chicago, IL
Jun 33
U.S. DEPARTMENT OF COMMERCE
National Technical Information Service
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EPA-600/2-83-051
June 1983
REMOVAL OF METALS IN
COMBINED TREATMENT SYSTEMS
by
James W.. Patterson
Prasad Kodukula
Tpshiro Ar.atani
Pritzker Department of Environmental Engineering
Illinois Institute of Technology
Chicago, Illinois - 60616
Grant No. R 804538
Project Officer
Thomas E. Short, Jr.
ROBERT S. KERR ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ADA, OKLAHOMA 74820
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/2-83-051
3. RECIPIENT'S ACCESSION NO.^.
PBS 3 ?2607B
4. TITLE AND SUBTITLE
Removal of Metals in Combined Treatment Systems
5. REPORT DATE
June 1983
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
James W. Patterson, Prasad Kodukula, and Toshiro
Aratani
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Pritzker Dept. of Environmental Engineering
Illinois Institute of Technology
Chicago, Illinois 60616
10. PROGRAM ELEMENT NO.
CBGB1C
R804538.
1Z_SeONSORING AGENCY NAME AND ADOBES? , _ ,
Kooert S. Kerr Environmental Research Laboratory
Office of Research and Development:
U.S. Environmental Protection Agency
'Ada,'OK 74820
13. TYPE OF REPORT AND PERIOD COVERED
rinal./7-12-76 - 12-21-78
14. SPONSORING AGENCY CODE
EPA/600/15
15. SUPPLEMENTARY NOTES
16. ABSTRACT This project assessed the variables influencing the removal of eight metals
through combined industrial-municipal treatment plants. The eight metals investi-
gated were: aluminum, cadmium, chromium, copper, iron, lead, nickel, and zinc.
The metals were studied at subtoxic influent concentrations, and the interrelation-
ships which influence metal removal were assessed. Batch studies on raw sewage and
activated sludge identified and defined the impact of individual parameters or
concentrations and of combinations of parameters on metals removal.
Eight pilot treatment plants,.each consisting of primary clarifier, aeration basin,
and secondary clarifier, were operated at varying influent metal levels to study the
effect of significant variables indicated from the batch studies.
The results of this project indicate that the removal of metals in combined industrial
municipal treatment systems is influenced by a number of wastewater and treatment
plant operation characteristics. The segregation of influent metals between the
sludge (primary and secondary) phases and the plant effluent can be predicted, based
upon the relationships presented in this report.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFlERS/OPEN ENDED TERMS C. COS AT I Field/Group
Activated Sludge
Sewage treatment
Combined industrial/
municipal
Joint treatment
Pretreatment
05D
8. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
27k
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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DISCLAIMER
Although the research described in this article has been funded in whole
or in part by the United States Environmental Protection Agency through
grant to Illinois Institute of Technology, it has not been subjected to the
Agency's required peer and policy review and therefore does not necessarily
reflect the views of the Agency and no official endorsement should be
inferred. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
ii
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FOREWORD
The Environmental Protection Agency is charged by Congress
to protect the Nation's land, air and water systems. Under a
mandate of national environmental laws focused on air and water
quality, solid waste management and the control of toxic sub-
stances, pesticides, noise, and radiation, the Agency strives--to-
formulate and implement actions which lead to a. compatible bal-
ance between human activities and the ability of natural systems
to support and nurture life. In partial response to these man-
dates, the Robert S. Kerr Environmental Research Laboratory,
Ada, Oklahoma, is charged with the mission to manage research
programs to investigate the nature, transport, fate, and manage-
ment of pollutants in ground water and to develop and demonstrate
technologies for treating wastewaters with soils and other nat-
ural systems for controlling pollution from irrigated crop and
animal production agricultural activities; for developing and
demonstrating cost-effective land treatment systems for the
environmentally safe disposal of solid and hazardous wastes.
This report is a study of the mechanism of metals uptake by
municipal treatment systems which receive a large amount of in-
dustrial wastes. Thus, the degree of "susceptibility" of heavy
metals ions to municipal waste treatment process was determined.
The results of this project indicate that the removal of metals
in municipal systems is determined by a number of wastewater and
treatment plant operation characteristics. The distribution of
influent metals between the sludge phases on the plant effluent
can be predicted, based upon the relationships presented.
Clinton W. Hall
Director
Robert S. Kerr Environmental
Research Laboratory
111
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ABSTRACT
This project assessed the variables influencing the
removal of eight metals through combined industrial-municipal
treatment plants. The eight metals investigated were:
Aluminum Iron
Cadmium Lead
Chromium Nickel
. Copper Zinc
The metals were studied at sub-toxic influent concentrations,
and the interrelationships which influence metal removal were
assessed.
The research was performed in two phases. Phase I involved
batch studies on raw sewage and activated sludge to identify
and define the impact of individual parameters or concentrations
and of combinations of parameters on metals removal. These
batch studies consisted of three parts. In Part I, metal
solubility in filtered raw sewage and secondary effluent was
determined as a function of pH. Part II investigated the
equilibrium adsorption of the test metals onto primary sewage
solids and onto activated sludge solids. In Part III, the
effect of sewage variables such as detergent and ammonia con-
centration on metal adsorption was evaluated. In Phase II,
eight pilot treatment plants, each consisting of primary clari-
fier, aeration basin, and secondary clarifier, were operated at
varying influent metal levels to study the effect of signifi-
cant variables indicated from the Phase I results.
The results of this project indicate that the removal of
metals in combined industrial-municipal treatment systems is
influenced by a number of wastewater and treatment plant
operation characteristics. The segregation of influent metals
between the sludge (primary and secondary) phases and the plant
effluent can be predicted, based upon the relationships pre-
sented in this report.
This report was submitted in fulfillment of Grant
No. 804538 by Illinois Institute of Technology under the
sponsorship of the U.S. Environmental Protection Agency.
iv
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TABLE OF CONTENTS
Foreword iii
Abstract iv
Figures vi
Tables xi
1. ' Introduction ......... 1
2. Conclusions 3
3. Recommendations. . 10
4.. Historical Perspective ... 12
5. Objectives 33
A. Goal of the Research Study 33
B. Specific Objectives . 33
6. Methods and Procedures 35
7. Results and Discussion 51
Solubility of Metals 51
Sorption of Metals 61
Effect of Waste Parameters on Metals
Distribution 85
Metals Distribution in Conventional
Activated Sludge Systems 103
8. Model Development 163
Prediction of Metals Distribution . . . 163
Process Models 176
References 188
Appendices
A. Summary Tables of Average Operational
Characteristics of Pilot Activated Sludge
SystemsTreatment Nos. 1 Through 39 .. . . 196
B. Correlation of Metals Distribution Data
With Predictive Models 236
C. Development of Predictive Models .- 254
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FIGURES
Number
1 Schematic of a typical municipal sewage treat-
ment plant illustrating the liquid and solid
phase pathways 19
2 Correlation of effluent heavy metals and
effluent BODg 31
3 Correlation of effluent heavy metals and
effluent suspended solids 32
4 Schematic of batch experiments set up to study
the minimum solubility of metals in tap water,
raw sewage, and activated sludge mixed liquor 37
5 Schematic of batch experiments set up to study
the adsorption of metals to solids in raw
sewage and activated sludge mixed liquor 39
6 Flow schematic of continously-run laboratory-
scale unit 48
7 Change in soluble metal concentration with
respect to time: Cadmium in raw sewage at
negligible sulfide concentration.. .. 52
8 .Change in soluble metal concentration with
respect to time: Cadmium in raw sewage at
sulfide = 1 mg/1 53
9 Change in soluble metal concentration with
...... respect to time: Cadmium in raw sewage at
sulfide = 10 mg/1 54
10 Change in soluble metal concentration with
respect to time: Cadmium in activated sludge
mixed liquor at negligible sulfide concentra-
tion 55
vi
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Number Page
11 Change in soluble metal concentration with
respect to time: Cadmium in activated sludge
mixed liquor at sulfide = 1 mg/1 56
12 Change in soluble metal concentration with
respect to time: Cadmium in activated sludge
mixed liquor at sulfide = 10 mg/1 57
13 Solubility curves for aluminum in tap water,
raw sewage, and activated sludge mixed liquor 65
14 Solubility curves for cadmium in tap water,
raw sewage, and activated sludge mixed liquor.... 66
15 Solubility curves for chromium in tap water,
raw sewage, and activated sludge mixed liquor..... 67
16 Solubility curves for copper in tap water,
raw sewage, and activated sludge mixed liquor..... 68
17 Solubility curves for iron 'in' tap water,
raw sewage, and activated sludge mixed liquor 69
18 Solubility curves for lead in tap water,
raw sewage, and activated sludge mixed liquor..... 70
19 Solubility curves for mercury in tap water,
raw sewage, and activated sludge mixed liquor 71
20 Solubility curves for nickel in tap water,
raw sewage, and activated, sludge mixed liquor 72
21 Solubility curves for zinc in tap water,
raw sewage, and activated sludge mixed liquor 73
22 Change in soluble cadmium concentration in raw
sewage after the addition of the metal below
its solubility limit 76
23 Change in soluble cadmium concentration in
activated sludge mixed liquor after the
addition of the metal below its solubility
limit 77
24 Adsorption isotherms for metals in raw sewage 78
25 Adsorption isotherms for metals in activated
sludge mixed liquor 79
vn
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Number Page
26 Freundlich adsorption isotherms for metals in
raw sewage 82
27 Freundlich adsorption isotherms for metals in
activated sludge mixed liquor 83
28 Relationship between total aluminum concentrations
in raw sewage and primary effluent. . . 109
29 Relationship between total cadmium concentrations
in raw sewage and primary effluent 110
30 Relationship between total copper concentrations
in raw sewage and primary effluent Ill
.31 Relationship between total chromium concentrations.
in raw sewage and primary effluent 112
32 Relationship between total iron concentrations
in raw sewage and primary effluent 113
33 Relationship between total lead concentrations
in raw sewage and primary effluent 114
34 Relationship between total nickel concentrations
in raw sewage and primary effluent 115
35 Relationship between total zinc concentrations
in raw sewage and primary effluent 116
36 Relationship between the removals of TSS and
sludge bound cadmium 118
37 Relationship between the removals of TSS and
sludge bound copper. 119
38 Relationship between the removals of TSS and
sludge bound nickel 120
39 Relationship between the removals of TSS and
sludge bound zinc 121
40 Relationship between total cadmium concentra-
tions in primary effluent and mixed liquor 122
41 Relationship between the total cadmium concen-
trations in mixed liquor and secondary effluent... 123
42 Metal adsorption isotherms for raw sewage 125
viii
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Number
Page
43 Metal adsorption isotherms for primary effluent... 126
44 Metal adsorption isotherms for mixed liquor....... 127
45 Metal adsorption isotherms for secondary effluent. 128
46 Adsorption isotherm for nickel in mixed liquor.... 131
47 Adsorption isotherm for aluminum in raw sewage.... 132
48 Adsorption isotherms of cadmium in raw sewage
at different VSS concentrations... .134
49 Adsorption isotherms of cadmium in primary
effluent at different VSS concentrations 135
50 Adsorption isotherms of cadmium in mixed
liquor at different TVSS concentrations 136
51 Adsorption isotherms of cadmium in secondary
effluent at different TVSS concentrations 137
52 Adsorption isotherms for aluminum in raw sewage
at different TVSS concentrations 138
53 Adsorption isotherms for aluminum in primary
effluent at different TVSS concentrations 139
54 Adsorption isotherms of aluminum in secondary
effluent at different TVSS concentrations 140
55 Adsorption isotherms for chromium in raw
sewage at different TVSS concentrations 141
56 Adsorption isotherms for chromium in primary
effluent at different TVSS concentrations 142
57 Adsorption isotherms for chromium in secondary
effluent at different VSS concentrations 143
58 Adsorption isotherms of copper in raw sewage
at different VSS concentrations 145
59 Adsorption isotherms of copper in primary
effuent at different VSS concentrations 145
60 Adsorption isotherms of copper in mixed
liquor at different VSS concentrations 146
ix
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Number
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
Adsorption isotherms of copper in secondary
effluent at different VSS concentrations
Adsorption isotherms of iron in raw sewage at
different VSS concentrations
Adsorption isotherms for iron in primary
effluent at different VSS concentrations
Adsorption isotherms of iron in secondary
effluent at different VSS concentrations.
Adsorption isotherms for lead in raw sewage
at different VSS concentrations.
Adsorption isotherms for lead in primary
effluent at different VSS concentrations
Adsorption isotherms for lead in secondary
effluent at different VSS concentrations
Adsorption isotherms of nickel in raw sewage
at different VSS concentrations.
Adsorption isotherms of nickel in primary
effluent VSS concentrations
Adsorption isotherms of nickel in mixed liquor
VSS concentrations
Adsorption isotherms of nickel in secondary
effluent VSS concentrations.
Adsorption isotherms of zinc in raw sewage
VSS concentrations .
Adsorption isotherms of zinc in primary
effluent VSS concentrations
Adsorption isotherms of zinc in mixed
liquor VSS concentrations
Adsorption isotherms of zinc in secondary
effluent VSS concentrations
Schematic of continuous flow combined treat-
ment system
Page
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
177
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TABLES
Number Page
1 Influent and effluent metals concentrations
in different POTWs 14
2 Summary of influent and effluent concentra-
tions of metals in selected treatment plants . 15
3 Summary xTf data collected on selected metals in
sewage sludges from various municipal wastewater
treatment plants 17
4 Removals.of selected metals during primary
treatment 21
5 Part III Experimental design 41
6 List of waste parameters and their levels
tested in Part III 42
7 Metals concentration in different metals com-
binations studies in Part III 43
8 Sample analyses performed in Part III .... 45
9 Summary of schedule of operation of continuously
pilot-scale activated sludge systems ..... 46
10 Average influent metals concentration (yg/1)
in raw sewage fed to 39 different activated
sludge systems . . . . 47
11 Correlation coefficients: pH vs. soluble
metal-concentration 58
12 Correlation coefficients: initial sulfide
concentration vs. soluble metal concentration
under equilibrium conditions ......... 62
13 pH of minimum solubilities of metals 74
xi
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Number Page
14 Order of concentration of metals in raw
sewage and activated sludge at 10 mg/1 metal
added 81
15 Average per cent metal removals due to
adsorption to sludge mass 84
16 AOV for aluminum in raw sewage 86
17 AOV for aluminum in mixed liquor 86
18 AOV for cadmium in raw sewage 87
19 AOV for cadmium in mixed liquor 87
20 AOV for chromium in raw sewage ....... 88
21 AOV for chromium in mixed liquor 88
22 AOV for copper in raw sewage 89
23 AOV for copper in mixed liquor 89
24 AOV for iron in raw sewage 90
25 AOV for iron in mixed liquor 90
26 AOV for lead, in raw sewage 91
27 AOV for lead in mixed liquor 91
28 AOV for nickel in raw sewage 92
29 AOV for nickel in Mixed liquor 92
30 AOV for zinc in raw sewage 93
31 AOV for zinc in mixed liquor ........ 93
32 Waste parameters whose treatment levels had
a significant effect on final soluble metal
concentration 94
33 Studentized test for treatments of raw
sewage 96
34 Studentized test for treatments of mixed
liquor 99
xii
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Number
35
36
37
38
39
40
41
42
43.
44
45
46
47
48
49
50
Comparison of results from student ized range
test
Overall averages and ranges for different
parameters in different test liquids ....
Average performance of treatment system in
metals removal
Regression analysis data for Figure 42 - raw
sewage
Regression analysis data for Figure 43 -
primary effluent
Regression. analysis dat-a for Figure 44 -
mixed liquor ... ... . . . . .
Regression analysis data for Figure 45 -
secondary effluent
Results of regression analysis for .effect of
SOC on metals distribution .
Mean relative errors of prediction of Models
I, 2 and 3 against measured data, %
Mean relative standard deviations of predic-
tions of Models 1, 2 .and 3 against measured
data, % .........
Regression constants for metals distribution
Model 3
Squared correlation coefficients for metals
distribution Model 3
Regression constants .for metals distribution
Model 4
Squared correlation coefficients for metals
distribution Model 4
Regression constants for metals distribution
Model 4' .
Mean prediction error and relative standard
deviation of Model PW at W = 1.0, based on
Model 3
102
104
108
129
129
130
130
165
167
168
170
171
173
174
175
179
Kill
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Number Page
51 Mean prediction error and relative standard
deviation of Model PW at W = 1.0, based on
Model 4' 180
52 Application of Model PW to averaged primary
clarifier performance for 39 runs 182
53 Mean prediction error and relative standard
deviation of Model FS at W = 1.0, based on
Model 3 184
54 Mean prediction error and relative standard
deviation of Model FS at W = 1.0, based on
. Model 4' 185
55 Full system predicted and measured metals
distribution, based upon distribution
Model 3 186
56 Full system predicted and measured metals
distribution, based upon distribution
Model 4' 187
xiv
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SECTION 1
INTRODUCTION
In recent years, increased emphasis has been placed on
studies of the chemistry, biological effects, treatment, fate,
and control of heavy metals in the environment. Findings
include the discovery of heavy metals at high concentrations in
surface waters receiving municipal and industrial waste dis-
charges containing such metals*; coupled' with the recognition of
potential health..hazards and adverse environmental impacts
associated with major disposal methods-for met-al-laden-municipal
and combined sludges. While the management of metals originat-
ing directly from industrial discharges has been implemented
under effluent limitations guidelines and National Pollutant
Discharge Elimination System (NPDES) permits, the control of
industrial plus non-industrial metals entering combined munic-
ipal-industrial public-owned treatment works (POTWs) has been
found to be much more difficult. As a result, heavy metals
discharge into the municipal sewage treatment systems, and
their fate during the sewage treatment processes, have become
subjects of considerable interest in recent years.
Most of the studies to date concerning heavy metals in
sewage treatment processes have represented attempts to perform
mass balances of metals around a POTW, and determination of the
per cent removal of each metal of concern across that POTW.
However, there is a relative lack of information on the actual
mechanisms affecting the distribution of heavy metals between
liquid and solid phases through a municipal sewage treatment
plant. There is a need for an understanding as to how the
distribution of heavy metals is affected by variables such as
the individual metal in total metals concentration, volatile
suspended solids (VSS), soluble organic carbon (SOC), and
inorganic ligands, such as carbonate, chloride, sulfate, and
ammonia. Such an understanding is essential for developing
criteria that can be used to predict the distribution of heavy
metals through combined sewage treatment systems. Development
of such criteria will be useful in different ways, including:
1) Given the influent and operational characteristics of a
sewage treatment plant, the metals concentration in the sludge
and the final effluent can be predicted. 2) Pretreatment
standards necessary for heavy metals in the influent to the
treatment plant can be predicted such that the metals will not
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accumulate in the sludge to such levels that agricultural use
will be restricted.
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SECTION 2
CONCLUSIONS
METAL SOLUBILITY IN PROCESS LIQUIDS
The following conclusions have been drawn from..the studies-
en metals solubility in filtered raw sewage and aeration basin
mixed liquor.
1. At all pH values tested, equilibrium solubility
conditions were achieved within six to 12 hours; Levels of
metal solubility were equivalent at 24 hours to those observed
at 12 hours.
2. High correlations were observed between metal solu-
bility and process liquid pH, for all metals investigated.
3. Within the process liquids, over the 24-hour period
of the solubility tests, the initial pH in each case shifted
from the more extreme high or low pH values toward a final pH
value of about 8.
This pH shift suggests that the process liquids are
well buffered, and the occurrence of more extreme pH conditions
in full-scale treatment systems would indicate the presence of
strong acid or basic industrial wastes, which would influence
metals solubility.
4. The effect of sulfide, at concentrations of 1 and 10
mg/1 on metal solubility were tested. A comparison of the
results where sulfide was added to those with no sulfide
present revealed no difference in observed metals solubility.
Sulfide effects may be significant at levels in excess of those
tested, however.
5. A comparison of metals solubility in filtered process
liquids with that in tap water revealed that in most instances
the process liquids yielded higher metals solubility than did
the tap water. This response is probably due to the complexa-
tion effects of organic and inorganic ligands in the process
liquids.
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6. The pH range of minimum metals solubility, for all
metals tested and in both process liquids, was in the pH range
of 8 to 9, except for aluminum in mixed liquor where a pH of
minimum solubility of 6.8 was observed.
SORPTION OF METALS
The distribution of metals between the soluble and solids
(sludge) phases in raw sewage and mixed liquor was studied,
with metals added to the test liquids at concentrations below
the metals solubility limits. The following results were
observed.
1. A major portion of each added metal was removed from .
the soluble phase onto the solid phase in each test liquid.
The sorption was essentially completed within a 15-minute
contact time although some minor additional sorption continued
for up to six hours.
2. Since the metals were added to the process liquids at
concentrations below their solubility limits, removal from the
liquid phase could not be by precipitation of metal salts, and
therefore was due to accumulation by sorption onto the raw
sewage and activated sludge solids.
3. The sorption behavior of each metal could be described
by an adsorption isotherm relating yg of sludge metal sorbed
per mg of total volatile suspended solids (TVSS) versus
metal added in (mg/l> to the process liquids.
4. Although the sorption data generally followed the
isotherm described in Item 3 above, the data for most metals
did not fit a standard Freundlich isotherm based upon residual
metal in solution.
5. Sorption of added metal in raw sewage ranged from 0
to -99%, with the following ranking of. metals from least to most
sorbed: iron, nickel, cadmium, copper, zinc, lead, chromium.
6. Sdrptio'n of added metal in" activated sludge mixed
liquor ranged from 8 to 98%, with the following ranking of
metals from least to most sorbed: iron, nickel, zinc, cadmium,
-:eh;rbihium, copper, and lead.
EFFECTS OF SEWAGE PARAMETERS ON METALS DISTRIBUTION ..
It has been suggested in the literature that a number of
different waste constituents might influence the distribution
of metals in raw sewage and mixed liquor between the soluble
and solid phases.
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This phase of the project investigated several domestic
and industrial waste constituents, at low, normal, and high
concentration, in replicate samples. The constituents evalu-
ated were inorganics plus hardness, detergents, suspended
solids concentration, SOC, pH, cyanide, and ammonia. The
following conclusions were drawn, based upon statistical
analysis of the experimental data.
1. Few of the waste constituents, at the levels tested
had a statistically significant effect on metals distribution
between the soluble and solid phases.
2. At the 99% confidence level, SOC influenced aluminum
distribution in raw sewage; pH influenced iron and nickel
distribution in raw sewage; and ammonia influenced aluminum in
mixed .liquor-.
3. At the"'95% confidence level,. inorganics and .hardness
influenced the distribution of aluminum and lead in raw sewage,
and cadmium and lead in mixed liquor.
4. At the 95% confidence level, detergent strength
influenced the distribution of chromium and nickel in raw
sewage. In mixed liquor, chromium, iron, lead, and nickel were
indicated to be influenced.
5. At the 95% confidence level, pH influenced the
distribution of aluminum in raw sewage and mixed liquor.
Ammonia was indicated to influence the distribution of cadmium
in raw sewage.
METALS DISTRIBUTION IN CONVENTIONAL ACTIVATED SLUDGE SYSTEMS
During this phase of the project, eight parallel continuous-
flow pilot activated sludge systems were monitored around
each unit process, .during a total of 39 runs. Raw domestic
sewage, spiked during each run with random levels of a mixture
of test metals, was treated. Composite process liquid samples
were collected several times weekly during each run, for raw
sewage, primary clarlfier effluent, mixed liquor, secondary
clarifier effluent, and settled primary and secondary sludge.
Total and filtered fractions of each metal were analyzed for
metals plus other constitutents including SOC and VSS.
Based upon the evaluation of this data, models were developed
to predict the distribution of metals in each process liquid,
and to predict the removal efficiency of each unit process
and the full-treatment system in metals removal. The
conclusions developed from this phase of the project are
comprehensive, and are only briefly summarized here.
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1. The removal of metals across the treatment system was
directly related to -the degree of distribution of each metal in
the raw sewage and mixed liquor, and the efficiency of removal
of the suspended solids (and associated metals) in the primary
and secondary clarifiers. Thus, there are two principal clas-
sifications of variables which influence metals removal in
combined treatment systems: those associated with the metals
distribution in each process liquid; and those associated with
the performance of the clarifiers solids separation.
2. In some experimental runs, negative removals of the
metals were observed across the primary clarifiers, and/or the
full-treatment systems. These negative metals removals always
resulted from negative removals of suspended solids in the
primary clarifier. Intermittent negative removals of suspended
solids in primary clarifiers were observed in full-scale systems
as well as pilot units. This negative performance of the
primary clarifier in suspended solids removal explains why many
short-term mass balance studies on full-scale systems have
resulted in negative full-system removals of metals.
3. Over the course of the 39 experimental runs, a wide
range of concentrations of influent SOC, VSS and metals were
observed, reflecting the combination of natural fluctuations
in the raw sewage composition, plus the spiking of the raw
sewage with metals. Average performance of the system in
solids removal was 76%, and removal of SOC averaged 61%.
4. Ranges of total effluent metals were also broad,
although less so than the influent metals ranges. However, an
evaluation of the soluble metals levels revealed that the
average soluble concentration, for each metal, remained essen-
tially constant across each unit process and the entire treat-
ment system. Thus, the reduction of total metals across the
unit processes was due to the sedimentation of solid-bound metal,
5. The lack of change in soluble metal concentration
between raw.sewage and primary clarifier effluent revealed that
there was no redistribution of-metals .in"that unit process.
6. The total metal concentrations in the activated
sludge aeration basin were much higher than those observed in
any other process liquid. However, the soluble metals levels
in all process liquids were equivalent, and the higher total
metals levels in the mixed liquor resulted due to higher levels
of suspended solids and their associated metals.
7. Relatively wide variation in the total metals dis-
charged in the secondary effluent resulted from variation in
effluent suspended solids; the effluent soluble level of each
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metal was comparable to the raw sewage soluble level of that
metal.
8. The relative contribution of the soluble fraction of
the effluent metals ranged from a low 2.9% for chromium up to
34.1% for nickel. Increased secondary clarifier efficiency in
suspended solids removal would reduce only the non-soluble
portion of the effluent metals.
9. The averaged removal of metals in the primary clari-
fier ranged from 14.0% for zinc to 41.1% for iron, and the
metals ranked from lowest to highest removal in the primary
clarifier were: zinc, copper, cadmium, aluminum, chromium,
lead, iron, nickel.
10. The averaged removal of metals in the activated
sludge process plus secondary clarifier ranged from 1.3 for
aluminum to: 38.9%,.fo-r ca-dmium, and the metals ranked from
"lowest to .highest as follows: aluminum, chromium, nickel,
iron, .zinc., copper, lead, cadmium.
11. The averaged overall removals of metal across the
entire 'treatment, system-'ranged from 27.6 for aluminum to
54.9% for lead, with the metals ranked from lowest to
highest removal as follows: aluminum, zinc, chromium,
copper, iron, nickel, cadmium, lead.
12. For the metals aluminum, chromium, iron, and nickel,
the bulk of overall removal occurred in the primary clarifier.
For the metals cadmium and copper, the secondary processes
accounted for the majority of overall removal. Removals of
lead and zinc were about equally distributed between the
primary and secondary stages.
13. A number of models were assessed for their accuracy
in predicting the distribution of metals in each process
liquid, between the soluble and solid phases. An investigation
of the influence'for the total metal concentration of the
parameters VSS, SOC,, and pH revealed that a model which
related total metal to sludge-bound metal per unit weight of
VSS and to VSS solids in the process liquid provided an .
accurate prediction' tool for metals distribution. This
model has been designated as Metals Distribution Model 3 in
.this report, and model coefficients for each metal in-each
process liquid were derived. At moderate to high suspended
solids levels, a simplified model (termed Model 4) which
directly relates total metal to sludge-bound metal is equally
accurate, and Models 3 and 4 have been utilized as the bases
for a model of the full-treatment system.
-------
14. Although the experimental data of the .'39 runs can be
fitted to adsorption isotherms in the manner described above in
Part II, Item 3, a more striking and significant' relationship
was identified on the basis of the data generated from the
continuous-run pilot units. This 'relationship reveals that the
concentration of each metal sorbed on the solids of each
process liquid was directly related to total metal, and was
inversely related to total VSS present. In other words, at
constant suspended solids, the metal per unit of solids
increased with increasing total metal. However, at constant
total metal, the metal per unit of solids increased with
decreasing suspended solids concentration. Model 3, which
incorporated all three variables, yielded high correlation
coefficients with the experimental data on each process
liquid and each metal, ranging from a squared coefficient of
0.80 for nickel to 0.99 for chromium in raw sewage, and
coefficients of 0.99 for all metals in mixed liquor.
MODEL DEVELOPMENT
Section 8 of this report presents the development and
application of the metals distribution and full-system metals
removal models. The results of this activity are summarized
below.
1. On the basis of the 'experimental data generated in
the 39 continuous runs of the pilot treatment systems, an
accurate metals distribution model, identified as Model 3, was
developed. With this model, and known total metal and VSS
concentrations, the distribution of soluble and solids bound
metal in raw sewage and each other process liquid can be
predicted.
2. A simplified version of Model 3, identified as
Model 4, was developed for application where suspended solids
concentrations are moderate to high. Model 4 can accurately
predict solids bound metal, with only the total"metal concen-
tration given.
3. Models 3 and 4 have been used, together with suspended
soTids mass balance relationships, to develop a model, PW,
for the performance of the primary clarifier. In addition
to the constants of Model 3 or 4, the efficiency of the
clarifier in suspended solids removal must be specified or
estimated. The relative standard deviation of predicted
against measured performance for Model PW (incorporating
Model 4) was less than 10% for aluminum, chromium, copper,
and zinc, and is near 15% for cadmium and iron. The relative
standard deviation of predicted performance for lead and
nickel was near 20%. The relative standard deviations,
where Model PW incorporated Model 3, were somewhat higher.
8
-------
Model 4 was thus indicated to be the preferred metals distri-
bution base model for Model PW.
4. A predictive model, identified as Model FS, and
incorporating Model PW, was developed to describe the full-
treatment system including primary and secondary stages. This
model also requires a solids mass balance, and this includes
factors for activated sludge yield per unit of SOC removed, and
secondary clarifier performance. Model FS has been used to
predict the percentages of influent metals which will occur in
the primary sludge, the secondary sludge, and the system
effluent. Model FS, based upon Model 4, has the capability to
predict effluent metal (and by difference sludge metal) within
about 10% or less for all metals except nickel. For nickel,
the difference between predicted and measured effluent metal was
slightly below 20%. ..
.. 5. Any full-system'model, such as Model FS, must incor-
porate several submodels. These include metals distribution
models^ suspended solids removal models for the primary and
secondary clarifier, and an excess sludge yield model for the
activated sludge process. The metals distribution models
resulting from this study were quite accurate. Prediction
errors for the full-system model resulted primarily from the
inability of existing clarifiers and activated sludge models to
accurately predict solids balances around those unit processes,
over short periods of performance. Thus, Model FS incor-
porates solids mass balance models with acknowledged in-
adequacies for short term performance. Until improved
solids models are available, Model FS should only be applied
to predict long-term (in excess of 60 days) performance on
metals removal in combined treatment systems.
-------
SECTION 3
RECOMMENDATIONS
This study has revealed that the.distribution of metals
in the individual process liquids of a combined treatment
system follows patterns which can be accurately described by
empirical relationships. Two such empirical relationships
have been developed as one result"of this project. The
relationships, identified as Metals Distribution Models 3
and 4, reveal that the distribution of the metals between
the soluble and solid phases of the process liquids are
controlled, for each specific metal, by the total metal
concentration and the VSS concentration. Model constants
have been derived by statistically fitting these models to
data collected during 39 runs on parallel continuous-flow
activated sludge pilot systems. It is recommended that
these two models, and the derived constants, be validated
against full-scale treatment systems performance. Some
preliminary validation has already been performed against
one full-scale system and the results were promising.
In this study, the behavior of eight metals were investi-
gated. Each metal demonstrated somewhat different behavior,
and the study has revealed that different process liquid
characteristics can influence the behavior of each metal to
a variable extent. There is little basic information on the
chemical and physical interactions of metals in process
liquids such as investigated here which could provide for .
interpretation of'these results as any basis other than an
empirical one. In order to better understand the response
patterns obseryed in this study and others'of similar- '
objective, fundamental research on the physical and chemical
interactions of metals in raw sewage and activated sludge
mixed liquor are necessary.
Finally, this project has resulted in the development of
a full-system model to predict the removal of metals at each
unit process across a combined treatment system. The full-
system model relies upon submodels for (1) metals distribution,
by process liquid, (2) primary clarifier performance in sus-
pended solids removal, and (3) secondary treatment system
performance in terms of sludge yield, and secondary clarifier
performance.
10
-------
A comparison of the full-system model to pilot-plant
experimental data revealed that, where the full-system model
was inaccurate, it failed through an inability to track the
short-term solids balance around each unit process. These unit
processes, while performing in a predictable fashion on a long-
term average basis, perform in a more erratic fashion over
short periods of days to weeks, sometimes exhibiting, for
example, negative suspended solids removal in the primary
clarifier or short-term interruptions in activated sludge
yeild. Metals removals are closely tied to the solids balances
around the unit processes of the treatment system, and improved
models to predict the short-term behavior of the systems in ...
terms of solids are necessary before more accurate short-term
modeling of metals dynamics will be possible.
11
-------
SECTION 4
HISTORICAL PERSPECTIVE
The large volumes of municipal and industrial wastewaters
and treatment residues, coupled with increasing energy costs,
reduced land availability, and enhanced public awareness of the
potential environmental and health hazards associated with the
toxic substances present in the effluents, have created a great
deal of concern in recent years. Heavy metals pollution of
surface waters, and the environmental hazards associated with
their presence in sludges disposed on land, have received much
attention beginning in the early 1970's. This concern is
principally due to two factors: 1) There is increasing indus-
trialization and growing awareness of the toxic effects of
metals such as cadmium and lead. 2) Analytical techniques
capable of measuring low metals concentrations found in the
water bodies of the nation and the discharged effluents have
increasingly become widely available.
Heavy metals loadings into surface waters arise from point
sources as well as diffuse sources, and assessing the relative
impact of the two sources is often difficult. Treated or
untreated effluents from municipal and industrial activities
are among the point sources, while atmospheric fall-out and
surface runoff comprise the major portions of diffuse sources
of heavy metals into the water bodies of the nation (Patterson
and Kodukula, 1978).
HEAVY METALS IN POTW INFLUENTS AND EFFLUENTS
.The metals found in municipal sewage originate from a
variety of industrial, commercial, and residential activities,
as well as from storm runoff. Several authors (Davis and
Jacknow, 1975; Gurnham, et al., 1979; Kodukula and Obayashi,
1979; Olthof and Lancy, 1978) have published discussions on the
sources of heavy metals in municipal sewage. The relative
contribution of heavy metals from residential and industrial
sources primarily depends upon the number and nature of the
contributing industries, and the pretreatment regulations in
the area under consideration. High influent metal concentra-
tions, either due to domestic or industrial activities, inter-
fere with the operation of treatment plants due to their toxic
effects during the biological treatment.
12
-------
Generally, in the United States, metals concentrations in
the influents to POTWs are lower than the threshold toxic
levels for biological treatment processes (U.S. Environmental
Protection Agency, 1978). The concentrations may however be at
environmentally unacceptable levels in the final effluent or
the sludge, depending upon the metals removal efficiency
within the treatment system. For example, Putnam and Paulus
(1976) reported 2.3 tons/day of total heavy metals input from
the sewage of the Twin Cities, entering the Minnesota Metro-
politan Sewage Treatment Plant. Approximately 54% was removed
by treatment processes prior to effluent discharge to the
Mississippi River, while the remaining cadmium (55%),
chromium (55%), copper (38%), manganese..(72%), nickel (68%),
lead (60%), and zinc (42%) were discharged with the plant
effluent. The Metropolitan Sanitary District of Greater
Chicago (MSDGC) was estimated to discharge 1,469 tons/year
of combined copper,- cadmium,, lead, mercury, nickel, zinc,
and chromium from.its- treatment -plants-(Patterson and Allen,
1975). Data presented by Patterson and Kodukula (1978) for
the same metals indicate that about 6,000 tons of total
metals from POTWs in the United States alone are discharged
every year into the Great Lakes.
An extensive field survey was conducted by Sverdrup and
Parcel, and Associates, on 103 POTWs across the United States.
Table 1 shows the ranges and medians of influent and effluent
metal concentrations reported in this study. Table 2 presents
a partial summary of other published data on influent and
effluent metal levels of several, conventional sewage treatment
plants. It is evident from these tables that there is extreme
variation in removal efficiencies for each metal, and that
while the metal removal efficiences are generally in the order
of zinc>mercury>lead>copper>chromium>cadmium>nickel, there
is variation in this order among plants.
HEAVY METALS.IN SLUDGE
Heavy metals in sewage sludges emanating from biological
treatment processes have received considerable attention in
recent years, due to their'-potential as toxic agents in sludge
treatment (e.g., anaerobic digestion) and disposal (e.g., land
application, incineration) operations. The properties of
sewage sludges and the agronomic and environmental considera-
tions involved in the development of guidelines for land
application of such sludges have been discussed in several
reviews (Chaney, 1973; Dowdy et al., 1976; Jones and Lee, 1977;
McCalla et al., 1977; Schmidtke and Cohen, 1977; Sommers and
Button, 1977). Kodukula and Obayashi (1979), in their review
paper, concluded that the heavy metals concentrations in sewage
sludges are highly variable (Table 3). Similar variability has
13
-------
TABLE 1. INFLUENT AND EFFLUENT METALS CONCENTRATIONS IN DIFFERENT
POTWs (AFTER U.S. EPA, 1977)
Metal
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Inf luentj
Median
11
100
120
2000
60
1
90 '
330
, yg/ia
Range
1-243
5-14,000
10-1,968
450-10,200
7-1,000
0.2-240
10-3,190
17-3,909
Effluent,
Median
9
18
33
250
25
0.6
55
110
J^g/l15
Range
2-79
3-246
10-352
48-569
7-80
0.-2-2.9
12-1240
13,1039
Percent Removal^
Median
18
82
73
88
58
40
, 39 .
67
a) Based on data available from 103 POTWs.
b) Based on data available from 22 POTWs meeting secondary treatment
performance levels.
-------
TABLE 2. SUMMARY OF INFLUENT AND EFFLUENT CONCENTRATIONS OF METALS IN SELECTED TREATMENT
PLANTS*
Ui
Location
Bryan , OH
Dallas, TX
Grand Island,
MI
Grand Rapids,
MI
Hyperion, CA
Joplin, MO
Muncie, IN
New York, NY
Rockford, IL
Burlington,
CANADA
Cadmium
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
-
0.
0.
0.
0.
-
0.
0.
0.
0.
-
0.
0.
0.
0.
:°o.
013
008
018
016
028
028
021
015
016
01
25
05
01
01
Chromium
0.
0.
0.
0.
0.
0.
3.
2.
0.
0.
8
2
22
09
059
013
6
5
3
21
0.066
0.041
0.
0.
0.
0.
-
0.
0.
26
05
16
08
04
03
Copper
0.2
0.1
0.09
0.06
0.17
0.067
1.4
1.6
0.13
0.13
0.316
0.047
0.26
0.07
0.27
0.15
1.17
0.19
0.10
0.02
Lead Mercury
_ _
0.09 0.5
0.04 0.2
0.16 0.6
0.092 0.5
-
0.11 0.5
0.10 0.5
0.19 0.5
0.065 0.8
0.93
0.22
_
-
<0.05 <1
<0.05 <1
Nickel
0.05
0.05
0.07
0.06
-
2.0
1.8
0.2
0.14
-
0.13
0.11
0.11
0.10
0.37
0.32
0.04
0.03
Zinc
2.2
0.2
0.32
0.11
0.353
0.182
1.5
0.8
0.43
0.26
0.984
0.484
0.97
0.26
0.41
0.21
2.8
0.45
0.11
0.04
Reference
Earth et al . ,
1965
Esmond and
Petrasek, 1974
Brown et
1973
Barth et
1965
al. ,
al. ,
Chen et al . ,
1964
Brown et
1973
Davis and
Jacknow,
Klein et
1974
Patterson
1978
al. ,
1975
al.,
'
Oliver and
Cosgrove, 1975
*Metal concentrations expressed as mg/1 except mercury, which is expressed as yg/1.
(continued)
-------
TABLE 2. (continued)
Location
Clarkson,
CANADA
Oaksville,
CANADA
Oxford,
ENGLAND
Zurich,
SWITZERLAND
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Average Concentration:
Influent
Effluent
Average Percent Removals
Cadmium
0.02
0.01
0.006
0.001
0.006
0.000
0.006
0.003
0.031
0.012
61.3
Chromium
0.14
0.06
0.29
0.06
-
0.08
0.03
0.459
0.239
47.9
Copper
0.26
0.10
0.31
0.08
0.082
0.006
0.09
0.06
0.290
0.164
43.4
Lead
0.37
0.08
0.23
0.15
0.20
0.00
0.27
0.05
0.229
0.081
64.6
Mercury
6
1
7
1
-
-
2.02
0.673
66.7
Nickel
0.08
0.07
0.33
0.27
-
0.07
0.05
0.267
0.226
15.4
Zinc
0.34
0.09
2.4
0.56
-
0.6
0.25
0.919
0.263
71.4
Reference
Oliver and
Cosgrove, 1975
Oliver and
Cosgrove, 1975
Perry et al . ,
1976
Roberts et al . ,
1977
*Metal concentrations expressed as mg/1 except mercury, which is expressed as yg/1.
-------
TABLE 3. SUMMARY OF DATA COLLECTED ON SELECTED METALS IN SEWAGE SLUDGES FROM
VARIOUS MUNICIPAL WASTEWATER TREATMENT PLANTS (KODUKULA AND
OBAYASHI, 1979)*
Location
United States:
Indiana
Michigan
Michigan
Minnesota
New. Hampshire
New Jersey
Ohio
Pennsylvania
Wisconsin
Canada . . '
England and Wales
Sweden
Switzerland
Cadmium
163
74
163
131
10
29
198
105
64
<200
13
30
Chromium
3195
2031
8086
931
6763
1606
1281
635
75
980
872
500
Copper Mercury
2846
1024 5.5
2423 2.6
1521
86
1400
1392 4370
1091
1147
19 28
970
791 6.0
800
Nickel
993
371
1040
231
63
156
710
172
482
6
510
121
300
Lead
2970
2940
1190
3347
327
1634
784
812
63
820
281
800
Zinc
8107
3315
4900
2368
121
2206
4153
3517
2982
181
4100
2055
3000
*A11 concentrations expressed as mg/kg.
-------
also been demonstrated among the heavy metals concentrations, in
sludges from major cities in the United States (Furr et al.,
1976).
HEAVY METALS IN SEWAGE.TREATMENT PROCESSES
Conceptually, a typical municipal treatment plant can be
divided into five major unit processes: primary sedimentation,
secondary treatment (activated sludge for the purpose of this
study), secondary clarification, anaerobic digestion and disin-
fection (Fig. 1). Similarly, the phases in which the heavy
metals exist in the wastewater can be classified into three
components: soluble, operationally defined as that portion
passing through a 0.45-micron filter; settleable solids,
characterized by being settleable within 30 minutes (Anon.,
1973); and non-settleable solids. Heavy metals in each process
stream exist in each of the above phases, as represented in
Figure 1.
As shown in Figure 1, settleable solids and associated
metals are removed via the primary and secondary clarifiers to
the anaerobic digester. The effluent from the primary clari-
fier, containing soluble metals and metals associated with
non-settleable solids, enters the aeration unit for secondary
treatment. The settled secondary effluent undergoes chemical
disinfection and is finally discharged. .The supernatant from
the sludge digester is usually returned to the raw waste
or primary clarified stream. This flow may or may not consti-
tute a significant mass source of heavy metals to the process
stream. Further, the complex organic and non-metal inorganic
constituents of the digester supernatant, when blended into the
primary waste, may have a significant effect on metal distribu-
tion in subsequent treatment processes.
There is little information available on the heavy metals
interactions in the disinfection process. It would, however,
be expected that chlorination, a major disinfection process in
the United States, could indirectly affect the heavy metals
distribution in the secondary effluent by changing the pH of
the medium and/or oxidizing some of the soluble and particulate
organic ligands with which the metals are complexed. Prior to
the point of disinfection however, the operational segregation
of influent metals between POTW effluent phase and sludge phase
is completed.
Sedimentation
In a typical sewage treatment plant, metals associated
with settleable solids are removed during primary and secondary
sedimentation. Metal removal efficiency in a primary clarifier
18
-------
LIQUID AND SOLID PHASE PATHWAYS
ACTIVATED SLUDGE
PROCESS
PRIMARY
CLARIFIER
ANAEROBIC
DIGESTOR
SECONDARY
CLARIFIER
RETURNED ACTIVATED SLUDGE
EXCESS SLUDGE
SOLUBLE
SOLID, NON-SETTLEABLE
DIGESTED
SLUDGE
SOLID, SETTLEABLE
Figure 1. Schematic of a typical municipal sewage treatment plant illustrating
the liquid and solid phase pathways.
-------
has two important implications. When the metals are largely
removed during primary sedimentation, problems might arise with
regard to toxicity of metals if the primary sludge is disposed
on land. Low metals removals in primary sedimentation due, for
example, to the presence of complexing agents in the waste,
which would render the metals soluble, or to ineffective solids
separation lead to increased levels of metals input to the
activated sludge system. The resulting high metals loading to
the aeration basin may cause a decrease in the process perform-
ance efficiency of the activated sludge due to metals toxicity,
and thereby result in a poor quality effluent in terms of
organics and metals.
Table 4 presents data collected on removal of metals
through ten primary treatment plants. According to this
survey, cadmium and lead were the least removed metals, while
iron, zinc, and copper exhibited the highest removals during
primary treatment. Brown et al., (1973) reported average per
cent removals for copper, cadmium, chromium, lead, mercury, and
zinc at 42, 15, 27, 37, 32, and 46, respectively, during primary
treatment. Except for lead, these results are comparable to
those presented for primary plants in Table 4, and are similar
to secondary plant data summarized in Table 2.
Activated Sludge Process
Removal of heavy metals by activated sludge has been a
subject of interest since the 1950's (Rudolfs and Zuber,
1953), but has received considerable attention only during the
last two decades (Adams et al., 1973; Cheng et al., 1975).
Most early studies dealt with the percentage removal of metals
by activated sludge, while information regarding the physical-
chemical interactions between metal ions and the biomass has
been reported only in recent years.
Rudolfs and Zuber (1953) studied the removal of copper and
zinc by activated sludge, using laboratory-scale units. They
reported removals of 33-100% and 31-90% for zinc and copper,
respectively, for a contact period of 30 minutes, and ..concluded
that the amount of metal removal was a function of two factors:
the concentration of the activated sludge and the time of
contact between the metal and the sludge. Stones (1955; 1956;
1958; 1959a; 1959b; 1959c) investigated the fate of iron,
copper, nickel, and zinc in each treatment unit of a sewage
treatment plant, and reported that activated sludge treatment
.removed about 80% of iron and copper, and 90, 60, and 30%
of lead, zinc, and nickel, respectively, present in presettled
sewage.
20
-------
TABLE 4. REMOVALS OF SELECTED METALS DURING PRIMARY
TREATMENT (U.S. EPA, 1977)
Metal
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
% Removal
.... - Range
0-15
0-71
14-60
19-66
0-25
0-75
8-21
8-67
Efficiency
Median
5
31
37
42
11
18
14
39
21
-------
Earth et al. (1965), in their, extensive pilot-plant
investigations of heavy metal.interactions'in sewage treatment
plants, demonstrated that activated sludge plays a major role
in overall POTW metals removal during the sewage treatment
process. This study found that removals of copper and zinc by
activated sludge are very high compared to those of chromium,
and especially nickel. Similar metals removals have been
reported by Tarvin (1956) and Brown et al.. (1973) in the
United States, by Oliver and Cosgrove (1974; 1975) in Canada,
and by investigators from England (Stones, 1955; 1956; 1958;
1959a; 1959b; 1959c), Germany (Anon., 1966), and Switzerland
(Roberts et al., 1977). The information available in the
literature on heavy metals removals indicates that copper and
zinc show high removals by activated sludge, while nickel
exhibits the least removal.
Extensive studies conducted at the Environmental Engi-
neering laboratories of the Illinois Institute of Technology,
Chicago, (Cheng, 1973) on heavy metal interactions in activated
sludge have demonstrated that the sludge solids have a great
ability to remove and accumulate metals from solution in a very
rapid initial phase, followed by a slow phase. The removal
achieved in the slow phase is relatively insignificant compared
to that of the first phase. In these studies, the biofloc of
the activated sludge process appeared to act as a chemisorption
system, following a Langmuir adsorption isotherm. The effi-
ciency of metal uptake by the sludge was found to follow the
order of lead>copper>cadmium>nickel, based on the percentage
removal of initial metal added. The total amount of metal
taken up by the sludge floe was found to increase with the
concentration of VSS. The removal of metal also increased
with increasing metal concentration, for a constant VSS
concentration. The amount of metal uptake increased with
increasing pH up to a level where precipitation of metal
hydroxide occurred. Cheng (1973) also studied the effect of
added soluble ligands such as oxalate, and silicate on the
metal uptake by activated sludge, and reported'that high,..
ligand concentration prevented metal sorption or precipi-
tation, by formation of soluble metal-ligand complexes.
Such reactions resulted in higher soluble metals concentra-
tions in the final effluent.
In biological processes, the relative affinity of metal
ion for the sludge depends upon the different metal ions
present in the system. The Irving-Williams series (Irving and
Williams, 1948; 1953) suggests that the stability complexes of
bivalent metal ions, regardless of the nature of complexed
ligand or of ligand molecules involved, follows the general
sequence of zinc>copper>nickel>cobalt>cadmium>iron>
manganese. However, Cheng et al., (1973) demonstrated that
22
-------
under similar conditions of pH, VSS, and metal concentration,
etc., the uptake of these metals by activated sludge is in the
order of lead>copper>cadmium>nickel. Schnitzer and Skinner
(1966; 1967), in their studies with metal-fulvic acid com-
plexes, also reported the sequence of the stabilities of
complexes as'being different than that of the Irving-Williams
series. It thus appears that guidelines derived from simple
system behavior are not directly applicable to the complex
systems of the POTW.
Anaerobic Digestion
Among the various process components of conventional
wastewater treatment, including the various sludge treatment
processes, anaerobic digestion appears to be particularly
vulnerable to excessively high heavy metal loadings. Numerous
investigators have attempted to study the heavy metal problems
with respect to anaerobic digestion systems in recent years.
However, most research performed thus far has focused, on the
toxic effects of heavy metals on anaerobic digestion systems
(Moore et al., 1961; McDermott et al.. 1963; English et al..
1964; Earth et al., 1967; Ghosh and Zugger, 1973), while only
few studies have centered on the distribution and chemistry of
metals within the digestor (Gould and Genetelli, 1975; Hayes
and Theis, 1978; Lingle and Hermann, 1975; Patterson and Hao,
1979).
Adams et al. , (1973) reviewed the effects and removal
of heavy metals in biological systems including anaerobic
digestion. Extensive studies conducted by Earth et al.,
(1967) over a period.of ten years of continuous feeding of
heavy metals demonstrated that a significant amount of heavy
metals were removed from the bulk solution in anaerobic diges-
tion. No effort was made in that study to investigate the
chemistry and removal mechanisms of the metals. Gould and
Genetelli (1975) examined the distribution of heavy metals in
anaerobically digested sludge, and reported that more than 90%
of the metals was found on the particulate fraction (>100
micron effective diameter).
More recently, Hayes and Theis (1978) investigated the.
distribution of heavy metals among the soluble, precipitated,
and extracellular components of anaerobically digesting sludge.
They concluded that the heavy metal chemistry is controlled not
only by the stability of inorganic precipitates, but also by
sorption onto and subsequent incorporation of metals into the
digester biomass. Toxic effects were found to coincide with
the near maximum uptake of metals by the biomass. M.icrobial
uptake activity competed with precipitation in the removal of
heavy metals from the digester supernatant. Depending upon the
23
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metal, between 30 and 60% of the total metal was associated
with the biomass.
Investigations by Patterson and Hao (1979) showed that in
addition to the uptake by biomass and precipitation reactions,
another important mechanism effecting heavy metal removal in
anaerobic digesters is complexation of metals with the solids
as well as the digester supernatant. By determining the
stability constants of metal-sludge complexes, it was shown
that the affinity of heavy metals toward anaerobically digest-
ing sludge follows the order of lead>copper>iron>cadmium
>nickel>zinc. A similar order of affinity was also observed
for metal-digester supernatant complexes. It was reported
in this investigation that in excess of 98% of each total
metal in the digester was associated with the sludge phase.
This corresponds to similar values reported by Gould and
Genetelli (1975) and Hayes and Theis (1978).
HEAVY METAL DISTRIBUTION
Chen et al., (1974) measured the distribution of several
metals in raw sewage at Los Angeles, California.. For the four
metals, copper, iron, lead, and zinc, the metal associated with
the settleable solids fraction was 7, 46, 22, and 57%, respec-
tively. For the same metals, the soluble fraction of the raw
sewage contained 91, 42, 63, and 30% of the respective total
influent metal. The remainder of the metals ranging from 2
(copper) to 13% (zinc) was associated with non-settleable
suspended solids. Patterson (1978) reported that for a treat-
ment plant in Illinois the soluble fractions of cadmium,
copper, iron, nickel, and zinc in raw sewage were 24, 26, 4,
68, and 16, respectively.
In order to study the phase partitioning behavior of
metals in raw sewage, Patterson et al., (1975) conducted batch
experiments in which increments of stock metal solution was
added dropwise to raw sewage, below a predetermined metal
solubility limit. The pH of the sewage was maintained con-
stant, and the reaction vessels were stirred for 24 hours,
before the final soluble metal concentrations were measured.
In this study, lead and zinc were most completely adsorbed to
the raw sewage solids, while most of the added nickel stayed in
solution. This distribution behavior partly explains the high
removals of zinc and low removals of nickel observed in primary
sedimentation. It was shown in these studies that for most
metals, partitioning into the soluble phase followed a log-log
function. However, the proportion of soluble copper appeared
to be quite insensitive to total copper (1 - 40 mg/1) added,
indicating that the soluble copper concentration in the primary
effluent may remain relatively constant despite fluctuations in
24
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the influent copper concentration over a limited range. The
addition of lead resulted in considerable partitioning onto the
solid phase, even at the highest lead concentration examined
(only 2.7% soluble at a total lead dosage of 1.3 mg/1), while
the data on cadmium, nickel, and zinc were found to be co-
linear. For these four metals, effluent soluble metal concen-
trations from a primary clarifier would increase proportionally
with increased influent metal concentration.
MECHANISMS EFFECTING HEAVY METALS DISTRIBUTION
Heavy metals in influent sewage undergo different physical,
chemical, and biological interactions during each stage of the
treatment process. The extent and affinity of such inter-
actions is a complex function of intrinsic variables, such as
the individual metal,, its concentration, and the presence and
concentrations of other metals; the physical-chemical character-
istics of the aqueous medium such as solids content, pH,
alkalinity, and its associated ions, the nature and variety of
organic and inorganic complexing agents, and external factors
such as plant operational procedures. The mechanisms which can
affect the heavy metals distribution between soluble and solid
phases are inorganic metal salt precipitation, sorption, bio-
logical uptake, and complexation. Of these mechanisms, sorp-
tion and complexation seem to be the most significant, as
discussed below, while the other two are negligible.
Precipitation
Precipitation of a metal ion occurs when the salt with
which it is in equilibrium reaches its solubility limit as
defined by its solubility product. The values of the logarithm
of the solubility products of different metal salts of interest
have been compiled by several authors (Bard, 1966; Feitknecht
and Schindler, 1963; Martell and Smith, 1974a; 1974b; 1974c;
1974d; Sillen and Martell, 1964; 1971). These constants may be
used to plot the theoretical solubility diagrams for each
metal. This information provides a representation of the
theoretical concentrations of the metal salt and its solubility
products in equilibrium with the specified precipitate solid
phase in the aqueous solution, at the indicated pH conditions.
The solubility of metal salts in aqueous solutions is a
function of factors such as pH, temperature, ionic strength,
and the presence of anions or other complexing agents in the
solution (Butler, 1964; Patterson and Minear, 1973). The
values of solubility products determined by different authors
for the same salts under similar conditions may vary. For
instance, at the same temperature (25°C) and ionic strength
(0), the solubility products of nickel hydroxide, Ni(OH)2, are
reported as 10-1°5, 1CT15-5, 1CT7-2 (Sillen and Martell,
1964).
25
-------
Jenkins et al., (1964) conducted experiments to determine
the effect of such factors as pH and concentration of metals
upon the precipitation of. heavy metal salts in water, sewage,
and sewage sludge. They reported that for copper and nickel,
precipitation occurred rapidly. The extent of precipitation
for copper increased slightly over a period of six to eight
hours, while for nickel it was very slight, and the fraction of
that metal precipitated was not as high as with copper. Within
the range of concentrations of copper used, 0.5 - 100 mg/1, the
fraction of metal precipitated increased with increasing con-
centration of copper. Salts of zinc were precipitated up to 60
and 80% at initial zinc concentrations of 100 and 10 mg/1,
respectively.
The solubility of metal salts in the filtered supernatant
of activated sludge is generally somewhat higher than the value
obtained from tap water experiments. For instance, it was
shown that the solubility of lead in the supernatant of acti-
vated sludge at a contact period of four hours was at least
4 mg/1 more than that observed in tap water, at the same pH
(Cheng, 1973). In the same investigation, a similar higher
soluble concentration of trivalent iron was also found.
Patterson and his co-workers (1975) determined the solu-
bility of a number of metals in tap water, filtered raw sewage,
and filtered secondary effluent. They reported that in all
cases, metal solubility in tap water"was less than that ob-
served in filtered raw sewage or in filtered secondary efflu-
ent. Furthermore, raw sewage solubility was greater than could
be accounted for by consideration of intrinsic carbonate,
hydroxide, and chloride ligand effects. Increased solubility
of cadmium in raw sewage and activated sludge mixed liquor
(Patterson, 1979) and of copper in activated sludge effluent
(Patterson et al., 1979) as compared to tap water was also
observed in other recent studies. Metal solubility was also
found to be higher in anaerobic digester supernatant than in
tap water (Patterson and Hao, 1979).
The primary reason for the higher solubility of metals in
different waste media than in tap water, explained by Patterson
and his co-workers, is due to complexation of metals with
inorganic and organic ligands in the waste. This important
phenomenon of complex formation will be discussed subsequently
in this chapter.
Sorption and Biological Uptake
The sorption phenomenon in an activated sludge system
represents the association of a metal with the particulate
matter, which is primarily raw sewage and floe particles,
26
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microorganisms, or colloidal solids. Colloidal matter in
sewage treatment process streams includes bacterial cell
walls, other cellular debris, viruses, phages, detached
flagella, clay and other inorganic particles, plus larger
protein, carbohydrate, lipid, and acid molecules (Rickert and
Hunter, 1972).
The biological floe of the activated sludge particles
plays a key role in the adsorption of heavy metals to suspended
matter. The microorganisms present in the biological floe are
considered to be hydrophilic biocolloids, which are electro-
negative within the operational pH range of the activated
sludge process (Baly, 1931; McKinney, 1956). The surface
charge of the microorganisms is a result of the ionization of
some of the anionic and non-ionic functional groups of the
polymeric materials from which the floes are built (.Bush and
Stumm, 1961; McKinney, 1962). The association of the func-
tional groups depends upon the pH of the system, and/there-
fore, the sorbability of heavy metals also depends upon pH
(Stumm and Morgan, 1970, Cheng, 1973).
The two major sorption processes that take place on the
surface of the sludge solids during the interaction of metal
and biomass are chemisorption and physical adsorption. In
chemisorption, the adsorbed ion undergoes chemical interaction
(for example, forming covalent bonds) with the adsorbent, while
physical adsorption occurs as a result of weak van der Waals'
forces, in which the adsorbed molecule is not fixed to a
specific binding site (Weber, 1972). Experimentally, it is
often difficult to distinguish between the two.
Various types of isotherm models, such as the Langmuir,
Brunauer, Emmett, Teller (B.E.T.), and Freundlich formulations
have been developed to describe sorption behavior (Weber,
1972). Among the three models, the Langmuir and Freundlich
isotherms have been tested and shown to be applicable for
the functional expression of metal association with the
sludge (Cheng, 197.3; Neufeld and Hermann, 1975). However,
Rudolfs and Zuber (1953) reported the failure of copper to
obey the.Freundlich isotherm.
Neufeld and Herman (1975), in their studies of metal
uptake by activated sludge, reported that metal equilibria
relationships for cadmium and mercury were found to fit a
Freundlich isotherm over a limited range of metal concentra-
tion. Since the sorption data in this study were collected
from laboratory activated sludge units under steady state
conditions, some of the metal believed to be sorbed to the
biological floe was possibly taken up by the cells. However,
Cheng (1973) used metal-sludge contact times of only 30 minutes
27
-------
in his sorption studies and demonstrated that Langmuir and
Freundlich isotherms were applicable for.the functional expres-
sion of metal uptake by activated sludge. ...
Neufeld and Hermann (1975) observed a decrease in the per
cent metal on biological floe at increased metal concentra-
tions, and concluded that this may be due to a saturation
effect of the floe surface by the metal and that the initial
metal removal is probably more related to the physical and
chemical properties of the biological mass than to biological
phenomena. As was stated above, results from short-term metal
uptake studies by Cheng et al., (1975) also seem to indicate
that the initial phase of the metal uptake by activated sludge
is due to sorption, a physical-chemical phenomenon.
Complexation
Complexation is the process whereby a positively charged
metal ion attaches or bonds to a molecule or a charged ion
called a ligand. Chelation is a special case of Complexation,
in which a ligand forms more than one bond with a metal ion
(Cotton and Wilkinson, 1966). Of all the mechanisms that
influence the heavy metals distribution in aquatic systems,
Complexation appears to play a relatively significant role.
Important inorganic ligands of environmental importance
include hydroxide, carbonate, sulfate, chloride, phosphate,
flouride, and ammonia. Significant concentrations of the above
complexing agents exist in sewage treatment plant effluents. A
wide variety of organic compounds exists which have chelating
properties. A number of naturally occurring humic substances
which act as chelators are found in natural waters and waste-
waters (Schnitzer, 1971). These substances are usually clas-
sified into two groups: 1) humic acids, the portion of soil
organic matter which is soluble in base and insoluble in
mineral acid and alcohol, and 2) fulvic acids, material ex-
tracted with dilute base and soluble in mineral acid. Reuter
and Perdue (1977) reviewed heavy metal-organic matter inter-
actions in natural waters, while much of the literature con-
cerning metal-fulvic acid interactions has been examined in
extensive reviews by Flaig et 'al., (1975) and by Schnitzer and
Khan (1972).
The organic matter in domestic sewage consists of carbo-
hydrates, proteins, amino acids, fats, and other compounds, and
its composition has been documented by Hunter and Heukelekian
(1965) in the United States, by Painter (1959; 1971) in the
United Kingdom, and by Rebhun and Manka (1971) in Israel.
Pavoni (1970) extracted exocellular polymers from an activated
sludge biomass of 1,200 mg/1 for determination of its chemical
28
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composition, and found that at least 28% of the extracted
material possessed functional groups which can play a role in
the formation of metal-sludge complexes.
The quantity of complexing agents present in sewage is
considerable, and the existence of such compounds in sewage as
well as other environmentally significant systems has been well
established by electrochemical techniques (Allen et al., 1970;
Bender et al., 1970; Chau et al., 1974; Chau and Lum-Shue-Chan,
1974) ion exchange methods (Cheng et al., 1975, Grosser and
Allen, 1977; Patterson et al., 1979; Patterson and Hao, 1979;
Van den Berg and Kramer, 1978) potentiometric techniques
(Grosser, 1975), continuous variation methods (Haas, 1974;
McBryde, 1974), and gel filtration (Mantoura and Riley, 1975).
Chau (1973) reported 1.8-2.5 micromoles of copper com-
plexing capacity for sewage effluents, while Kunkel and Manahan
(1973) reported 0.90 mg/1 (,or 14.16 micromoles/1) for the metal
in sewage. The latter pair of investigators also found that
raw sewage and primary effluent contained 3.39 and 3.01 mg/1 of
copper chelation capacity, respectively. Manahan and Smith
(1973) found that the chelating capacity of tap water for
copper was undetectable, while for raw sewage the capacity was
3.54 mg/1, and for activated sludge effluent, the capacity was
0.9 mg/1. These results suggest a reduction in quantity (but
perhaps not strength) of ligands, as the sewage treatment
process proceeds.
Bender et al., (1970) found that in an activated sludge
effluent binding copper, ligands were associated with molecular
weight fractions of 500-1000 and around 10,000 as determined by
Sephadex G-50 medium. These fractions constitute a significant
portion of the organics discharged from an activated sludge
plant, as indicated by Rebhun and Manka (1971) and Manka et al.,
(1974). Such correlations would allow the use of a parameter
like total organic carbon (TOG) or chemical oxygen demand (COD)
as a substitute for the organic ligands concentrations, in
studies of metal-organic interactions (Cheng et al., 1975).
From the above, discussion, it is evident that complexation
reactions could play an important role in heavy metal trans-
formations in aqueous systems, by influencing the distribution
of the metals between the soluble phase and particulate phases.
HEAVY METALS TRANSPORT
In order to gain preliminary insight into the nature of
heavy metal transport through sewage treatment plants, Patterson
et al., (1975) made a comparison between effluent metal levels
and various influent and effluent wastewater parameters for
several treatment plants in Chicago. They reported a strong
29
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correlation between quarterly mean values for effluent five-day
biochemical oxygen demand (BODs) versus effluent metals (Fig. 2)
and for effluent suspended solids versus effluent metals
(Fig. 3). These observations have been confirmed through
subsequent assessments of 11 treatment plants, in California
(Chen, 1976), Illinois (Cheng et al.. 1975) and New York
(Klein, 1974). Statistical evaluation of effluent BODs versus
total effluent metals yielded overall correlation coefficients
for the 11 plants of 0.82 (range for individual plants 0.80 to
0.98) and for effluent suspended solids versus effluent metals
of 0.87 (range 0.83 to 0.98) (Patterson, 1978).
In addressing these relationships between effluent metals,
BOD5, and suspended solids, alternate conclusions may be drawn.
It is possible that the organic matter represented by BOD5
serves to transport metals into the effluent via complexation;
alternately, high levels of influent metals may cause lowered
treatment efficiency resulting in higher effluent BODs.
Suspended solids may likewise serve to transport metal into the
effluent via sorption, as has been observed, by Patterson et al.,
(1975). However, sewage with high influent metal content may
cause effluent deterioration accompanied by high suspended
solids concentrations. Whatever is responsible for the rela-
tionship between effluent metals and BODs and suspended solids,
the data reported by Patterson et al., (1975) confirm that
effluent metals are strongly influenced through their associa-
tion with effluent suspended solids. More interestingly,
the soluble organics also appear to influence the metals
removal in the treatment plant and thus the metals discharged
from the treatment plant.
30
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40
32
O
S
O 24
O
en
Z
ui
1 6
ui
0 0.2 0.4 0.6 0.8
TOTAL EFFLUENT METALS,MG/L
Figure 2. Correlation of .effluent heavy metals and
effluent BOD5.
31
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32
O
5
°24
_i
O
a
ut
tu
a.
vt
3
to
z
liJ
3.
_j
u.
u.
O O.2 O.4 O.6 O.8
TOTAL EFFLUENT METALS ,MG/L
Figure 3. Correlation of effluent heavy metals and
effluent suspended solids.
32
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SECTION 5
OBJECTIVES
Goal of the Research Study
The primary goal of this investigation was to study the
distribution of selected heavy metals between soluble and solid
phases of different process liquids of ,a conventional activated
sludge system. In order to achieve this goal, the investiga-
tion was divided into five parts and the specific objectives
discussed below were established.
Specific Objectives
Solubility of Metals
1) to determine the solubility limits of heavy metals in
tap water, raw sewage, and activated sludge mixed liquor for a
range of initial pH levels and total sulfide concentrations;
Sorption of Metals
2) to develop heavy metal-solids sorption isotherms for
raw sewage and activated sludge mixed liquor from batch experi-
ments by relating total or soluble metal concentration to
weight of metal adsorbed per unit weight of VSS;
Effect of Waste Parameters on Metals Distribution
3) to study the effect of individual natural waste
characteristics, such as BOD- and., suspended solids and
industrial waste characteristics such as cyanide and ammonia on
heavy metals distribution between soluble and solid phases of
raw sewage and activated sludge mixed liquor;
Metals Distribution in Conventional Activated Sludge Systems
4) to develop heavy metal sorption isotherms for different
process liquids of continuously-run pilot-scale conventional
activated sludge systems, and to compare these isotherms to
those developed in batch experiments under specific objective
B-II;
33
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5) to study the effect of total metal and VSS concentra-
tions on the heavy metals distribution between the soluble and
solid phases of raw sewage and activated sludge mixed liquor;
Model Development
6) to develop an empirical model, based on the results
from the preceding four parts of the investigation, which would
predict the heavy metals distribution between the soluble and
solid phases of different process liquids of a conventional
activated sludge plant, given the influent and operational
characteristics of the system. A secondary part of this objec-
tive was to attempt to develop an overall POTW process model to
describe metals distribution and removal through combined
treatment systems;
7) to present an illustrative example dealing with heavy
metals distribution through a conventional activated sludge
system by using the POTW model developed under the above
specific objective and to discuss the limitations of the model.
34
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SECTION 6
METHODS AND PROCEDURES
The overall objective of this project was to develop
technical information from laboratory studies which could be used
to develop an empirical model for predicting metals distribu-
tion between soluble and solid phases through a conventional
activated sludge system. This study focused, upon the following
eight metals:
Aluminum- - Iron
Cadmium Lead
Chromium "" Nickel
Copper Zinc
These metals were studied at sub-toxic influent concentrations,
and the interrelationships which influence metal distribution
in different process liquids of a conventional activated sludge
system were assessed. The above metals were selected for study
because of their environmental significance. The reason for
selecting the trivalent form of chromium is that very little
hexavalent chromium would be present in the influent raw sewage
to most treatment plants, due to reducing conditions present in
the sewers (Jan and Young, 1978).
As indicated' in Section 5, this investigation was divided
into five parts, and a brief description of each part of the
project is given .here:
I. Batch studies on tap water, filtered raw sewage,
and filtered conventional activated sludge mixed liquor
to determine the solubility limits of the eight metals.
II. Batch studies on raw sewage and activated sludge
mixed liquor to develop sorption isotherms for selected metals.
III. Batch studies on raw sewage and conventional acti-
vated sludge mixed liquor to investigate the influence of both
domestic and industrial waste constituents on metals distribu-
tion between the soluble and solid phases.
... 35
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IV. Continuous-flow pilot-scale conventional activated
sludge systems to study the effect of different variables, such
as total metal concentration, TVSS, SOC,"and major inorganic
ligands, on heavy metals distribution in different process
liquids.
V. Modelling techniques to predict the heavy metals
distribution between soluble and solid phases of different
process liquids of an activated sludge system.
A detailed discussion of the methods and procedures used
under each part of the investigation is in order. Part V,
which includes the model development, is, however, not included
in this chapter, since it is more appropriate to discuss it
after the experimental results are presented. Section 8 of
this report presents the model development.
SOLUBILITY OF METALS
In this part of the study,-solubility of metals at dif-
ferent pH levels and sulfide concentrations was determined for
tap water, raw sewage, and activated sludge mixed liquor.
Tap water used in this study came from Chicago's city
water distribution system, while the raw sewage and activated
sludge mixed liquor were obtained from the West-Southwest
Wastewater Treatment Plant operated by the Metropolitan Sani-
tary District of Greater Chicago. Batch experiments were
performed for each test liquid (tap water, raw sewage, and
mixed liquor) according to the following procedure.
Initially, test liquid was filtered using a 0.45-micron
membrane filter. Raw sewage and activated sludge mixed liquor
were settled and prefiltered prior to membrane filtration, to
enhance membrane filtration efficiency. Twelve batch units,
each consisting of 500 ml of filtered sample in a 1000-ml
Erlenmeyer flask, were set up for each test liquid and.each of
the eight metals. Each set of the 12 batch units was sub-
divided into three groups of four (see Figure 4). Two groups
of each set received sulfide addition so as to result in
initial sulfide concentrations of 1 and 10 mg/1 in each group,
while the third group acted as a control receiving no sulfide
addition. pH levels of 6, 7, 8, and 9 (+0.3 units) were
established in units of each group by pipetting sodium hy-
droxide or nitric acid into the test liquid, as required, with
constant stirring. Prior to metal addition, two test liquids
were adjusted .to the required test sulfide levels. The back-
ground sulfide level was negligible, based upon analysis.
Following sulfide adjustment, the appropriate concentrated
metal solution was pipetted into the test liquid. Simulta-
neously, pH adjustment was made to maintain the target test pH
36
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pir
6.0
7.0
8.0
9.0
Metal
Addition
CO
6.0
0.45y
Filtered
Test
Liquid
Addition
of Acid
or
Alkali
/ *
\
7.0
8.0
9.0
Metal
Addition
S Addition,
1.0 mtr/1
6.0
7.0
8.0
9.0
Metal
Addition
S~
Addition,
10..0m£r/l
Figure 4. Schematic of batch experiments set up to study the minimum solubility
of metals in tap water, raw sewage, and activated sludge mixed liquor.
-------
level. Metal solution was added until a visible precipitate
formed and remained after one minute of continuous stirring.
The sample was continuously stirred during the metal addition
step, and the pH was monitored.
The batch units were sealed with parafilm and placed on a
shaker with continuous shaking at ambient temperature. After
two hours, all batch units were readjusted to correct for any
pH change. Aliquots of the test liquids were taken at six, 12,
and 24 hours for measurement of pH, soluble metal, SOC,.and
sulfide. Background analyses on the test liquids included pH,
total dissolved solids, total volatile dissolved solids,
initial SOC, background metals, sulfide, sulfate, total
phosphorus, orthophosphate, ammonia, hardness, and alkalinity.
SORPTION OF METALS
In this part of the project; sorption of metals to sludge
was studied by measuring the amount of metal associated with
the sludge fraction after the metal is added to the test liquid
at a level below its solubility limit, as determined in Part I.
Batch experiments were set up in a similar fashion to that
described in Part I, according to the scheme outlined in
Figure 5. In this component of"the project; unfiltered samples
were taken, their pH was adjusted to the desired levels, and
the selected metals added. The amount of metal added was below
its solubility limit, to avoid precipitation. The minimum
solubility of each metal for different pH levels was determined
from the experiments in Part I. The initial sulfide concen-
tration in all the samples was kept at the background level,
which analysis showed to be negligible. After the metal
addition, the samples were constantly stirred and aliquots of
samples were taken at 0.25-, 0.50-, 1-, 3-, 6-, and 24-hour time
intervals, to measure pH and soluble metal concentration. The
samples from the 24-hour test period were also analyzed for
total organic carbon (TOC), SOC, inorganic carbon, total
suspended solids (TSS), VSS, total dissolved solids and total
volatile dissolved solids, total phosphorus, orthophosphate,
and alkalinity,
EFFECT OF WASTE PARAMETERS ON METALS DISTRIBUTION
Part III was designed to investigate, in depth, the
influence of domestic and industrial waste constituents on
the distribution of heavy metals between the soluble and solid
phases of raw sewage and activated sludge mixed liquor. This
objective was accomplished by spiking aliquots of test liquids
with each selected waste constituent and determining how the
distribution of metals was affected. In addition, for each
38
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pll
Unfiltered
Test Liquid
03
'O
Addition of
Acid or
Alkali
6.0
7.0
8.0
9.0
Metal
Addition
Figure 5. Schematic of batch experiments set up to study the adsorption of
metals to solids in raw sewage and activated sludge mixed liquor,
-------
domestic/industrial waste parameter tested, the metal distribu-
tion in test liquids with seven different metals compositions
was studied. The factorial design of this series of experi-
ments is explained in Table 5.
As presented in Table 6, seven different domestic/indus-
trial waste parameters, with three levels for each parameter,
were tested. For each parameter tested, a series of seven
different metal combinations was evaluated (Table 7). Metal
Combinations 1 through 4 consisted of mixtures of nine metals
at low (Combination 1) to high (Combination 4) relative con-
centrations. Metal Combinations 5 and 6 were replicates of
Combination 3, providing a statistical basis for the evaluation
of experimental results. In Metal.Combinations 7 and 8 the
metal levels were varied randomly (i.e., some metals were at
high and others at low concentrations). Random metal combi-
nations were incorporated in the studies in order to determine
whether interactive effects upon.metal removal result from
preferential removal of specific metals by the sludge phase.
There was a control group (Co) to which no metal was added.
All metals concentrations fell within the range of influent
values for POTWs reported in Table.1.
The waste parameters listed in Table 6 were also studied
at multiple levels. Hardness, inorganic constituents, and
detergents, the domestic waste variables, were varied by
addition of the required constituent to the raw waste. The "as
received" waste constituted the lowest level tested except for
suspended solids. In the case of suspended solids, the lowest
tested level was obtained by dilution of the raw sewage with
filtered sewage, while the highest level was achieved by the
addition of concentrated (settled) sludge to the raw sewage.
The lowest BOD5/TOC concentration was achieved by dilution of
sewage with tap water, and the highest level by adding sewage
which had been homogenized in a blender and subsequently
filtered to remove remaining particulate matter. Suspended
solids concentration was held constant for each BODs/TOC level
tested. For the industrial waste parameters listed in Table 6,
the levels tested were sub-toxic. The waste parameters listed
in Table 6 simulated the varying characteristics of raw sewage,
as might occur in the collection system.
Eight sets of three batch test units each were used for
each test liquid and each waste parameter. All batch units
consisted of 500 ml of test liquid (raw sewage or .
activated sludge mixed liquor) in 1000-ml Erlenmeyer flasks.
Of the three units in each set, one unit was a control, while
the other two were adjusted for the desired level (Table 6) of
the waste parameter tested. One of the eight sets of the batch
units served as the overall -control group, while the remaining
40
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TABLE 5. PART III EXPERIMENTAL DESIGN : :
Item Explanation
2 Test liquids Raw sewage and activated sludge mixed liquor
X
7 Waste parameters See Table 6
X
3 Waste parameter levels See Table 6
X ;.
7 Metal combinations See Table 7
X
2 Liquid samplings Whole fraction and filtered sample
-------
TABLE 6. LIST OF WASTE PARAMETERS AND THEIR
LEVELS TESTED IN PART III: .
Waste parameter
Concentration,
Level 1 Level 2
mg/1
Level 3
Domestic Waste Variables:
1
2
3
4
Inorganics and Sodium
Hardness
Potassium
Sulfate
Phosphate
Chloride
Calcium
Magnesium
Detergents
Suspended Solids
BOD5/TOC
122.0*
83.0*
97.2*
1.1*
125.0*
33.0*
10.0*
40.0*
252.0
5.5
600.0
415.0
194.4
11.2
625.0
330.0
100 . 0
80.0
40.0
15.8*
1220.0
830.0
388.8
22.4
1250.0
660.0
200.0
120.0
126.0*
38.1
Industrial Variables:
5
6
7
PH
Cyanide . ; ...
Ammonia-N
5.0
Trace*
30.0*.
7.0*
0.1
. 300 . 0
9.0
0.5
450.0
*"As is" Level and Control.
42
-------
TABLE 7. METALS CONCENTRATION IN DIFFERENT METALS
COMBINATIONS: STUDIES IN PART TIT.
Combinations of
. Metal
Aluminum
Cadmium
Chromium
Copper
Iron
mercury
Nickel
Lead
Zinc
0
0
0
0
0
0
0
0
0
Cl
.04
.005
.02
.04
.20
.001
.10
.015
.08
0
0
0
0
0
0
0
0
0
C2
10
.01
.04
.10
.40
.002
.20
.03
.20
C
0
0
0
0
1
0
0
0
0
metal
3,5,6
.20
.025
.10
.20
.00
.005
.50
.075
.40
concentrations, mg/1
0
0
0
0
2
0
1
0
0
C4
.40
.05
.20
.40
.0
.01
.0
.15
.80
0
0
0
0
2
0
1
0
0
c.7
.04
.025
.02
.10
.0
.005
.0
.15
.20
C8
0.40
0.01
0.04
0.40
1.0
0.001
0.20
0.075
0.80
43
-------
seven sets of three units were dosed with six different
combinations of heavy metals. These combinations, C±
through Cg (3, C§, CQ are replicates) were presented in
Table 7.
Each group of 27 batch units was placed on a shaker table
for four hours at ambient temperature. At the termination of
the mixing period, an aliquot of the whole fraction of the
batch unit samples was taken for analyses. An additional
aliquot was filtered through a 0.45-micron filter to obtain
soluble samples. The analyses performed on raw sewage and
activated sludge mixed liquor are given in Table 8.
Since metals influent to activated sludge units have had
extended contact periods with raw sewage, it is invalid to
simulate metals distribution within the activated sludge
process by direct addition of inorganic stock metal solutions.
Therefore, in order to validly simulate the input of metals to
the activated sludge process, it was necessary to precontact
the metals with raw sewage. Therefore the settled supernatant
resulting from the raw sewage experiments was utilized as the
media for introduction of metals to the activated sludge
process.
METALS DISTRIBUTION IN CONVENTIONAL ACTIVATED SLUDGE SYSTEMS
This part of this investigation was designed to study
the distribution of heavy metals in different process liquids
of continuous-flow pilot-scale conventional activated sludge
systems receiving raw sewage and heavy metals at different
concentrations.
The continuous-flow studies of Part IV were divided into
six runs, each run consisting of eight separate parallel
pilot-scale activated sludge treatment systems. Table 9
presents a summary of the schedule of operation of those treat-
ment systems. As indicated in the table, there were 39 dif-
ferent activated sludge treatments contained in this phase.
Table 10 presents the concentrations of different heavy metals
in the raw sewage fed during the 39 different activated sludge
runs. These individual metals concentrations and combinations
were selected on a random basis to simulate low, high, and
mixed levels of'metals in raw sewage.
A flow schematic of each activated sludge system used in
this study is presented in Figure 6. Municipal sewage was
pumped from a City of Chicago sewer line to a laboratory grit
chamber on a continuous basis. Settled grit was discharged.
Raw sewage overflowed from the grit chamber into a 300-gallon
stirred holding tank, having an average six-hour detention
time. The holding tank was equipped with a low level alarm, to
44
-------
TABLE 8. SAMPLE ANALYSES: PERFORMED IN PART III
Test liquid
Parameters analyzed
Raw Sewage and Activated'
Sludge Mixed Liquor
Whole Fraction "
Filtered Supernatant.
Control Units at T=0 and T=4 hours
pH, D.O., TSS, TVSS, Temperature
Total dissolved solids,
Total volatile dissolved solids,
TOC, Alkalinity, Ammonia
Ortho-Phosphate, Total Phosphorus,
9 test metals, calcium, magnesium
Raw Sewage and Activated
Sludge Mixed Liquor
Whole Fraction
Filtered Supernatant
Test Units at T=4 hours
pH*, D.O.
9 test metals, calcium, magnesium
9 test metals, calcium, magnesium
*At T=0 hrs. also.
45
-------
Ol
TABLE 9. SUMMARY OF SCHEDULE OF OPERATION OF CONTINUOUSLY
: : PILOT-SCALE ACTIVATED SLUDGE SYSTEMS . !
Run No.
I
II
III
IV
V
VI
Period of Operation,
Daily
4/5 -
5/19 -
6/27 -
8/23 -
9/26 -
11/1 -
5/19
6/27
8/23
9/26
10/31
11/23
Unit ID: A
1
2
3
3
3
4
Treatment Number
B
5
6
7
8
9
10
C
11
12
13
14
15
16
D
17
18
19
20
21
22
E
23
24
25
26
27
28
F
29
30
31
32
G
33
34
35
36
H
37
38
39
-------
TABLE 10. AVERAGE INFLUENT METALS CONCENTRATIONS (Ug/1) IN RAW
SEWAGE FED TO 39 DIFFERENT ACTIVATED SLUDGE SYSTEMS
Treatment
No.
I
2
3
4
5
6 '
7
8
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29 .
30
31 . .
32
33
34
35
36
37
38
39
Aluminum
783
433
1003
1310
678
298
375
372
932
383
495
500
295
710
678
678
295
677
520
661
983
655
385
785
240
834
890
669
278
567
216
740
778
1574
1193
678
337
693
Cadmium
25
42
140
80
12
124
63
143
60
28
77
105
154
93
59
12.
137
' 88
138
146
53
24
157
135
128
77
57
11
63
69
98
22
222
87
102
11
87
81
Chromium
135
143
174
630
113
84
150
128
600
155
159
153
122
1062
460
113
97
183
144
500
420
106
137
109
124
513
530
113
62
90
128
144
253
140
100
113
84
124
Copper
393.
359
274
280
90
161
177
530
150
429
479
271
460.
338
240
90
173
453
625
425
270
308
460
367
325
363
350
90
162
213
180
302
756
1071
210
98
170
269
Iron
1265
1542
1750
1460
1399
1247
1292
1610
2675
1439
1641
1521
2220
3360
1534
1399
1576
636
1510
3225
2510
1378
2243
2492
1488
3200
2350
1399
1527
936
650
1385
2322
2117
1510
1399
1483
671
Lead
81
93
293
140
35
37
75
320
150
'57
88
158
170
150
90
35
154
267
475
150
140
41
75
221
190
175
180
35
100
143
120
66
200
260
160
35
97
100
Nickel
672
756
1629
2740
334
369
1780
1220
838
795
1002
869
986
1220
1615
245
352
2983
3263
1678
1263
680
653
4008
6075
2050
2132
330
366
490
2050
603
1522
708
319
245
373
619
Zinc
482
413
1114
826
409
383
481
830
1583
510
617
643
553
1003
1575
409
450
1114
694
1025
1860
564
477
514
766
1463
2160
409
440
429
644
520
536
540
463
409
413
450
47
-------
Anaerobic
Digester.... ',
Supernatant
Chenical
Additive
Reservoir
Puao
Holding
Tank
overflow
Unit 1
Pump
.-. to
"^"units
2-8
Primary
Clarifier
chemical
dosing
tanks
underflow
flow splitter.-
Pump
activated sludge
aeration tank
filtered
_air supply
secondary
clarifier
Municipal Sewer
- flow
Figure 6. Flow schematic of continuously-run laboratory-
scale unit.
48
-------
cut off all downstream pumps and valves (except for return
activated sludge pumps and excess sludge wastage valves), in
the event that the raw sewage flow was interrupted. The raw
sewage was pumped into a common header, and then into eight
parallel dosing tanks of two-hour detention time each. Selected
metals were metered into each chemical dosing tank, in accord-
ance with the experiment underway for that particular treatment
system.
Each dosing tank overflowed to the primary clarifier of the
system. The flow rate was about 130 ml/mn. Clarifier (primary
and secondary) design was based upon the design reported by
Mulbarger and Castelli (1966), as modified by and in use at
the U.S. EPA Municipal Environmental Research Laboratory.
The design of the primary and secondary clarifiers was scaled
for compatibility with the activated sludge units.
Primary clarifier overflow was through a flow splitter, to
control hydraulic loading to the activated sludge unit. Each
activated sludge unit was constructed as a five-chamber,
100-liter total capacity unit, with removable partitions to
convert from a plug flow to complete mixed mode. Design
criteria for the activated sludge units were based on the
design of Mulbarger and Castelli (1966). Due to weak raw
sewage, it was difficult to accurately monitor the solids
retention time.
Activated sludge unit mixed liquor overflowed by gravity
to the secondary clarifier, where settled sludge was returned
by a peristaltic pump to the activated sludge unit. The
recycle ratio used for all activated sludge units in this study
was 0.5. Excess sludge was wasted directly from the
secondary clarifier or by intermittent interval wasting of
activated sludge unit overflow as was most appropriate for
control of sludge age. Sampling from each unit was by timer
activated solenoid switch flow diverters, to yield eight-hour
composite samples.
Composite samples of the raw sewage, primary effluent,
activated sludge mixed liquor, secondary effluent, primary
sludge, and secondary sludge were collected several times each
week. Total and soluble metal analyses were performed on all
process liquid samples, while the sludge samples were analyzed
for total metals. In addition, pH, suspended solids, and VSS
were also measured on these samples. Soluble samples of the
four process liquids were analyzed for TOC, SOC, inorganic
carbon, phosphate, sulfate, chloride and ammonia nitrogen.
49
-------
ANALYTICAL PROCEDURES
Metal analyses were performed by atomic absorption spec-
tometry, using a Perkin Elmer Model 305B. The pH was measured
using a Horizon (Ecology Company) Model 5998-10 pH meter.
Total phosphorus, orthophosphate, sulfate, chloride, ammonia,
calcium, hardness, and alkalinity determinations were performed
according to procedures described in EPA Methods (U.S. EPA,
1974). TSS are reported as the weight of the dry solids per
liter of sample retained by a 0.45-micron membrane filter.
total dissolved solids represented the dry solids present in
the filtrate of one liter of original sample. Volatile solids
are reported as the weight of residue lost upon ignition at
600°C of one liter of the original sample. Sulfide ion
concentration was measured.using a specific ion electrode,
Orion-94-16.
50
-------
SECTION 7
RESULTS AND DISCUSSION
SOLUBILITY OF METALS
In order to study the kinetics of metal solubility,
soluble metal concentration was plotted with respect.to time
for each metal, for the four different pH levels, and for each
initial sulfide concentration and test liquid. Since the
number of such graphs is very large (3 test liquids x 3 sulfide
levels x 8 metals =72), only representative plots along with
information on change in pH over the test period, for cadmium,
are presented as examples, in Figures 7 through 12. From graphs
such as those presented in Figures 7-12, the following obser-
vations were made:
1) Equilibrium solubility conditions seem to have been
achieved within six to 12 hours after the addition of the metal
in each test, since soluble metal concentration of most tests
were found to be similar at t=12 hours and t=24 hours.
2) High correlation coefficients were found for soluble
metal vs. pH (Table 11) indicating that variations in soluble
metal within the test matrix are a reflection of changes in
equilibria caused by fluctuations in pH. Changes in soluble
metal concentration of a given sample over the test period also
seem to be due to pH dependent variations of soluble metal
species.
3) Generally, over, the 24-hour period the pH of the
samples with initial pH below 8 increased, while decreasing for
samples with initial pH of 8 or higher. In other words, the
pH of each sample shifted with time toward a pH value of 8,
in most instances. This suggests that the test liquids were
well buffered, probably by the carbonate-bicarbonate system.
4) A comparison of the results for samples at different
initial sulfide concentration levels revealed that sulfide at
all levels tested had no identifiable effect on the rate of
precipitation or at the residual soluble level of metals, at
any initial pH. In order to demonstrate the lack of effect of
initial sulfide concentration on metal solubility, correlation
coefficients were computed for the initial sulfide concentration
51
-------
102 r
10
o
-------
12
TIME, HOURS
18
Figure 8. Change in soluble metal concentration with
respect to time: Cadmium in raw sewage at
sulfide = 1 rag/1.
53
-------
102 r
Initial
PH
6.15
10 -
UJ
10-1
UJ
21
UJ
o
CO
10
-2 -
10-3 -
Final
PH
7.40
10-1
Figure 9.
0 6 12 18 24
TIME, HOURS -
Change in soluble metal concentration with
respect tortime: Cadmium in raw sewage at
suliide «* 10 mg/1.
54
-------
102 r
10
10-1
ca
o
CO
10-2 -
10-3
Initial
pH
TIME, HOURS
Figure 10. Change in soluble raetal concentration with. ..
respect to'time: Cadmium in activated sludge
mixed liquor at negligible sulfide concehtration,
55
-------
10 2
10
1 -
t_>
O
5 10
-i -
CQ
O
CO
10-2 -
10-3
0
12
TIME, HOURS
18
Figure 11. Change in soluble me'tnal concentration with .
respect to-'time: Cadmium .in activated sludge
mixed liquor at sulfide - 1 mg/1.
56
-------
10
Initial
PH
CD
10-1 -
o
2=
O
10-2
LiJ
_l
pa
CD
GO
10-3 -
Final
PH
7.73
8.28
8.39
8.80
0
12
TIME, HOURS
18
24
Figure 12.''
Change in soluble metal concentration with .
respect to time/; Cadmium' in activated sludge
mixed liquor at sulfide = 10/mg/l.
57
-------
TABLE 11. CORRELATION COEFFICIENTS:
. METAL CONCENTRATION
pH VS. SOLUBLE
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Sulfide
level
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
6
.829
.920
.804
.928
.937
.909
.976
.845
.815
.931
.887
.957
.953
.926
.858
.986
.998
.894
.999
. 998 .
.916
.998
.999
.995
.999
.999
.991
Tap water
7
.912
.875
.804
.965
.955
.863
.814
.691
.859
.959
.922
.932
.860
.652
.706
.916
.977
.996
.999
- .989
.999
.999
.999
.998
.866
.907
.987
pH level
8
.988
.998
.835
.785
.918
.605
.810
.949
.946
.967
.943
.981
.703
.832 .
.820
.957
.992
.917
.999
.993
. 876
1.000
.998
1.000
.738
.805
.808
9
.962
.970
.924
.829
.628
.975
.911
.863
.984
.968
.998
.991
.874
.757
.853
.865
.991
.937
1.000
.981
1.000
.993
.861
.971
.921
.986
.647
(continued)
58
-------
TABLE 11. (continued)
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Sulfide
level
0
1
10
0
1
. . 10
0
1
10
0
1
10
o -'
1
10
0
1
10
0
1
10
0
1
10
0
1
10
6
.ND
ND
ND
.999
.999
. . 994 ,
_ f
- .
. 992
.929
.998
.982
.897
.983
.696
.936
.991
.976
.851
. 9.23
.998
.999
.997
.999
.996
.894
Raw sewage
7 .
ND
ND '
ND
.999
.977
.858
_
-
-
.852
.929
.869
.903
.965
.979
.818
.814
.866
.967
.960
.947
.999
1.000
.996
.999
.986
.974
pH level
8
ND
.937
.980
1.000
1.000
.. .981
_
-
-
.848
.750
.934
.949
.981
.981
.807
.783
.949
.994
.988
1.000
.999
1.000
1.000
.570
.830
.835
9
.961
.983
.995
.994
.999
.997
_
-
-
.890
.904
.887
.910
.909
.952
.876
.843
.964
.998
.999
.936
.988
.976
.981
.675
.845
.784
ND
nondetectable
(continued)
59
-------
TABLE 11. (continued)
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Sulfide
level
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
0
1
10
6
1.000
.323
.972
.955
.953
.862
.729
.835
.881
.796
.797
.999
.914
.782
.997
.944
.909
.913
.992
1.000
.932
.999
.999
.999
1.000
.991
.918
Mixed Liquor
7 .
.937
.826
.923
.973
.862
.952
.654
.661
.883
.987
.981
.957
.974
.983
.801
.977
.993
.957
.999
.996
1.000
.999
.997
.999
.983
.911
.954
pH level
8
.904
.993
.997
.784
.634
.762
.922
.943
.745
.998
.999
.953
.998
.967
.775
.995
.981
.942
.988
.999
.907
.999
.996
.999
.769
.939
.945
9
.986
.996
.999
.754
.924
.592
.688
.986
.820
.999
.966
.932
.743
.958
.983
.906
.975
.753
.999
.992
.943
.982
.916
.902
.891
.917
.664
60
-------
vs. the soluble metal levels at 12 hours and 24 hours for each
pH level and each test liquid (see Table 12). The low correla-
tion coefficients in Table 12 confirm that there was no signi-
ficant relationship between the initial sulfide concentrations
at the levels tested, and metal solubility. This lack of
effect of initial sulfide concentration is postulated to be due
to the following reasons: a) much of the sulfide added escaped
from the system during the incubation period, or b) the sulfide
concentrations used in this study were too low to result in any
noticeable changes in metal solubility.
5) In most cases, the soluble metal concentration was
higher in samples of filtered raw sewage and mixed liquor
than in their counterpart tap water samples. This is
possibly due to the presence of organic and inorganic
ligands in raw sewage and mixed liquor, which complex with
the metals and increase their solubility.
6) In accordance with generalized hydroxide and carbonate
solubility relations, the soluble metal concentration decreased
for all test metals except aluminum as the pH increased, while
the reverse pH relationship was observed for aluminum. No
consistent relationship was observed between pH and soluble
metal concentration in the case of lead.
i
Since it was demonstrated (Table 12) that initial sulfide
concentration at the three sulfide levels tested had no effect
on metal solubility, the data for all sulfide levels for each
test liquid were composited into a single data base, thus
making no distinction between the samples with different initial
sulfide concentrations. In order to determine the minimum
solubility of metal in each test liquid, equilibrium soluble
metal concentration was plotted as a function of pH for each
test liquid, as shown in Figures 1.3 through 21. The actual
data points are not shown in these figures because of excessive
overlapping of data points. In the case of mercury in acti-
vated sludge mixed liquor and lead in tap water, the data
points were too scattered to establish a smooth curve. From
these figures, pH values for minimum solubility limits were
determined, and are presented in Table 13.
SORPTION OF METALS
In this investigation, metal was added below its solu-
bility limit (Table 13) to the test liquids and the metal
distribution between the soluble and solid phases was determined.
In order to study the kinetics of metal distribution in raw
sewage and activated sludge mixed liquor, the change in soluble
metal concentration was monitored, and the results are plotted
with respect to time for each metal and each test condition.
61
-------
TABLE 12. CORRELATION COEFFICIENTS: INITIAL SULFIDE
CONCENTRATION VS.. SOLUBLE METAL CONCENTRATION
UNDER EQUILIBRIUM CONDITIONS'
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Sampling
time
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
6
.285
.650
.051
.047
.217
.036
.217
.265
.373
.195
.260
.688
.076
.058
.123
.150
.079
. 802
Tap water
7
.547
.553
.153
.021
.305
.048
.774
.786
.983
.402
.486
.646
.054
.050
.236
.231
.078
.292
pH level
8
.663
.605
.146
.083
.060.
.238
.647
.555
.675
.115
.283
.694
.016
.023
.408
.. 405
.184
.816
9
.710
.580
.035
.210
.280
.507
.681
.598
.957
.618
.493
.821
.009
.012
.624
.570
.898
.148
(continued)
62
-------
TABLE 12. (continued):
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron .
Lead
Mercury
Nickel
Zinc
Sampling
time
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24'Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
Raw
6
ND
ND
.123
" " .104
.389
.414
.433
. 947
.707
.504
.336
.073
.031
.220
.215
.057
.022
sewage
7
ND
ND
.092
.032
.222
.762
. 687
.883
.893
.259
.821
.186
.154
.180
.167
.110
.124
pH level
8
.916
.897
.221
.178
.517
.211
.930
.511.
.615
.368
.197
.157
.171.
.290
.283.
.331
.047
9
.594
.692
.449
.378
.522
.265
.487
.787
.465
.813
.967
.167
.266
.571
.367
.812
.503
ND
nondetectable
(continued)
63
-------
TABLE 1.2.. (continued)
Test
metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Sampling
time
T=12 Hrs.
T=24 Hrs.
T=12 Hrs. .
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
T=12 Hrs.
T=24 Hrs.
Activated sludge pH
6 7 8
.292
.278
.052
.065
.772
.884
.939
.985
.996
.998
.512
.482
.082
.058
.325
. 321
.078
.073
.686
.809
.031
.051
.981
.919
.687
.552
.938
.294
.630
.595
.068
.066
.317
.329
.008
.012
.748
.660
.064
.030
.955
.748
.780
.557
.993
.393
.606
.519
.068
.080
.818
.813
.495
.334
level
9
.640
.712
.102
.097
.992
.239
.796
.622
.297
.490
.818
.394
.045
.046
.598
.911
.788
.385
64
-------
5,0
Tap Water
en
CJ1
c
o
H
4J
n)
M
4J
C
OJ
o
c
6
M
0)
a
0)
O
(O
4.0
3.0
2.0
1.0
0.0
Mixed Liquor.
Raw Sewage
Mixed Liquor
Tap Water
10
PH
Figure 13. Solubility curves for aluminum in tap water, raw sewage,
and activated sludge mixed liquor.
-------
1600
Mixed Liquor
1280
o>
Oi
c
o
H
4J
at
M
4*
c
0)
o
c
O
CJ
4J
0)
-------
Mixed Liquor
Ci
c
o
C
0)
O
C
O
u
cd
.p
(U
^
XI
O
C/l
Tap
Raw
10
PH
Figure 15. Solubility curves for chromium in tap water, raw sewape,
'and activated sludpe mixed liauor.
-------
60
00
c
o
H
P
nl
M
-P
C
o>
u
C
O
U
0)
O
U)
48
36
24
12
Raw Sewage
Mixed Liquor
TO
PH
Figure 16. Solubility curves for copper in tap water, raw sewage,
and activated sludge mixed liquor.
-------
2.00
tn
E
a
o
H
<0
u
c
5
id
p
a)
s
0)
rH
XI
3
r-l
O
1.60
1.20
0.80
0.40
0.00
Mixed Liquor
Raw Sewage
Raw Sewage
Mixed Liquor
Tap Water
10
Figure 17,
PH
Solubility curves for iron in tap water,
nctivated sludge nixed licmor.
raw sewage
-------
6,0
to
*»
CD
1.8
3,6
c_>
CD
2,4
CD
CO
.,2
Mixed Liquor
Raw Sewage
PH
8
10
Figure 18. Solubility curves for lead in raw sewage, 'and activated
sludge mixed liquor .
-------
C
o
H
.p
K)
c
0)
u
c
o
o
nt
O
CO
0.40
rH 0,32
0.24
0.16
0.08
0.00
Tap Water
Raw Sewage
10
PH
Figure 19. Solubility curves for mercury in tap water and
raw sewage
-------
500
to
\
Cn
e
c
o
H
4J
id
M
4J
c
0)
u
c
o
u
rtf
-P
S
-------
150
CO
Cn
e
c
o
H
4J
-P
c
0)
o
C
O
u
m
+J
0)
(1)
o
en
Mixed Liquor
Raw Sewage
120
Tar Water
90
60-
30
' 0
Figure 21. Solubility curves for zinc in tap water, raw sewage,
r 'v activated sludge mixed liquor.
and
-------
TABLE 13. pH OF MINIMUM SOLUBILITIES OF METALS
Metal Tap Water Raw Sewage Mixed. Liquor
Aluminum 7.4 7.9 6.8
Cadmium 7.3-9.0 7.4-8.9 7.8-8.7
Chromium 8.1-8.3 8.3-9.0 8.3-8.8
Copper 8.0-9.3 9.5-9.3 8.6-8.8
Iron 8.7-9.0 6.8-9.0 8.7-9.0
Mercury 8.1-8.3 8.0-9.0
Nickel 8.7 8.5-9.0 8.7-9.0
Zinc 7.0-9.2 7.8-9.1 8.1-9.0
74
-------
Figure 22 presents data for cadmium in raw sewage while Fig-
ure 23 is for cadmium in activated sludge mixed liquor. Due to
the large number of graphs involved, plots for other test
systems are not included in this report.
The following observations were made from the kinetic
studies on metal distribution:
1) A major portion of each metal was removed within 15
minutes after the addition of the metal. The metal removal
appeared to follow a two-phase reaction, as previously reported
by Cheng et al. (1975); an initial rapid phase in which the
metal was rapidly removed followed by a long-term slow-phase
uptake process proceeding for many hours. In most instances,
near-equilibrium conditions seem to have been reached approxi-
mately six hours after metal, addition, with the soluble metal
concentration remaining relatively constant thereafter.
2) Since the amount'of metal added was below the solu-
bility limit of the metal for the pH of each unit, the decrease
in soluble metal concentration of samples cannot be attributed
to precipitation reactions. Thus the decrease in soluble metal
concentration must be due to metal removal by sludge mass
through sorption and/or biological uptake. However, consider-
ing the biological uptake of metal to be slow, especially
during the relatively short test periods used in this study, it
can be assumed that the metal removal was due primarily to
sorption phenomena.
3) The change in pH of a given sample was generally found
to be toward the neutral side. Despite initial pH values
established for a given set of samples ranging from pH of 5.7 to
9.3, the final values were within a pH range of + 0.5 units.
Adsorption isotherms for the test metals in raw sewage
and mixed liquor were developed, as shown in Figures 24 and 25,
respectively. These .isotherms demonstrate the relationship
between the amount of metal added to the test liquid and the
amount adsorbed to the solids in the liquid under equilibrium
conditions. The data points on each curve represent samples
with different equilibrium pH values; the difference being
only within ±0.5 units, in most instances.
The relationships shown in Figures 24 and 25 indicate that
the metal adsorbed per unit weight of volatile suspended
matter increases as the metal added to the test solution
increases, until the solubility limit of the metal is reached.
Such relationship did not appear to exist for iron and mercury,
however. In the case of iron, the added metal remained in
solution with no adsorption taking place, within the range of
75
-------
O'
lOOOr-
<8D-O*
100
GO
V
,ar
0,1
*v-
0
960.8 mg/1
Final pH = 6.9
B-<)
-------
1000
100
CD
GO
10
1.0
0,1
%o c
0
654.08 mg/l Cadmium,
Final pH = 6.6
136.95 mg/1 Cjadmium added
Final pH =.7.1
lp84 mg/1 Cadmium added
8 12 16
TIME, HOURS
20
Figure 23. Change in soluble cadmium concentration in activated sludge mixed
liquor after the addition of the metal below its solubility limit,
-------
10*
CD
GO
CO
UJ
10
11
-1
10
-2
Cd
Cr
Cu
Pb
Ni
Zn
Cadmium
Chromium
Copper
Lead
Nickel
Zinc
10
-2
10'1 1 10 II2
METAL ADDED, MG/L
Figure 24. Adsorption isotherms for metals in raw sewage.
78
-------
10" r
GO
00
10
102
10
3 .
CD
GO
10"J-
10'
Cd
Cr
Cu
Pb
Ni
Zn
Cadmium
Chromium
Copper
.Lead
Nickel
Zinc
10
Ni
-2 10'1 1 10 102
TOTAL METAL CONCENTRATION, MG/L
Figure 25. Adsorption isotherms for metals in activated
sludge mixed liquor.
79
-------
iron added. For mercury, a portion of the metal added was
adsorbed but no relationship was observed between the total
metal concentration in the system and the metal adsorbed per
unit weight of VSS.
The relative placement of the isotherms in Figure 24
indicates that cadmium is most highly adsorbed per unit weight
of volatile suspended solids in raw sewage, followed by chromium,
copper, lead, zinc and nickel, in that order (Table 14).
However, if the isotherm for nickel is extended toward lower
total nickel concentration, it can be seen that the concentration
of sludge-bound nickel per unit weight of TVSS will be higher
for nickel than for other metals at any total metal concentration
in the lower range. This indicates that nickel sorption will
be higher compared to other metals, when nickel concentration
is relatively lower. A similar pattern is also observed for
nickel in activated sludge mixed liquor (Figure 25). From
Figure 25, it can be seen that zinc is adsorbed to the greatest
extent to the activated sludge solids, followed by chromium,
lead, copper, cadmium and nickel. This order of removal is
similar to that reported by Cheng et al., (1975) for activated
sludge solids.
A comparison of the ranked order of metals sorption onto
the two sludges, from Table 14, indicates that for all metals
except cadmium and zinc, the relative sorption ranks are
similar. Cadmium sorbed most in raw sewage solids and much less
in mixed liquor solids, while zinc demonstrated the reverse
pattern.
An attempt was made to determine if the results of the
adsorption experiments would fit a standard Freundlich iso-
therm. As shown in Figures 26 and 27, adsorption of cadmium and
copper in the case of raw sewage, and of cadmium, copper and
nickel in the case of activated sludge seems to follow a
Freundlich isotherm model. The rest of the metals did not fit
the Freundlich model. As noted in Section 4, there have been
conflicting results reported in the literature (Rudolfs and
Zuber, 1953; Cheng, 1973) on the question of metal adsorption by
activated sludge according to Freundlich isotherms.
Table 15 lists the average per cent removals of metals by
the solids portion of raw sewage and mixed liquor, and as can be
seen from the data, the magnitude of metals removals are
generally similar for raw sewage and mixed liquor, except in
the case of mercury. These per cent removal values are higher
than the corresponding values for full-scale treatment plants
reported in the literature. (See Section 4.) However, the
metals removals reported in Table 14 are based on laboratory-
scale filtration through 0.45-micron filters, while the data
80
-------
TABLE 14. ORDER OF CONCENTRATION OF METALS
IN RAW SEWAGE AND ACTIVATED
Metal
Added
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
SLUDGE AT. 10! m{
Rank Order
Raw Sewage
1
2
3
7
4
6
5
r/:i METAL ADDED
of Concentration
Mixed Liquor
5
2
4
7
3
6
1
81
-------
lO'1
10-2
CO
LLJ
sg ID'3
CO
CO
-------
ID'1
10-2
o
a
10
r
"5
:3 t«
10
-6
10
,-7
10
,-8
O Cadmium
n Copper
A Nickel
10'8 10'7 10"6 10'5 10"^ 10"3 10"2
SOLUBLE METAL CONCENTRATION, MOLES
Figure 27. Freundlich adsorption isotherms for metals in
activated sludge mixed liquor.
83
-------
TABLE 15. AVERAGE PER CENT METAL REMOVALS DUE
TO ADSORPTION TO SLUDGE MASS
Metal
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Nickel
Zinc
Test
Raw sewage
75
99
82
0
97
20
29
89
liquid
Activated sludge
80
93
98
8
98
93
34
76
84
-------
in Tables 1 and 2 are based on full-scale primary and activated
sludge clarifier units.
EFFECT OF WASTE PARAMETERS ON METALS DISTRIBUTION
This part of the investigation dealt with batch studies in
which the effects of domestic/industrial waste parameters and
the metals combinations on the metals distribution in raw
sewage and activated sludge mixed liquor were studied. The
discussion concerns the analysis of data on filtered samples of
raw sewage and activated sludge mixed liquor for replicate
metal combinations. As discussed earlier, these three treat-
ments have identical metals combinations.
In order to assess the variability in the residual soluble
metal concentrations in the replicates, and the effect of
different waste parameters on the final individual, metal con-
centration in the filtered fractions of. the test liquids,
statistical evaluations were performed using the technique of
analysis of variance (AOV).
The results of the AOV calculations for the three
replicate treatments are presented in Tables 16 through 31.
Refer to Table 6 for the identification of treatment levels.
The notation used in the AOV tables is described below:
df degrees of freedom
SS sum of squares
MS mean square
F mean square/error square
REPS .replicates
TRMTS treatments (waste parameters)
* F test significant at 0.01
** F test significant at 0.05
The statistical analysis indicates that few of the waste
parameters evaluated in this component of the project had a
significant affect on the final metal concentration of the
filtered test liquids, at the parameter concentrations tested.
However, there were certain waste parameters for which the
AOV indicated an effect on the final soluble concentration,
for some of the metals. These effects are summarized in
Table 32. Among the waste parameters tested, the levels of
pH, inorganics plus hardness, and detergents seem to affect
most metals. The detergent concentration had a significant
influence on the final soluble concentrations of chromium
and nickel in both test liquids, and on those of iron and
lead in mixed liquor.
85
-------
TABLE 16. AOV FOR ALUMINUM IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
498262
350424.
13.
65701.
15466.
852.
709.
6574.
11552
1666.
2429.
42613.
01
87
88
89
67
56
22
67
56
78
MS
6.
9385.
7733.
426.
354.
3287.
5776
833.
1214.
926.
94
98
45
34
78
11
34
78
39
0
10
8
0
0
3
6
0
1
F
.01
.13
.35**
.46
.38
.55*
.23**
.90
.31
TABLE 17
. AOV
FOR ALUMINUM
IN MIXED
LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4 '
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
334975
184528.
1341.
124201.
53.
1350.
50.
304.
9602.
50.
1814
11621.
13
08
76
56
02
0
89
89
0
78
MS
670.
17743.
26.
675.
25.
152.
4801.
25.
907
252.
54
11
78
01
0
45
45
0
65
2
70
0
2
0
0
19
0
3
F
.65
.2**
.11
.67
.10
.60
.0**
.10
.59*
86
-------
TABLE 18. AOV FOR CADMIUM IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
7305
4528.35
0.192
1267.98
66.67
0.22
16.89
4.22
44. 22
44.67
317.56
1009.8
MS
0.1
181.14
33.34
0.11 . .
8.45
2.11
22.11
22.34
158.78
21.95
F
<0.01
8.25**
1.52
0 . 01
0.38
0.10
1.01
1.02
7.23**
TABLE
19.
AOV FOR CADMIUM
IN MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
1748
1283.56
13.52
258
111
8
0.2
0.7
5
0.2
2
65.85
MS
6.76
37
56
4
0.1
0.4
2.5
0.1
1
1.43
F
4.72*
25.8**
39.1**
2.8
0.07
0.29
1.75
0.07
0.7
87
-------
TABLE 20. AOV FOR CHROMIUM IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
3940
2862
8
721
1
213
8
5
8
0
5
104
.72
.45
.72
.56
.56
.67
.56
.00
.00
.56
.2
MS
4.
. 103.
0.
106.
4.
2.
4.
0.
2.
2.
23
10
78
78
34
78
00
00
78
27
F
1.86
45.42**
0.34
47.04**
1.91
1.22
1.76
0.00
1.22
TABLE
21.
AOV FOR CHROMIUM
IN MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
20447
9964
93
6803
0
856
2
268
46
0
128
2283
.01
.53
.66
.00
.89
.89
.67
.22
.00
.00
.13
MS
46.
971.
0.
428.
1.
134.
23.
0.
64.
49.
77
95
00
45
45
34
11
00
00
63
F
0.94
19.58**
0.00
8.63**
0.03
2.71
0.47
0.00
1.29
88
-------
TABLE 22. AOV FOR COPPER IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
241204
118584.5
2997.25
52205.28
3902.89
37.56
2689.56
2810.89
134.89
120.67
4934.22
52690.07
MS
1498.63
. 7457.90
1951.45
18.78
1344.78
1405.45
67.45
60.34
2467.11
1145.44
F
1.31
6.51**
1.70
0.20
1.17
1.23
0.06
0.05
2.15
TABLE
23, AOV
FOR COPPER
.IN MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS .
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
34688
16867
212
10684
246
48
10
28
22
.. .4
96
6471
MS
106
1526
123
24
5
14
11
2
48
140.7
F
0.75
10.8**
0.87
0.17
0.04
0.10
0.08
0 . 01
0.34
89
-------
TABLE 24.. . AOV FOR IKON IN. RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
1690906
1024834.7
18718.05
263035.3
281.56
20200.67
8321.56
9100.22
49355.56
34738.89
688.89
257430.6
MS
9359.03
37576.47
140.78
10100.34
4160.78
4550.11
24677.78
17369.45
344.45
5596.32
F
1.67
6.71**
0.03
1.80
0.74
0.81
4.41*
3.10
0.06
TABLE
25.
AOV FOR IRON IN
MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
ss
2310910
1382785
9617
706830
3545
118084
140
793
6022
4381
3200
74024
MS
4809
100976
1773
59042
70
397
3011
2191
1600
1609
F
3.0
62.8**
1.1
37.0**
0.04
0.25
1.90
1.40
0.99
90
-------
TABLE 26. AOV FOR LEAD IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
94741
15283.35
1022.19
56353.88
6852.67
0.22
8.22
26.89
2.0
8.. 22
168.22
15015.14
MS
511.10
8050.55
3426.3
0.11
4.11
13.45
1.0
4.11
84 . 11
326.4
' F
1.57
24.66="*
10.50**
<0.01
0.01
0.04
<0.01
0.01
0.28
TABLE
27.
AOV FOR LEAD
IN MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
44682
9248
7
26839
8047
313
10
2
0.2
2
0.9
213
MS
3.5
3834
4024
157
5
1
0.04
1
0.45
4.63
F
0.78
828.1**
869.0**
33.8**
1.08
0.22
0.01
0.22
0.10
91
-------
TABLE 28. AOV FOR NICKEL IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
11978474
11127403
38740.21
429608.11
8963.56
175664.67
6560.89
1134
27234.89
10040.22
14177.56
137206.22
MS
19370.11
61372.59
4481.78
87832.34
3280.45
.567
13617.45
5020.11
7088.78
2982.74
F
6.49**
20.58**
1.50
29.45**
1.10
0.19
4.57*
1.68
2.38
TABLE
29.
AOV FOR NICKEL IN
MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS
ERROR
1
2
3
4
5
6
7
df
72
1
2
7
2
2
2
2
2
2
2
46
SS
2068632
1696482
15426
132043
... 451.
175665
1408
38
3654
488
2839
39385
MS
7713
18863
226
87833
704
19
1827
244
1420
856.2
F
9.0**
22.0**
0.3
103.0**
0.8
0.02
2.10
0.28
1.70
92
-------
TABLE 30. AOV FOR ZINC. IN RAW SEWAGE
TOTAL
MEAN
REPS
TRMTS
LEVELS 1
2
3
4
5
6
ERROR
df
63
1
2
6
2
2
2
2
2
2
40
SS
2179688
1288859
4078
582520
81
.2443
1784
28620
2231
12872
206609
MS
20354
97087
40.5
1221.5
892
14310
1116
6436
5165.23
F
0.10
0.47
<0.01
0.01 .
<0.01
0.07
0.01
0.03
TABLE
31.
AOV FOR ZINC IN
MIXED LIQUOR
TOTAL
MEAN
REPS
TRMTS
LEVELS 1
2
3
4
5
6
ERROR
df
63
1
2
6
2
2
2
2
2
2
40
SS
2077686
904561.92
5566.13
198545.52
1652.67
14126
2082.89
26456
9302.89
436.22
861737.87
MS '
2783.07
33090.92
826.34
7063
1041.45
13228
4651.45
218.11
21543.45
F '
0.13
1.54
0.04
0.33
0.05
0.61
0.22
0.01
93
-------
TABLE 32. WASTE PARAMETERS WHOSE TREATMENT LEVELS HAD A SIGNIFICANT EFFECT ON FINAL
SOLUBLE METAL CONCENTRATION
Treatment Level Aluminum Cadmium Chromium Copper Iron Lead Nickel Zinc
1. Inorganics and RS* ML** RS,ML
Hardness
2. Detergents RS,ML ML ML RS,ML
3. Suspended Solids
4. SOC RS
5. pH RS,ML ML RS
6. Cyanide
7. Ammonia ML RS
*RS = raw sewage
**ML = mixed liquor
-------
From the tables of AOV, it can be seen that the F-test
is significant for every metal in each test liquid (except
for aluminum in raw sewage and zinc in mixed liquor) with
regard to the waste parameters (treatments). This indicates
that the final metal concentrations in the filtered fractions
of the test liquids did differ significantly in samples
receiving different treatments although, as demonstrated by
the AOV., there was little significant difference when each
sample was tested against the mean value for that group. A
Studentized Range Test was conducted, with the results shown in
Tables 33 and 34, to determine which treatments resulted in
higher residual soluble metal concentrations. The results
from this statistical analysis are summarized in Table 35.
The numbers given in this table represent the treatment
(waste parameters) applied, and their position in the table
indicates if they result in low, normal, or high concentra-
tions of metals (on a relative scale) in the filtered
fractions of the test liquids, at the end of the equilibra-
tion period.
The studentized range test indicates the following
impacts of the waste parameters on the distribution of
metals between the liquid and solid phases.
1. Higher inorganic and hardness levels induce higher
soluble levels of cadmium, copper and lead, in raw sewage
and mixed liquor.
2. Detergents induce higher soluble raw sewage and. mixed
liquor chromium, and mixed liquor iron, lead, and nickel.
3. For cadmium (raw sewage and mixed liquor) and raw
sewage nickel, a direct relationship is indicated between
increased suspended solids and increased soluble metal. This
result is unexpected since data reported by Cheng (1973)
indicated reduced soluble metal with increased mixed liquor
suspended solids.
4. Higher levels of SOC resulted in higher soluble
levels of mixed liquor chromium and raw sewage iron, only.
5. Cyanide, at the levels tested, influenced the
solubility of raw sewage cadmium, and mixed liquor iron, only.
6. Higher ammonia levels induced higher soluble concen-
trations of mixed liquor aluminum, raw sewage cadmium and
chromium, and raw sewage and mixed liquor copper.
95
-------
TABLE 33. STUDENTIZED TEST FOR TREATMENTS: OF RAW SEWAGE
Studentized Range Test:
CADMIUM qn _ = 4.5 (from statistical tables)
U 0
S = (21.95)* = 1.56
X 9
(QA =)S = 4.5 x 1.56 = 7.03
U O X.
1.56* 3.44 8.22 9.78 11.89 12.30 12.67
T2 T4 T5 T3 T? T6 TI
CHROMIUM S = (2.27)* = 0.50
x -
v = 4.5 x 0.50 = 2.26
X
2.33
T5
3.33
T3
4.56
Tl
5.56
T4
6.00
T6
10.5-
T7
COPPER S = (1145.44)* = 11.28
X 9
(qn ,-)S = 4.5 x 11.28 = 50.77
U . O X
12.00 12.78 16.22 34.56 45.89 73.56 90.22
*Metal Concentration in yg/1, ranked (continued)
96
-------
TABLE 33. (continued)
IRON
S = (5596.32)* = 24.94
X g
(q
0.5'"x
43.56 77.00
= 4.5 x 24.94 = 112.23
81.56 118.89 191.11 239.44 318.67
LEAD
S = (326.4)^ =6.02
x 9~
(q
Oc yk
iJ
1.33
T-
= 4.5 x 6.02 = 27.10
2.44 3.78 4.78
1 /* A 0 ^ o
7.11 7.78 88.3
NICKEL
(2982.74)s = 18.20
9
.)S = 4.5 x 18.20 =
81.92
249.33 327.11 361.00 381.44 424.78 440.89 519.44
(continued)
97
-------
TABLE 33. (continued)
ZINC S = (5165.23)* = 23.96
x g
(qft (-)S = 4.5 x 23.96 = 107.8
\J 3 X
58.67 59.22 89.4 97. 272.78 310.1
T2 Tl T5 T3 T6 T4
98
-------
TABLE 34. STUDENTIZED. TEST FOR TREATMENTS OF MIXED LIQUOR
Studentized Range Test:
ALUMINUM qn K = 4.5
U 0
S = (252.65)* =5.30
x 9
(dn =) (Sv) = 4.5 x 5.30 = 23.84
U . O X..
15.67* 17.33 22.4 44.56 55.44 70.67 149.44
T3 T6 Tl T2 T4 T7 T5
CADMIUM S = (1.43)* =0.40
x 9
(q_ -)S,, = 4.5 x 0.40 = 1.79
U » D X
2.11 3.00 3.67 4.33 4.56 6.22 7.89
T6 T4 T2'. T5 T? T3 T±
CHROMIUM S = (49.63)^ =2.35
x 9
(qn *)S = 4.5 x 2.35 = 10.57
U . Q X
2.11 4.11 6.00 7.67 8.00 27.0 29.22
T T T T T T
*Metal Concentration in yg/1, ranked (continued)
99
-------
TABLE 34. (continued)
COPPER S = (140.7)* = 3.95
x 9~
(q0.5)(Sx} = 4-5 x 3'95 = 17'79
3.89 5.11 6.89 17.89 22.56 24.11
rri rp rri rri rri rn
X6 X2 3 X4 11 i5
IRON S = (1609)* = 13.37
X 9~
(qn K)S = 4.5 x 13.37 = 60.17
U . 0 X
43.89 64.78 81.44 128.89 146.67 700.22
T4 T! T0 T5 T? T2
39.44'
T7
368.53
T6
LEAD
S = (4.63)* = 0.72
x ~9
(q0 5)S = 4.5 x 0.72 = 3.23
1.
T
11
I
5
1.56
T7
3.33
6
3.44
T3
4.33
T4
14.67
T
L2
61.22
Tl
(continued)
100
-------
TABLE 34. (continued)
NICKEL
x
(q
(856.2) =9.75
9~
_)Sv = 4.5 x 9.75 = 43.89
X
102.22 110.11 137.33 139.56 154.78 175.56 249.33
ZINC The test was found not to be significant for any
treatment of zinc in mixed liquor.
101
-------
TABLE 35. COMPARISON OF RESULTS FROM STUDENTIZED RANGE TEST1
LOW3 MEDIUM . HIGH
ALUMINUM
raw sewage
mixed liquor 3,6,1 . 2,4 7,5
CADMIUM
raw sewage 2,4,5 3,7,6,1
mixed liquor 6,4,2 5,7 3,1
CHROMIUM
raw sewage 5,3,1 4,6 7,2
mixed liquor 3,5,6,7,1 4,2
COPPER
raw sewage 6,2,5,3,4 1,7
mixed liquor 6,2,3,4 1,5,7
IRON
raw sewage 1,2,3,5 7,6 4
mixed liquor 4,1,3 5,7 2,6
LEAD
raw sewage 5,6,2,3,4,7 1
mixed liquor 5,7,6,3 4 2,1
NICKEL
raw sewage 2,1 7,6 2
mixed liquor 4,3,1,5 4,7,6,5 3
ZINC
raw sewage 2,1,5,3 6,4
2
mixed liquor
1) The numbers in the columns indicate the number of the
waste .parameter tested. See Table 6 for the waste para-
meter corresponding to the number.
2) F-test not found to be significant.
3) Low means that the waste parameter given under this
column has the least effect compared to other waste para-
meters on the distribution of metal between solid and
soluble phases; medium means an intermediate effect;
high means relatively significant effect.
102
-------
These results indicate that no single waste parameter
influences the metals distribution of all metals tested, and
that different parameters affect different metals. The affect
may be observed in one or both of the raw sewage and mixed
liquor process streams. Further, the affect may be rather
slight, since AOV failed to identify many of these factors.
METALS DISTRIBUTION IN CONVENTIONAL ACTIVATED SLUDGE SYSTEMS
This part of the investigation dealt with the 39 separate
continuously run pilot-scale activated sludge units. The
overall range and average values of different parameters
(including metais) for raw sewage, primary effluent, mixed
liquor, and secondary effluent for these 39 units are
summarized in Table 36. As is evident from Table 36, a wide
range of values for each parameter was observed in the raw
sewage feed. As may be expected, the range of values of
different parameters of other process liquids is also wide.
However, the per cent soluble metal for any given metal
seems to be relatively constant for all the process liquids,
despite large variations in the total metal concentration.
Tables A.I through A.39 in Appendix A summarize the average
equilibrium values of various parameters analyzed in each
treatment for the. four different process .liquids (raw sewage,
primary effluent, mixed liquor and secondary effluent.
Overall System Characteristics
As demonstrated in Table 36, the influent sewage to the
pilot treatment systems was relatively weak, averaging 62 mg./l
VSS and 28 mg/1 SOC. The primary clarifier effluent VSS
averaged 36 mg/1, representing on the basis of average influent
and effluent a 42% removal efficiency of VSS. Overall VSS
removal efficiency, from raw sewage to secondary effluent,
was 76%. The clarifiers sometimes performed erratically,
with negative efficiencies of VSS removal occurring in the
primary elarifier. Settled sludge bridging was also a
problem, and would result in floating sludge in the primary
and secondary clarifiers, plus interruption of sludge return
from the secondary elarifier to the aeration basin. Mechani-
cal rakes were eventually installed in the secondary clari-
fiers, and were at least partially effective in solving the
operational problems of that unit process.
As indicated by the reduction in SOC across the primary
elarifier, there appeared to be significant biological activity
in that process. SOC was reduced from an average of 28 mg/1 in
the raw sewage, to 19 mg/1 in the primary effluent. Thus,
biological growth in the primary elarifier may have contri-
buted to the erratic VSS removal efficiencies observed in
103
-------
TABLE 36. OVERALL AVERAGES AND RANGES FOR DIFFERENT PARAMETERS IN DIFFERENT
TEST LIQUIDS*
Parameter
VSS
SOC
Aluminum
Cadmium
Chromium
Copper
'
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Ave.
Range
Ave.
Range
Ave.
Range
Ave.
Range
Ave.
Ave.
Range
Ave.
Range
Ave .
Ave.
Range
Ave.
Range
Ave.
Ave.
Range
Ave.
Range
Ave.
Raw
sewage
62
2-460
28
3-294
652
63-5100
81
11-425
12.4
85
3-650
16
1-305
18.8
241
18-1700
4.2
2-17
1.7
330
11-2900
17
1-157
5.2
Primary
Effluent
36
1-196
19
1-106
478
24-3032
79
8-375
16.5
72
2-514
14
1-295
19.4
170
5-650
4.0
2-9
2.4
281
3-913
12
1-100
4.3
Mixed
liquor
1307
150-8106
14
1-200
7179
526-21000
61
0-325
0.8
411
4-810
15
1-98
3.6
1292
10-3150
4.0
2-9
0.3
3215
4-8500
14
1-96
0.4
Secondary
Effluent
15
1-220
11
1-38
472
67-2732
83
5-350
17.6
44
2-382
13
1-67
29.5
162
31-1600
3.9
2-5
2.4
210
11-1866
14
1-50
6.9
*VSS and SOC expressed as mg/1,
metals concentrations as yg/1.
(continued)
-------
TABLE 36. (continued)
O
Parameter
Iron
Lead
Nickel
Zinc
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Total
Soluble
% Soluble
Ave.
Range
Ave.
Range
Ave.
Ave.
Range
Ave.
Range
Ave.
Ave.
Range
Ave.
Range
Ave .
Ave.
Range
Ave.
Range
Ave.
Raw
sewage
1778
200-7000
118
5-783
6.6
142
0-1069
24
2-197
16.9
1349
22-8500
319
8-1168
23.6
741
100-5000
90
2-1000
12.1
Primary
Effluent
1247
200-3500 ;
97
5-842
7.8
100
0-600
27
2-248
27.1
794
5-15000
297
9-1479
37.4
637
80-3400
74
1-430
11.6
Mixed
liquor
28184
1048-8400
70
3-885
0.2
1971
11-9000
24
2-474
1.2
6602
77-23000
290
5-975
4.4
11589
1000-36000
79
2-900
0.7
Secondary
Effluent
1089
100-5800
52
3-580
4.7
64
0-1200
18
2-211
28.3
733
10-5000
250
3-849
34.1
514
100-4100
65
1-900
12.6
*VSS and SOC expressed as mg/1,
metals concentrations as yg/1.
-------
that unit. Overall SOC reduction across the treatment
systems averaged 61%, yielding an average secondary effluent
SOC value of 11 mg/1.
As would be expected, there was a strong correlation
between VSS and TSS for all process liquids. The ratio
VSS:TSS, and the squared correlation coefficients (r2) are
listed below:
Process liquid VSS:TSS r2
Raw sewage 0.73. 0.95
Primary effluent 0.68 0.89
Mixed liquor 0.68 0.92
Secondary effluent 0.65 0.96
Primary sludge 0.69 0.90
Secondary sludge 0.68 0.94
There was no correlation between VSS and SOC, in any process
liquid. For raw sewage, this indicates that VSS and SOC varied
in strength independently.
The patterns of metals transported across the treatment
systems are extremely interesting. The range of raw sewage
concentrations for each metal were quite broad, reflecting
the combination of material fluctuations in the influent raw
sewage, plus the spiking of the raw sewage with metals
within the laboratory. For each metal, there was a reduction
in the average total metal concentration across the primary
clarifier. However, there was no significant reduction in
the average soluble metal across that process. This indicates
that the reduction in total metal is due to sedimentation of
solids-bound metal. The lack of change in soluble metal
concentration from'raw sewage to primary effluent indicates
that there was no redistribution of metals between the
soluble and solid phases within the primary clarifier.
The total concentrations of metals in the mixed liquor
are much higher than in the raw sewage, typically by 5- to
10-fold. For iron, lead, and zinc the concentration factor
is closer to 15-fold. However, the soluble metal levels in
the mixed liquor are equivalent to those in the raw sewage
and primary effluent, revealing that the higher metal con-
centrations in the mixed liquor are the result of the higher
mixed liquor VSS concentrations. The mixed liquor VSS are
about 10-fold greater on the average then the raw sewage
VSS. Comparing this to the data for metals suggests that
iron, lead, and zinc are disproportionately overconcentrated
106
-------
(compared to the concentration of VSS) in the mixed liquor,
while cadmium, chromium, and nickel (at a 5-fold concentra-
tion from primary effluent to mixed liquor) are dispropor-
tionately underconcentrated. In other words, for these six
metals the concentrations effect of VSS (with which the
major fraction of each of the metals is associated) in the
mixed liquor does not fully account for the concentration
factor observed for those metals.
An evaluation of the composite of secondary clarifier
effluent (secondary effluent) reveals that the soluble
metals levels are essentially unchanged from the raw sewage
soluble metals levels, except for iron and perhaps nickel
and zinc. Thus, from the data base of Table 36, there is
either no, or only slight change in the soluble levels of
the test metals through the full-treatment system. Any
removal of metals in the unit processes therefore results
only from removal, of .dn.f-lue.nt solids-bound metals. The
"implication, of this finding is that in combined treatment
systems, metals removal efficiency. Is.directly tied to the
efficiency of removal of suspended solids. Table 37 sum-
marizes the average metals removal efficiencies across the
primary clarifier activated sludge aeration basin plus
secondary clarifier, and overall treatment system.
Although the soluble.metals levels in the secondary
effluent were equivalent to those in the influent sewage,
the relative contribution of the soluble metals to the
total secondary effluent metals discharge varied. On the
average, soluble chromium and iron constituted less than 5%
of the total secondary effluent levels of these metals,
while soluble cadmium, lead, and nickel contributed close to
30% of the total second effluent values of these latter
metals. This indicates that enhanced VSS removal in the
secondary clarifier would reduce total secondary effluent
metals such as chromium and iron (which are predominantly
solid-bound in the secondary effluent) to a much greater
extent than for cadmium, lead, or copper.
Relationship Across the Primary Clarifier
Since the primary clarifier represents the first step
in solids, and associated solids-bound metal removal, the
performance of that process unit is discussed in this section.
Figures 28 through 35 present relationships between the
metals concentrations of raw sewage and primary effluent.
These graphs clearly demonstrate that the metal concentration
in the primary effluent is a function of the metal concentration
in the influent to the primary sedimentation tank. Metal
removal in the primary clarification stage is due to suspended
107
-------
TABLE 37. AVERAGE PERFORMANCE OF TREATMENT SYSTEM IN METALS
REMOVAL : ..-;
Metal
% Removal
Across P. Clarifier
% Removal
Across A. Sludge
Overall
6 Removal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
26.6
15.3
29.5
14.8
29.9
29.6
41.1
14.0
1.3
38.9
4.7
25.3
12.7
36.0
7.7
19.3
27.6
48.2
32.8
36.4
38.8
54.9
45.7
30.6
108
-------
0,8
UJ
UJ
o
(O
0,0
O o
o
oo
0,0 0,1
0,8
RAW SEWAGE, MG/L
1,2
1,6
Figure 28. Relationship between total aluminum concentrations in raw sewage
and primary effluent. ;
-------
CADMIUM
120
80
UJ
LU
DC
Q.
0
0
40 80 120
RAW SEWAGE, UG/L
160
Figure 29. Relationship between total cadmium concentrations in raw sewage
and primary effluent.
-------
600
2 400
LU
U.
LL
LLi
> 200
DC
DC
Q.
0
COPPER
200 400 . 600
RAW SEWAGE, UG/L
800
Figure 30. Relationship between total copper concentrations in raw sewage
and primary effluent.
-------
0,4
LU
M
to
0.2
cc
QL.
0,0
0,0
1
1
1
0,2 0,1 0,6
RAW SEWAGE, MG/L
0,8
Figure 31. ' Relationship between total chromium concentrations in raw sewage
and primary effluent.
-------
M
M
i 2,0
H^
LU
Zj
U_
U_
UJ
| 1'°
S
Q_
0,0
o
_ - 00
o
0 ° 0
o o Q o
&o o° ; o
6° o
_ vj O
0 0 j
^>
\ II 1
0,0
1,0 2,0 3,0
RAW SEWAGE, MG/L
Figure 32. Relationship between total iron concentrations in raw sewage and
primary effluent.
-------
0,6r-
0,2
0,0
o
o o
cP0
I
I
I
0,0
0,2 0,4 0,6
RAW SEWAGE, MG/L
0,8
Figure 33. Relationship between total lead concentrations in raw sewage and
primary effluent.
-------
NICKEL
Ol
1200
o
2 800
UJ
LL
LL
UJ
cc
i
cc
0.
400
0
0
D
400 800 1200
RAW SEWAGE, UG/L
1600
Figure 34. Relationship between total nickel concentrations in raw sewage
and primary effluent.
-------
ZINC
1200
o>
LLJ
UJ
cc
cc
Q.
800
400
0
400 800 1200
RAW SEWAGE, UG/L
1600
Figure 35. Relationship between total zinc concentrations in raw sewage
and primary effluent.
-------
solids removal, and the scatter of data in Figures 28 through
35 reflects variations in suspended solids removal performance
by the primary clarifier.
Figures 36 through 39 present the relationship between
the per cent removal of suspended solids and per cent removal
of sludge-bound metal in the primary clarifier. These
figures indicate a linear relationship between the solids
removal and sludge-bound metal removal, and confirm that
sedimentation of solids-bound metal is the major removal
mechanism for metals during primary .sedimentation. The
lines on Figures 36 through 39 have about 1:1 slopes. If sludge
bound metal is equally distributed per unit of VSS mass
among particules over the full spectrum of settleability,
the data points should be fit by that line. Although there
is scatter in the data, for at least copper and zinc, the
points fall below the line, suggesting that these two metals
are disproportionately distributed onto the non-sett.leable
fraction of the VSS. Similar results were observed for
chromium and iron. The data for the remaining four metals
generally followed the line of about 1:1 slope, although with
some scatter, indicating uniform distribution of metal per
unit of VSS among all solids particles irrespective of their
settling characteristics.
An attempt was made to relate the metal concentrations
in the primary effluent and mixed liquor, as shown for
cadmium in Figure 40. The mixed liquor cadmium concentra-
tion seems to Increase with increasing cadmium concentration
in the primary effluent, but the data are too scattered to
draw firm conclusions based upon this preliminary data
analysis. One reason for this scatter could be variation in
the amount of. metals sent back to the aeration tank through
the sludge recycle line. The slope of the line of Figure 40,
which is equivalent to a concentration factor of mixed
liquor to primary effluent cadmium, is about 6. This is
equivalent to the mixed liquor to primary effluent concen-
tration factor indicated for average performance in Table 36.
Figure 41 presents the relationship between the cadmium
concentrations in mixed liquor and secondary effluent. This
figure does not indicate a strong relationship between the
metal concentrations of the two process liquids. Similar
observations were also made in the case of other metals
studied. This lack of correlation is probably principally
due to variations in the efficiency of suspended solids (and
associated solids-bound metals) removal in the secondary
clarifier.
117
-------
CADMIUM
Q
LLI
o
2
LU
CC
LU
O
Q
s
100
80
60
20
0
I
0 20 40 60 80
°/> TSS REMOVED
100
Figure 36. Relationship between the. removals of"TSS
and sludge bound cadmium.
118
-------
100
o
LLJ
O 80
UJ
oc
UJ
O
Q
3
CO
S
60
40
20
0
COPPER
f I I |_
0 20 40 60 80
X, TSS REMOVED
100
Figure 37. Relationship between the removals of TSS
and sludge bound copper.
119
-------
NICKEL
Q
UJ
UJ
oc
UJ
UJ
o
o
CO
s
lOQj
80
60
20
0 20 40 60 80
X, TSS REMOVED
100
Figure 38. Relationship between the removals of TSS
and sludge bound nickel.
120
-------
ZINC
a
lil
Ill
CC
i
in
LU
a
Q
CO
100
80
60
20
qlx^ ot % I | |_
0 20 40 60 80
°/0 TSS REMOVED
100
Figure 39;. Relationship between the removals of TSS and
sludge bound zinc.
121
-------
to
to
200
o
150
DC
S 100
50
0 I
0
100
200 300 400 500 600
TOTAL CADMIUM IN MIXED LIQUOR, MG/L
Figure 40. Relationship between total cadmium concentrations in primary
effluent and mixed liquor.
700
-------
13
120 r-
CO
LU
a:
CD
<_J
LU
CO
80 -
LU
C_)
O
0
0
0,2 0,4 0,6 0,8
TOTAL CADMIUM CONCENTRATION IN MIXED LIQUOR, MG/L
1,0
Figure 41. Relationship between the total cadmium concentrations in mixed
liquor and secondary effluent.
-------
Adsorption Characteristics of Process Liquids
In order to study the adsorption characteristics of
sludge solids of different process liquids, different types
of adsorption isotherms were attempted. Efforts to relate
the soluble metal concentration to the concentration of
metal on the sludge solids, using either Freundlich and
Langmuir isotherm models, were futile.
Figures 42 through 45 present best fit adsorption
isotherms for the four process liquids and the eight metals
studied in this investigation. These isotherms relate the
concentration of total metal present in the process liquid
to the amount of metal associated with a unit weight of VSS
in that process liquid. There seems to be a log-log relation-
ship between the two variables, although there is quite a
bit of scatter in the data points for most of the metals.
The actual data points from which the lines in Figures 42
through 45 were developed are not presented, because of
excessive overlapping of too many data points. However, the
'goodness of fit' of each line in the figures representing
the adsorption behavior of the metals can be evaluated by
examination of the regression analysis data presented in
Tables 38 through 41. Figure 46 presents the adsorption
isotherm for nickel in mixed liquor, which had the best
regression coefficient of 0.98, while Figure 47 is the
isotherm for aluminum in raw sewage which had the poorest
regression coefficient of 0.36. These figures give an idea
of the relative scatter of data, with respect to the regres-
sion coefficients.
Among the four process liquids studied, more signifi-
cant log-log relationship (higher regression coefficient)
between the sludge metal and total metal in the system was
obtained in the case of mixed liquor than in other process
liquids. This may directly result from the fact that in
mixed liquor, the soluble metal fraction of the total metal
is extremely low and typically below 1%. In the other
process liquids, the soluble fraction is much greater, and
therefore constitutes a higher portion of the total metal in
raw sewage, primary effluent, and secondary effluent.
The adsorption isotherms presented in Figures 42 through
45 demonstrate that the amount of metal bound per unit of
VSS generally increases with increasing total metal concen-
tration, over the range studied. It may be that at very
high metal concentrations, the sludge solids would reach a
maximum adsorption capacity, where the isotherm would level
off. However, precipitation of metals might occur before
124
-------
1000
100
10
GO
GO
LU
CT5
GO
1,0
0,1
0,01
Al = Aluminum
Cd = Cadmium
Cr = Chromium
Cu = Copper
Fe = Iron
Pb = Lead
Ni = Nickel
Zn = Zinc
0,001
0,01 0,1 1
TOTAL I'lETAL CONCENTRATION, MG/L
Figure 42. Metal adsorption isotherms for raw sewage.
10
125
-------
1000
2:
CJ3
C/O
CO
ca
ZD
00
100
10
1,0
0,1
Al = Aluminum
Cd = Cadmium
Cr = Chromium
Cu = Copper
Fe = Iron
Pb = Lead
Ni = Nickel
Zn = Zinc
I
0,001 0,01 0,1 1
TOTAL METAL CONCENTRATION, MG/L
10
Figure 43. Metal adsorption isotherms for primary effluent.
126
-------
ioo r
CJ3
s:
6
GO
GO
-------
CD
GO
CO
LU
C3
ca
1000 r
100
10
0,1
Al = Aluminum
Cd = Cadmium
Cr = Chromium
Cu = Copper
Fe = Iron
Pb = Lead
Ni = Nickel
Zn = Zinc
I
0,001 0,01
0,1
10
TOTAL METAL CONCENTRATION, MG/L
Figure 45. Metal adsorption isotherms for secondary effluent,
128
-------
TABLE 38. REGRESSION ANALYSIS DATA FOR FIGURE 42 - RAW SEWAGE
Metal
Aluminum
Cadmium
Chromium
Copper
.Iron
Lead
Nickel
Zinc
Regression
log
log
log
' log
log
log
log
log
y
Y
Y
Y
Y
Y
Y
Y
= 0.
= 1.
= 1.
= 0.
= 1.
= 1.
= 0.
= 0.
68
46
25
83
88
78
37
55
Equation
(log
(log
(log
(log
(log
(log
(log
(log
X)
X)
X)
X)
X),
X)
X)
X)
- 0.
- 2.
- 2.
- 1.
- 4.
- 3.
+ 0.
- 1.
83
57
26
23
50
42
05
89
0
0
0
0
0
0
0
0
r2
.36
.82
.71 ,
.47
.69
.75
.36
.50
TABLE 39.
REGRESSION ANALYSIS
EFFLUENT
DATA
FOR
FIGURE 43
- PRIMARY
Metal
Aluminum
Cadmium
Chromium
Copper
Iron'
Lead
Nickel
Zinc
Regression
log
log
log
log
log
log
log
log
Y
Y
Y
Y
Y
Y
Y
Y
= 1.
= 1.
= 1.
~~ JL
= 1.
= 1.
= 1.
= 0.
58
60
18
08
05
47
00
45
Equation
(log
(log
(log
(log
(log
(log
(log
(log
X)
X)
X)
X)
X)
X)
X)
X)
- 3.
- 2.
- 1.
- 1.
- 1.
- 2.
- 1.
- 1.
21
70
91
74
72
61
81
48
0
0
0
0
0
0
0
0
r2
.76
.92
.83
.73
.55
.82
.71
.75
129
-------
TABLE 40. REGRESSION ANALYSIS DATA FOR FIGURE 44 - MIXED
LIQUOR
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
log Y
log Y
log Y
log Y
log Y
log Y
log Y
log Y
Regression
= 0.69
= 0.71
= 0.68
= 0.66
= 0.82
= 0.80
= 0.89
= 0.75
Equation
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
- 1.93
- 2.36
- 2.11
- 1.89
- 2.35
- 2.44
- 2.71
- 2.11
r2
0.84
0.81
0.71
0.90
0.91
0.89
0.98
0.86
TABLE 41.
REGRESSION
EFFLUENT. .
ANALYSIS
DATA FOR
FIGURE 45 -
SECONDARY
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
log Y
log Y
log Y
log Y
log Y
log Y
log Y
log Y
Regression
= 0.89
= 0.80
= 0.77
= 0.53
= 0.70
= 1.05
= 0.87
= 1.08
Equation
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
(log X)
- 0.97
- 0.96
- 0.62
- 0.08
- 0.25
- 1.37
- 0.94
- 1.43
r2
0.69
0.84
0.82
0.71
0.64
0.88
0.83
0.86
130
-------
100
O)
x
o>
10
CO
0)
CO
+*
0)
0)
O)
o
CO
0,1
MIXED LIQUOR: NICKEL
log Y = 0.89 log X - 2.715
r* = 0.98
I
I
I
0,1 1 10 100
Total Metal Concentration, mg/L
Figure 46. Adsorption, isotherm for nickel in mixed liquor.
131
-------
ID
i 100
3.
CO
CO
x 10
C/3
log Y = 0.68 log X - 0.836
-r2 = 0.36
0,01 0,1 1,0 10
TOTAL METAL CONCENTRATION/ MG/L
Figure 47. Adsorption isotherm for aluminum in raw sewage,
132
-------
the maximum adsorption capacity of the sludge is reached.
If precipitation of metals occurred, it would be difficult
to develop valid adsorption isotherms of sludge solids due
to the presence of another solid phase, the metal precipitate.
Figures 42 through 45 are indicative of general trends,
but can not be used for predictive purposes such as esti-
mating the amount of metal present in the solid phase for a
given total metal and VSS concentrations. This is due to
the degree of scatter of the data points around the isotherm
lines for most of the metals. This .scatter results in the
generally poor correlation coefficients seen in Tables 38
through 41.
The reason for this poor fit is that, the isotherms
presented in Figur.es 42 .through .45 .can. not account for
-differences in VSS concentrations. This problem is, however,
overcome by"plotting isotherms for total metal concentration
'vs. sludge metal/VSS, for constant VSS concentrations, as
shown in Figure 48. This figure presents adsorption iso-
therms for cadmium in raw sewage at VSS concentrations of
25, 50,, and .100 mg/1. Since the experiments were not originally
designed to keep the VSS concentrations constant at given
levels, the VSS concentration is noted next to each data
point on Figure 48,. and isotherms for the three VSS levels
.were interpolated. .-.,.;
\ _.-.- . .
Figures.48 through 51 are the adsorption isotherms for
cadmium in raw sewage, primary effluent, mixed liquor, and
secondary effluent, respectively. Similar isotherms are
presented in Figures 52 through 75 for the other seven
metals investigated. The figures for all metals in all
process liquids follow similar patterns, and illustrate the
following:
1) As the total metal concentration increases, the
amount of sludge bound metal per unit weight of
VSS also increases, at each constant level of VSS.
2) At any given total metal concentration, the .
sludge-bound metal per unit weight of VSS de-
creases as the VSS concentration increases.
3) At low total metal concentrations, the effect of.
VSS on sludge-bound metal is slight.
These relationships hold for all eight metals, and for all
four process liquids.
The relationships represented in Figures 48 through 75
can be described mathematically, and therefore can provide a
133
-------
CO
o
\
CD
3.
»
GO
GO
CD
(=1
30 mg/1
0,0/1 0,03 0,12 0,16
TOTAL METAL CONCENTRATION, MG/L
Figure 480 Adsorption isotherms of cadmium in raw sc.vage at different VSS
concentrations0
0,20
-------
CO
01
o
CO
GO
16
CD
ca
8
0
0
mg/l
I
\
mg/l
L
I
0,2 0,4 0,6 .0,8
TOTAL METAL CONCENTRATION, MG/L
1,0
Figure 49. Adsorption isotherms of cadmium in primary effluent at different
TVSS concentrations.
-------
CO
01
0,6
\
CD
GO
CO
0,1
0,2
0
0
1500 mg/1
2000 mg/1
0,2 O.'l 0,6 0,8
TOTAL METAL CONCENTRATION, MG/L
1,0
Figure 50. Adsorption isotherms of cadmium in mixed liquor at different TVSS
concentrations.
-------
3.
00
GO
= 2
00
0
0
ig/1
0,02 0.04 0,06 . 0,08
TOTAL METAL CONCENTRATION, MG/L
0,10
Figure 51. Adsorption isotherms of cadmium in secondary, effluent a-
TVSS concentrations. "
different
-------
CO
00
CJ
CO
CO
ca
_j
CO
0
20 mg/1
1,2
1,6
1,8
TOTAL METAL CONCENTRATION, MG/L
Figure 52. Adsorption isotherms for aluminum in raw sewage at different
VSS concentrations.
-------
CO
co
30
CO
->v.
CD
CO
oo
«=c
CD
ca
CO
20
10
0,0
0,0
j_
0,2. 0,4 0,6 0,8
TOTAL METAL CONCENTRATION/ MG/L.
35 mg/1
60 mg/1
1.0
Figure 53. Adsorption isotherms for aluminum in primary effluent at different
VSS concentrations.
-------
M
^
O
mg/1
0,4 0,6 0,8
TOTAL METAL CONCENTRATION, MG/L
25 mg/1
1,0
Figure 54. Adsorption isotherms of aluminum in secondary effluent at
different VSS concentrations.
-------
60 r
M
ttx
CO
co
UJ
s:
UJ
CD
=3
IS)
40 -
20 -
0,0
0,0
0.4 0,6 0,8
TOTAL METAL CONCENTRATION, MG/L
1,0
Figure 55. Adsorption isotherms for chromium in raw sewage at different
VSS concentrations.
-------
M
>&.
to
\
co
co
ca
ra
co
0,1
15 mg/1
0,2 0,3
TOTAL METAL CONCENTRATION, MG/L
25 mg/1
30 mg/1
0,5
Figure 56. Adsorption isotherms for chromium in primary effluent at different
VSS concentrations.
-------
OJ
to
3.
GO
GO
10 mg/1
0,1 0,2 0,3
TOTAL METAL CONCENTRATION, MG/L
25 mg/1
0,5
Figure 57. Adsorption isotherms for chromium in secondary effluent at
different VSS concentrations.
-------
* 16 -
00
oo
8 -
CD
0
0
mg/l
mg/1
0,2 0,<[ 0,6 0,8
TOTAL METAL CONCENTRATION, MG/L
1,0
Figure 58. Adsorption isotherms of copper in raw sewage at different VSS
concentrations.
-------
its.
Ul
GO
CO
8
CD
0
0
65 mg/1
0,2 0,4 0,6 ;
TOTAL METAL CONCENTRATION, MG/L;
0,8
Figure 59. Adsorption isotherms of copper in primary effluent at different
VSS concentrations.
-------
Oi
CD
3.
c>o
CO
UJ
C/D
0
0
800 rag/1
1000 mg/1
1500 mg/1
2500 mg/1
2 i\ 6 8
TOTAL METAL CONCENTRATION, MG/L
Figure 60. Adsorption isotherms of copper in mixed liquor at different
VSS concentrations.
10
-------
36
CD
2"
GO
0
0
0 mg/l
ing/1
0,2 0,4 0,6 0,8
TOTAL METAL CONCHNTRATION, MG/L
1,0
Figure 61. Adsorption isotherms of copper in secondary effluent at different
VS3 concentrations.
-------
240
00
CD
^
CO
CO
_J
-------
120
CD
: CD
\
CO
80
ttx
CD
0,0
0,0
15 mg/1
25 mg/1
0,8 1,2 1,6
TOTAL METAL CONCENTRATION, MG/L
40 mg/1
2,0
Figure 63. Adsorption isotherms for iron in primary effluent at different
VSS concentrations.
-------
120
ID
GO
GO
M «=C
Oi t
O UJ
80
0,0
0,0
rag /I
0,8
1.2
25 mg/1
1,6
2,0
TOTAL METAL CONCENTRATION, MG/L
Figure 64. Adsorption isotherms of iron in secondary effluent at different
VSS concentrations.
-------
Ol
CD
CD
3.
^
GO
GO
LU
cr>
(=>
GO
Q
20 mg/1
30 mg/1
0,08 0,16 0,24 0,32
TOTAL METAL CONCENTRATION, MG/L
0,40
Figure 65. Adsorption isotherms for lead in raw sewage at different
VSS concentrations.
-------
M
en
to
o
CO
co
^
«c
UJ
to
12
8
0
0,0
0,03
25 mg/1
0,16
0,32
0,40
TOTAL METAL CONCENTRATION, MG/L
Figure 66. Adsorption isotherms for lead in primary effluent at 25 mg/1
VSS concentration.
-------
M
o»
CO
tu
oo
oo
cC
00
12
8
0
0,0
10' mg/1
25 mg/1
0,04 0,08 0,12 0,16
TOTAL METAL CONCENTRATION, MG/L
0,20
Figure 67. Adsorption isotherms for lead in secondary effluent at different
VSS concentrations.
-------
01
120 i-
a 80
CO
CO
^
-------
50 r-
tn
cn
ID
s:
o
GO
CO
20 L-
CO
ca
CO
0
0
0,32 0,6'i 0,96 1,23
TOTAL METAL CONCENTRATION, MG/L
1,60
Figure G9. Adsorption isotherms of nickel in primary effluent at different
VSS concentrations.
-------
en
CD
GO
C/5
16 -
8 -
00
0
8
2500 mg/1
12
16
20
TOTAL METAL CONCENTRATION, MG/L
Figure 70. Adsorption isotherms of nickel in mixed liquor at different
V8S concentrations.
-------
Ol
120 r-
\
CO
CO
80 -
CO
0
0
10 mg/1
0.3 1,2 1,6
TOTAL HETAL CONCENTRATION, MG/L
2,0
Figure 71. Adsorption isotherms of nickel in secondary effluent at different
VSS concentrations.
-------
en
oo
60
ea
e>
* t\Q
CO
20
CD
CO
0
0
0,8
1,2
TOTAL METAL CONCENTRATION, MG/L
30 mg/1
1,6
Figure 72. Adsorption isotherms of zinc in raw sewage at different VSS
concentrations.
-------
CO
>_*
3 32 -
oo
00
16 -
C3
0
0
0,4 0,3 1,2 1,6
TOTAL METAL CONCENTRATION, MG/L
2,0
Figure 73. Adsorption isotherms of zinc in primary effluent at different
VSS concentrations. .
-------
ei
^
co
i
i
-------
56 ,-
o
00
CO
CD
mg/1
0
0.2 0,1 0,6 0,8
TOTAL METAL CONCENTRATION, MG/L
Figure 75. Adsorption isotherms of zinc in secondary effluent" at different
VSS concentrations.. ;
1,0
-------
basis to predict the distribution of any of the eight metals,
within any of the four process streams, as a function of
total metal concentration and VSS concentration. The general
equation of any of the lines shown in these figures is,
CSM/VSS = m x CTM
Where, CgM/VSS is sludge metal/VSS, yg/mg CTM is
total metal concentration, mg/1 m is the slope of
the line.
The slope of each isotherm is an inverse function of VSS,
since slope increases as VSS decreases. Analysis of the
slopes, for each metal and each process liquid, revealed
that the relationship between m and VSS is linear, and takes
the form
m = 1/(A x VSS + B)
Where A and B are constants for each metal and
process liquid.
These relationships have not heretofore been presented in
the published literature in.metals distribution in combined
treatment systems, and are a unique contribution of this
study. These relationships provide the basis for the devel-
opment of a predictive model on metals distribution, as
described in Section 8 of this report.
162
-------
SECTION 8
MODEL DEVELOPMENT
This section of the report describes two predictive
models for distribution of metals in the process streams,
and presents a model for metals removal through the combined
treatment plant. The process models, built up from material
balance equations through, the plant., predict the effluent
.heavy metals concentrations, based upon influent metals
concentrations and the operating conditions of the plant,
such as per cent VSS removal and per cent SOC biodegradation.
The predictive process models are checked against the
measured data of the .39 continuously run pilot-scale units.
In Part.I, the. distribution relationship between solid
and liquid phases for the heavy metals is reviewed, and
'models based upon regression equations obtained describing
the correlation of the heavy metals concentrations between
the liquid and solid phas.es, with the sampled liquors taken
.from the process stream of the pilot-scale units. The
regression models can predict metal concentration of either
the solid or liquid phase.
In Part II, a predictive process model for metals
removal through the primary clarifier is first developed.
Two regression models developed in Part I are used to pre-
dict the solid bound metal concentration from the total
metal concentration. Part II also demonstrates a predictive
process model for the continuously run pilot systems. For
the process model, both of the predictive regression models
of Part I were tested. The final section of Part II concerns
the heavy metal removal percentage, predicted by the process
model developed in Part II.
PREDICTION OF METALS DISTRIBUTION
In order to develop a predictive process model, it is
necessary first to predict the metals distribution between
the solid and liquid phases for the process streams. After
evaluation of the experimental data on metals distribution,
regression models are developed to predict the metals
163
-------
distribution. The predictive models have been checked for
their prediction errors, against the data collected from the
pilot-scale system.
The distribution is described in terms of the correla-
tion, among the total metals concentration GCTM)> tne solid
(or sludge) bound metal concentration (CSM), and the soluble
metal concentration (Cso)- The distribution may be influ-
enced by soluble ligands, such as SOC, or proton concentration
Proton concentration was measured as pH, where
pH = - log Cff
Freundlich type isotherms do not incorporate information
about soluble metal-ligand complexation effects, since the
isotherm is the correlation between Csu/VSS and CSQ- As
is seen in Figures 42 through 45, most of the isotherms show
low correlation coefficients. This may be due in part to
the existence of soluble, ligands, which affect the metal
distribution.
To describe the ligand effect, Cheng (1973) proposed a
chemical equilibrium absorption model. His model was based
upon the liquid phase chemical equilibrium between the
soluble ligands (he used COD and pH, and successfully
correlated the liquid phase data) concentration and the
metal concentration in the soluble phase. Using his model,
the researchers tested for a correlation between
Cgo x (VSS/CSM) and SOC.
That is Cgo x (VSS/CgM) = l/Kg + (KL/Kg) x SOC
where Kg = CgM/(VSS x CM) '
KL = CML/(SOC x CM>
CM is noncomplexed soluble metal concentration, and CM. is
the soluble complexed metal concentration. This predictive
model for the metals distribution was tested by the linear
regression technique against the 39 runs of data. The
results of the regression analysis are listed in Table 42.
Very low squared correlation coefficients were obtained,
implying poor prediction of any impact of soluble ligands by
Cheng's model. Therefore, different equilibrium models were
tested, some of which also incorporated soluble and solid
phase ligands (SOC, CH and VSS). The investigators assumed
a linear combination, and tested three separate models of
increasing simplicity as follows:
164
-------
TABLE 42. RESULTS OF REGRESSION ANALYSIS FOR EFFECT
OF SOC ON METALS DISTRIBUTION
. . -Me.ta-1
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Squared
Raw
. Sewage .
"0.31
0.25
0,33
0.18
0 . 43
0.14
0.17
0.48
Correlation
Primary
; Effluent
" 0.13
0.26
0.44
OV21
0.40
0 . 40
0.05
0.29
Coefficient
Mixed
Liquor
0.01
0.00
0.07
0.23
0.43
0.28
0.06
0.24
Secondary
Effluent
0.02
0.17
0.00
0.18
0.01
0.04
0.11
0.00
165
-------
CTM x (VSS/CSM) = A x VSS + B x SOC + C x CH + D Model 1
CTM x (VSS/CgM) = A x VSS + B x SOC + C Model 2
CTM x (VSS/CSM) = A x VSS + B Model 3
The latter, Model 3, was that postulated on the basis of the
data presented in Section 7, Figures 52 through 75, and
represents the most simple form of a model to predict the
distribution of CTM between the soluble and solid phases.
Model 3 results through a rearrangement of the equations
presented at the end of Section 7, which described the
relationship of Figures 52 through 75.
CSM/VSS = m x CTM
m = 1/(A x VSS + B)
For Model 3, only CTM and VSS would be required, to determine
CSM/VSS, the sludge bound metal concentration per unit
weight of VSS. Since, in the model calculation VSS is
given, then CgM can ^>e determined and by difference between
CSM and CTM, GSO is also determined. . The model parameters;
A, B, C and D were computed by multivariant linear regres-
sion and the computed models then tested against the perform-
ance data for the 39 pilot runs . For each model, and each
metal in each of the four process liquids, the means of the
relative errors and the mean relative standard deviations
for each model were calculated. The means of the relative
errors are summarized in Table 43, and the mean relative
standard deviations of the predicted from the measured
conditions are presented in Table 44. As demonstrated in
Table 43, there is surprisingly good fit by all three models,
with little difference in mean relative error among any of
the models for any metal in any process liquid, except that
for zinc in raw sewage the simple Model 3 gave a greater
prediction error than did Models 1 or 2. Table 44 reveals
that, again except for zinc in raw sewage, the best model
for fit to the experimental data is Model 3, the most simple
model. For Model 3, the relative standard deviations are
all below 20%, and next are below 10%. This indicates
extremely good fit, and Model 3 was therefore selected as
the model of choice among the three tested, in predicting
metals distribution within the process liquids.
A fourth model, incorporating only SOC was also tested.
This model takes the form CTM x (VSS/CgM) -Ax SOC + B.
There was essentially no correlation between CTM x (VSS/CgM)
166
-------
TABLE 43. MEAN RELATIVE ERRORS OF PREDICTION OF
MODELS 1, 2 AND 3 AGAINST MEASURED DATA, %
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Raw
Sewage
12.0
12.6
1.3
4.0
6.3
24.8
21.5
"" 9.8
11.4
12.3
1.4
3.7
6.6
23.2
20.7
9.6
13.6
12.4
1.3
4.0
4.2
26.5
20.9
21.5
Process
Primary
Effluent
14.2
12.6
1.0
2.1
3.7
14.3
, 27.3 .-.
10.5
Model
13.5
15.0
1.1
2.2
3.7 ,
13.1
29.2
9.9
Model
16.0
15.0
1.3
2.9
4.0
16.7
30.8
10.6
Liquid
Mixed
Liquor
0.6
1.8
0.0
0.2
1.1 .
1.5
.. 2.9
0 . 5
2
0.6
2.0
0.0
0,2
. 1.1
1.5
3.0
0.6
3
0.6
2.0
0.1
0.2
1.6
2.0
3.4
0.7
Secondary
Effluent
16.8
18.6
1.0
3.3
7.1 -
20.1
24.6
15.3
17.9
24.0
1.0
3.6
7.1
20.1
22.2
18.7
17.2
24.7
1.2
4.5
6.9
25.8
22.6
19.1
167
-------
TABLE 44. MEAN RELATIVE STANDARD DEVIATIONS OF
PREDICTIONS OF MODELS 1, 2 AND 3
AGAINST MEASURED DATA, %
Process Liquid
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Raw
Sewage
22.3
17.6
2.4
6.2
11.7
36.7
36.0
15.8
20.5
16.9
2.4
5.6
11.7
24.9
30.9
15.4
9.9
9.2
1.3
3.1
3.3
13.4
13.7
18.3
Primary
Effluent
21.6
18.3
1.7
3.6 .
6.1
20.9
38.3
15.7
Model
20.8
21.2
1.7
3.7
6.0
20.5
37.6
14.5
Model
14.3
9.8
0.8
2.2
3.7
8.3
20.2
7.5
Mixed
Liquor
0.9
3.1
0.2
0.3
1.9
2.6
5.1
0.8
2
1.0
3.3
0.2
0.3
1.9
2.6
5.3
1.0
3
0.5
1.5
0.0
0.3
1.6
1.9
3.5
0.6
Secondary
Effluent
22.7
28.7
1.1
5.2
11.7 .
31.4
37.1
21.9
23.8
32.9
1.6
5.4
11.5
31.4
35.4
26.0
10.5
15.0
0.8
3.8
8.0
16.3
19.1
12.6
168
-------
and SOC. The distribution of metals is therefore indicated
to be primarily influenced by the solid phase, VSS. Metals
equilibrium distribution through the plant is controlled by
the solid or sludge phase, rather than by the liquid phase.
The liquid phase ligands SOC and pH have scant effect on the
metals distribution. VSS, the solid phase ligand, is revealed
to be the dominant factor.
As reported in Section 7, the slopes of Figures 52
through 75 are obviously inversely proportional to the VSS
values. Model 3, CTM x (VSS/CsM) = A x VSS + B corresponds
to the relationships observed in Figures 52 through 75.
Table 45 presents the Model 3 parameters A and B, and
Table 46 the squared.correlation coefficients of the simpli-
fied linear Model 3. The high squared.correlation coefficients
of Table 46 indicate that Model 3 is extremely accurate,
particularly in predicting the metals distribution in raw
sewage and mixed liquor. Poorest correlation in raw sewage',
although still quite good, is observed for. nickel and zinc.
Raw sewage correlation coefficients for all other metals
exceed 0.95. Correlation coefficients for all metals in
mixed liquor exceed 0.98, ..reflecting the predominance of
the sludge bound metal in that process liquid.
As would be expected, the correlation coefficients for
primary effluent and secondary effluent are somewhat lower
than for raw sewage, since.in these two effluents, the
distribution is at least in part influenced by. the efficiency
of clarifier suspended solids (and associated solids-bound
metals) removal.
The results of these evaluations, for the several
models considered, are that the simple Model 3 provides best
prediction of the distribution of all metals in all process
streams, and the fit of Model 3 to the observed data is
excellent, as indicated by the regression analysis correlation
coefficients. The fit of data to Model 3 is also illustrated
in the computer-generated graphs contained in Figures B.I
through B.8 of Appendix B.
One aspect of the distribution behavior pattern described
by Model 3, and demonstrated in Figures 52 through 75, is
that at any fixed value of CTM, metal concentration per unit
weight of sludge increases as total VSS decreases. For.
example, considering cadmium in raw sewage at a C^jj of
0.2 mg/1, the values of Csn/VSS at VSS levels of 25, 15 .and
5 mg/1 are 7, 11 and 38 yg/mg, respectively. This pattern
suggests that same factor controls or establishes the maximum
possible soluble metal level, and the excess metal above
that maximum is "driven" onto the VSS present.
169
-------
TABLE 45. REGRESSION CONSTANTS FOR METALS DISTRIBUTION
MODEL 3
Process Liquid
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Constant
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Raw
Sewage
1.23
-0.58
1.34
-1.37
1.05
-0.70
1.06
0.67
1.17
-2.59
1.34
2.93
1.52
-2.15
1.09
7.34
Primary
Effluent
0..96
11.23
1.24
2.50
1.03
0.01
1.08
-0.71
1.11
... -0.28
1.50
-6.13
1.94
-1.37
1.16
1.42
Mixed
Liquor
1.00
11.19
1.05
-17.02
1.00
-0.01
1.00
4.88
0.99
24.07
1.00
' 21.19
1.00
92.69
1.00
16.62
Secondary
Effluent
1.09
3.26
1.08
6.45
1.02
0.15
1.02
1.02
1.02
0.84
1.70
-1.18
2.69
-13.77
0.90
5.06
170
-------
TABLE 46. SQUARED CORRELATION COEFFICIENTS FOR
METALS: DISTRIBUTION MODEL 3 :
Process Liquid
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
.- Raw
.Sewage
0.959
0 . 970
0.999
0.996
0.989
0.877 "
0.803
0.953
Primary
Effluent
0.749
0.837 -
0.999
0.989
0.984
-0.840 '
-.0.560
0.914
Mixed
Liquor
0.999
0.997
0.999
0.999
0.998
0.997
0.986
0.999
Secondary
Effluent
0.852
0.720
0.999
0.992
.. 0.949
0.826
0.909
0.814
171
-------
Model 3 correlates CTM and CSM as a function of VSS
concentration. At high values of VSS, the term (A x VSS +
B)/VSS is essentially constant, and another correlation is
feasible. That is the direct correlation between CTM and
CSM, without VSS.
9.
(CTM = PCSM + *)
The relationships between CTM and ^SM as described in
Model 4 are shown in Figures B.9 through B.16 of Appendix B.
At high VSS values, where CTM or CSM also have high values,
the plots demonstrate good linear relationships. In lower
CTM °r CSM domains, particularly where VSS is also low,
there is more scattering, with nonlinear aspects. The
slope, p, and intersection, q, of the linear regression for
Model 4 are listed in Table 47. -The intercept value, q,
represents the residual solubility of the metal in the
system, and at CTM values below this intercept value, all
metal present is predicted to be in solution. At CTM value
in excess of the intercept value, the slope, p, represents
the distribution of the increment in total metal between the
sludge and soluble phases. The intercept, value for each
metal across all four process' liquids remains essentially
constant, indicating little or no change in the soluble
concentration of each metal from raw sewage to secondary
effluent. These patterns were also noted in the averaged
performance of the 39 runs as summarized in Table 36, and in
fact the q values are extremely close to the average soluble
metals concentrations noted in Table 36.
Table 48 lists the squared correlation coefficients for
Model 4, as tested against the pilot data. The squared
correlation coefficients, r , are all very close to a value
of unity. The lowest value, obtained for nickel in primary
effluent is r2 = 0.90885 (r = 0.95334). The highest value
is r2 = 0.99999, for chromium in mixed liquor. This simplified
model must be employed with caution, and only within the
range of CTM and VSS values for which the experimental data
apply. At CTM values exceeding the maximum values indicated
on Figures B.9 through B.16 of Appendix B, the metals for
which the slope, p, in Table 47 is less than 1.0 could be
predicted to have values of CSM exceeding CTM- This-condition
obviously cannot occur.
In applying Model 4, for the overall process model
development, it is necessary to calculate CSM from given
values of CTM- Therefore, Model 4 has been rearranged as
shown below, and Table 49 presents the calculated values of
p' and q' for Model 4'.
172
-------
TABLE 47. REGRESSION CONSTANTS FOR METALS DISTRIBUTION
MODEL 4:
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Constant
P
q
P
q
P
q
P .,
q
P
q ...
P
q
P
q
P
q
Raw
Sewage
0.953
0.107
1.045
0.011
1.002
0.004
1.016
0.012
0.997
0.173
1.036
0.015
1.033
0.276
0.928
0.137
Process
Primary
Effluent
0.890
0.122
1.089
0.009
1.004
0.003
1.018
0.007
0.992
0.107
1.024
0.013
1.090
0.245
0.961
0.096
Liquid
Mixed
Liquor
1.003
0,038
1.035
0.001
1.001
0.002
1 . 001
0.009
0.999
0.106
1.007
0.010
1.019
0.172
0.997
0.108
Secondary
Effluent
0.955
0.100
.1.022
0.012
1.007
0.003
1.001
0.012
0.945
0.108
1.137
0.011
1.300
0.106
0.943
0.090
Units of q are mg/1.
173
-------
TABLE 48. SQUARED CORRELATION COEFFICIENTS FOR
METALS DISTRIBUTION MODEL 4
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Raw
Sewage
0.975
0.963
0.999
0.998
0.993
0.976
0.964
0.981
Process
Primary
Effluent
0.961
0.944
0.999
0.998
0.985
0.981
0.909
0.992
Liquid
Mixed
Liquor
0.999
0.994
0 . 999
0.999
0.999
0.999
0.999
0.999
Secondary
Effluent
0.961
0.947
0.999
0.998
0.983
0.953
0.913
0.988
174
-------
TABLE 49. REGRESSION CONSTANTS FOR METALS DISTRIBUTION
MODEL 4
Process Liquid
Metal
Aluminum
Cadmium.
Chromium
Copper
Iron
Lead
Nickel
Zinc
Constant
P^
q
' ' .*
q
. . . P
-*
q
P^
q
P^
q
p'
q
*V
q
p/
q
Raw
Sewage
1.023
-0.096
0.921
-0.007
0.998
-0.004
0.982
-0.011
1.027
-0. 166
0.942
-0.012
0.934
-0.220
1,057
-0.132
Primary
Effluent
1.079
' -0.117
0.875
-0.005
0.996
-0.003
0.980
-0.007
0.993
-0.088
0.959
-0.011
0 . 834
-0.157
1.032
-0.094
Mixed
Liquor
0.997
-0.037
0.961-
0.001
0.998
-0.002
0.999
-0.009
1.001
-0.106
0.993
-0.009
0.981
-0 . 164
1.002
-0.107
Secondary
Effluent
1.006
-0.086
0.926
-0.009
0.993
-0.003
0.987
-0.012
1.040
-0.095
0.838
-0.007
0.705
-0.033
1.048
-0.089
175
-------
CSM = P' x CTM + *' Model 4'
In summary, the correlation of Model 3, between G.JJJ x
and VSS, though simple, is a good predictive model
for metals distribution between the solid and liquid phases
in the process liquors. The relative prediction error of
the model ranges from 1 to 26% and averages 13% for raw
sewage, 0 to 7% and averages 1% for mixed liquor, 1 to 30%
and averages 12% for primary effluent and 1 to 25% and
averages 15% for secondary effluent. Good prediction is
also possible using Model 4, involving a direct correlation
between CTM and CSM. as long as conditions fall within the
range of the experimental data base, and high values of VSS
are present.
PROCESS MODELS
Process models are developed here to predict the removal
of heavy metals through the complete combined treatment
system. Thfe predictive process models are checked against
the measured data of the 39 continuously run pilot-scale
plants. The process models, built upon material balance
equations through the plant, are intended to predict the
effluent heavy metals concentrations from the given inflow
heavy metal concentration and from the operating conditions
of the plant. A prediction of the percentages of influent
heavy metals contained in the primary clarifier sludge and
the secondary clarifier sludge can also be performed, as
part of the application of the predictive process model.
The model continuous treatment plant is illustrated in
Figure 76. Symbolic codes and nomenclature are also included
in Figure 76.
Predictive, Process Model for Primary Effluent Metal Concentra-
Based Upon the Raw Sewage Condition
Based on the metals and TSS balances around the primary
clarifier, a predictive process model, Model PW, has been
developed. Model PW predicts the total metals concentration,
CpTM. of this primary effluent, from the influent total metal
concentration (CRTM)> and the operating condition of the
primary clarifier. The operating condition utilized is the
efficiency, Zp, of VSS removed through the primary clarifier.
176
-------
O: flow rate
(RO)
X,
^
^ .-
. N
L .. .*
\
I D
(P
>
v i /"*
1
/ (P E)i
C /r !
/ y !
/ AP !
i
C" \
S)
Cnc
" \LL)
AT
A 1
V: volume
Y : yield factor
f R: recvcle ratio
"**
(ML) X
>
\
1
y
i
c
>
c
(SS)XSS
xss
STM
'PSTM
PC = Primary Clarifier RO = Raw Sewage
AT ** Aeration Tank ML = Mixed Liquor
SC = Secondary Clarifier PS = Primary Sludge
PE = Primary Effluent
SE = Secondary Effluent
SS = Secondary Sludge
Fitrure 76. Schematic of continuous flow combined treatment system.
-------
ZP " + t1 - W>
Appendix C presents the derivation of Model PW. Tables 50
and 51 present the mean predicted errors and relative standard
deviations of Model PW, as based upon Models. 3 and 4',
respectively.
Model PW develops a mass balance for influent, effluent,
and settled sludge components of the primary clarifier. A
more simple approach is possible if only the effluent
conditions are to be predicted, based upon the influent
conditions. The steps in performing this mass balance
prediction around the primary clarifier are as follows:
1. For a given CRTM and Z if using Model 4', or C.RTM>
Z , and raw sewage VSS for Model 3, calculate the
concentration of CRSM. By difference between CRTM
and CRS.., determine CT. "the soluble metal level.
2. For CRgM, calculate CpgM as a function of Z
CPSM = (1 " V CRSM
The predicted total metal in the primary effluent is
then CPSM plufe CSOL' e<*uals CPTM-
178
-------
TABLE 50. MEAN PREDICTION ERROR AND RELATIVE STANDARD
DEVIATION OF MODEL PW AT W = 1.0, BASED ON
MODEL 3
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Prediction
Error
0.214
0.288
0.179
0.241
0.248
0.301
0.229
0.184
. Standard
Deviation
0.139
0.193
0.071
0.114
0.195
0.346
0.168
0.065
Z = .0.277
179
-------
TABLE 51. MEAN PREDICTION ERROR AND RELATIVE
STANDARD DEVIATION OF MODEL PW AT
W = 1.0. BASED ON MODEL 4"
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Prediction.
Error
0.196
0.261
0.185
0.250
0.238
0.255
0.254
0.197
Standard
Deviation
0 . 093
0.134
0.056
0.090
0.153
0.205
0.240
0.044
Z = 0.314
180
-------
Table 52 presents the results of this prediction around
the primary clarifier, using Model 4'. The input data are
the average values of CRTM and Zp, from Table 36. The
predicted values of CPTM are compared with the actual averaged
values and for most of the metals are quite close. Even for
copper and nickel, the predicted values of CPTM were only in
error by about 20%.
Predictive Process Model for Secondary Effluent Metal
Concentration from the Raw Sewage Condition
By performing a material balance of VSS and metal
around Domain II in Figure 76, including SOC biodegradation .
associated with sludge yield in .the aeration; tank, the
predictive process model for the full-process system,
Model FS, is developed. This model is fully derived in
Appendix C. When: the researchers utilize the semi-empirical,
'correlation of Model 4',-, as demonstrated in Appendix C, then,
cpred. = rcpred. 1
LSTM q
is the derived predictive equation of the process model.
D r e d .'- ' '
CSTM '» the Secondar7. effluent total metal, is the target of
m**p*ri ' ' ' ' ,--.''
the prediction. . C£TM " 'is the Predicted CPTM for the Siven
CRTM. through Model PW, developed above. , J is defined as,
J = (Xp - X0 + Y (1 + R)(SR - SQ) - kd x X x V/S)/XQ
J is related to the sludge generation in the aeration tanks,
where Xn> Xo an<* X are VSS values for primary effluent, secondary
effluent and mixed liquor (see Figure 76). SR and So are the
SOC values for raw sewage and secondary effluent. V and Q
are the volume of the aeration tank, and the influent flow
rate. The constant, k^, is the endogenous reaction constant. .
Y is the yield factor, with substrate expressed as SOC,
The computed means of the relative prediction errors,
and the relative standard deviations on CSTM with Model FS are
shown in' Table 53 for Model 3, and Table 54 for Model 4'. The
fit of the Model FS, based upon Models 3 and 4', is quite
good.
181
-------
TABLE 52. APPLICATION OF MODEL PW TO AVERAGED PRIMARY
CLARIFIES PERFORMANCE FOR 39 RUNS
Measured
Metal CRTM(1)
H-
00
to
Aluminum
Cadmium
Chromium
Copper
Iron
Le-ad
Nickel
Zinc
0.
0.
0.
0.
1.
'.o-.
1.
0.
652
080
241
303
778
142
064
657
Calculated Values^
CRSM
0.
0.
0.
0.
1.
0.
0.
0.
571
067
236
287
660
122
774
562
CSOL
0.081
0.013
0.004
0.016
0.118
0.020
0.290
0.095
r (2)
PTM
0.
0.
0.
0.
1.
0.
0,
0.
471
059
165
212
332
117
819
479
Measured
r (1)
PTM
0.
0.
0.
0.
1.
0.
. 0.
0.
478
062
170
268
247
100
674
548
Present
Error
- 1.5
- 4.8
- 2.9
-20.9
6.8
17.0
21.5
-12.6
(1) Values taken from Table 36
(2) Based upon average per cent VSS removal in primary
clarifier of 31.7%
(3) Calculations based upon Model 4
-------
Prediction of Heavy Metal Removal Through the Combined
Treatment System
As the final objective of this investigation, the metal
removal percentage from the primary and secondary sludge is
predicted by the process Model FS, and compared with the
measured removal percentage, based on the pilot-scale data.
By utilizing the PW model, the heavy metal removal rate,
Hps = XPS x Q x (XR ~ Xp)» in mf of metal/hour is determined
for the primary sludge. Regarding the secondary sludge, the
following relationship is used.
HSS = XSS(Q{0(P - X0> + Y(1 + R) (SR - So)} ' kd*V) ' ' - "
The.secondary, effluent rate-is HQ = COTM x Q-
Xpg and xss are predicted as
XPS = W.+ .a-^> (CpSM/xp)
XSS = CSSM//'xo . . .
-". " .CRSM'.CPSM andCSSM are P^dicted by CRTM, C' and
through either Model 3 or 4 . '
From the predicted values of heavy metal content in the
sludge XPS an<* XSS> ^PS» Hg§ and HQ can be calculated, and
therefore, the percentages:
% PS = (Hps/HT) x 100
% ss = (HSS/HT) x 100
% SE = (HQ/HT) x 100
where HT = Hpg + HSS + HQ
Tables 55 and 56 present the predicted and measured per-
formance, for W = 1.0, k(j = 0.0, b.ased upon Model 3 and Model 4",
respectively. Further, as shown in Tables 55 and 56, the
predicted performance is quite close to the measured perform-
ance, for most of the metals.
183
-------
TABLE 53. MEAN PREDICTION ERROR AND RELATIVE
STANDARD DEVIATION OF MODEL FS AT
W = 1.0. BASED ON MODEL 3
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Prediction
Error
0.340
0.310
0.400
0.346
0.402
0.337
0.375
0.261
Standard
Deviation
0.167
0.195
0.195
0.239
0.175
0.275
0.243
0.172
Note: kd = 0.0, Y = 0.438, Z =0.485
134
-------
TABLE 54. MEAN PREDICTION ERROR AND RELATIVE
STANDARD DEVIATION OF MODEL FS AT
W = 1.0. BASED ON MODEL 4"
Metal
Aluminum. - ,
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Prediction
Error
.-.: .- -0..303.
0.272 . .
0.412
0.326
0.365
0..309
0,382
0.231
Standard
Deviation
-. 0.178
0.201
0.192
0.244
0.177
0.295
0.240
0.160
Note: k. = 0.0, Y = 0.238,
Q .
= 0.485
185
-------
TABLE 55. FULL SYSTEJM PREDICTED AND MEASURED METALS
DISTRIBUTION, BASED UPON DISTRIBUTION
MODEL 3
Met til
Alumihum
Cadmitun
Chromium
Coppei:
Iron
Lead
Nickel
Zinc
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Primary
Sludge
18.1
22.9
16.7
21.5
32.2
27.3
11.1
25.7
32.3
26.0
29.5
19.2
38.2
19.6
14.5
21.0
Per Cent
Secondary
Sludge
22.2
21.6
40.8
21.5
14.5
22.9
37.1
24.7
16.1 .
24.3
30.5
21.0
27.7
20.4
24.0
22.7
Metal In
Secondary
Effluent
59.7
55.5
42.5
37.0
52.2
49.8
48.8
49.7
51.6
49.7
40.0
59.7
34.1
60.0
61.4
56.3
Note: Results for all metals based only upon runs
yielding net metals removals from PE to SE.
186
-------
TABLE 56. FULL SYSTEM PREDICTED AND MEASURED METALS
DISTRIBUTION, BASED UPON DISTRIBUTION
MODEL 4'
Per Cent Metal In
Metal
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Measured
Predicted
Primary
Sludge
18.1
27.4
16.7
26.4
33.2
31.0
14.1
29.8
32.3
29.6
29.5
24.0
38.2
25.1
14.5
24.3
Secondary
Sludge
22.2
21.7
40.8
23.4
14.5
24.0
37.1
24.8
16.1
24.9
30.5
22.5
27.7
21.7
24.0
23.0
Secondary
Effluent
59.7
50.9
42.5
50.1
52.2
45.0
48.8
45.4
51.6
45.5
40.0
50.5
34.1
53.2
61.4
49.8
Note: Results for all metals based only upon runs
yielding net metals removals from PE to SE.
187
-------
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195
-------
APPENDIX A
SUMMARY TABLES OF AVERAGE OPERATIONAL CHARACTERISTICS OF PILOT
ACTIVATED SLUDGE SYSTEMSTREATMENT NOS. 1 THROUGH 39.
196
-------
TABLE A-l. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-l
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw Primary
Sewage Effluent
7.58
116
82
35.7
336
" 154
2.5
783
49
25
...3. .
135
3
393
19
1265
135
81
9
672
173
482
80
7.61
84
66
28.3
737
32
22
3
118
2
' 353
12
1192
134
58
30
633
176
450
56
Mixed
Liauor
7.88
1581
.1176
17.7
4704
30
336
. .3.
1005
2
3621
16
2047
74
497
14
3072
258
5510
87
Second.
Effluent
8.28
20
12
11.7
355
162
2.4
340
55
20
5
99
2
165
22
1229
32
36
11
595
241
370
89
Primary
Sludge
7.
12
11045
9467.
7.
0.
1.
4.
39.
1.
16.
48.
89
60
07
89
17
51
78
22
Second.
Sludce
7.71
8599
6389
. .
17.68
0.52
2.33
6.88
57.20
2.58
18.93
22.46
All. metal concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
197
-------
TABLE A- 2. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 2
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.20
36
26
39.1
222
145
0.8
4.5
433
87
42
6
143
2
359
9
1542
231
93
11
756
341
413
118
Primary
Effluent
7.62
54
41
22.6
360
75
35
5
122
2
337
7
1126
125
44
8
454
263
367
161
Mixed
Liquor
7.99
1762
1132
13.4
3537
69
572
6
1526
2
2820
11
32170
46
1702
15
3403
316
11075
153
Second.
Effluent
8.23
17
11
8.7
203
132
0.9
0.3
357
.120
30
7
"" 131
2
' ' 268
11.
1489
21
23
9
1089
278
312
162
Primary
Sludge
7.06
2156
1513
25.82
1.52
1.75 .
5.14
53.80
3.05
11.84
29.92
Second.
Sludge
7.98
5802
3825
17.88
1.36
1.33
5.06
36.70
2.80
7.15
18.09
All metals concentrations in micrograms/1 except for primary and secondary
sludges wher« concentrations are in tag/1.
198
-------
TABLE A- 3. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 3
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Ch lo r i de , mg/1
Sulfate, mg/1
Phosphate., mg/1
Ammonia-N, mg/1
Aluminum. Total .
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
" Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.09
178
128
75.3
169
133
0.4
' 3.9
1003
155
.140
23'
174
5
274
26
1750
244
293
47
1629
315
1114
66
Primary
Effluent
7.44
56
37
28.9
819
131
96
' ""30 '
137
5
268
10
1650
129
264
62
1413
' 332
571
. 63
Mixed
Liquor
7.77
2540
1786
20.6
19339
125
. .468
25
1514
5
4586
16
50214
62
2814
37
7307
333
-18983
.. -52
Second.
Effluent
8.27
16
10
15.3
142
128
0.4 .
0.2
425
178
41
28
116
5
164
21
886
23
79
22
1337
260
375
39
Primary
Sludge
6.89
811
18
27.61
0.58
1.69
5.36
59.79
3.89
7.88
19.71
Second.
Sludae
7.90
5292
3695
1.32
0.70
1.55
.2.97
6.60
1.16
1.48
2.14
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
199
-------
TABLE A- 4. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-4 L . . ..
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.26
44
31
5.0
-
-
-
1310
19
80
6
630
5
280
7
1460
51
140
10
2740
226
826
15
Primary
Effluent
7.39
55
39
6.3
938
28
78
12
490"
5
240 -
7
1050
64
90
7
596
196
744
14
Mixed
Liauor
7.81
2175
1445
5.4
15440
26
544
5
2720
5
' 5860 .
10
56000
17
2960
8
16780
174
27750
9
Second.
Effluent
7.93
51
25
3.4
50
69 "
0.4
0.5
890
28
35
8
'"' 400
5
250
12
. .1175
37
40
5
666
146
754
5
Primary
Sludge
6.86
21765
8610
23.60
0.68
3.35
5.84
104.80
6.64
22.18
66.17
Second.
Sludge
7.43
11110
7075
24.44
0.62
3.16
5.72
77.10
5.78
19.28
43.96
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are1 in mg/1.
200
-------
TABLE A- 5. SUMMARY OP AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 5
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC , mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, mg/1 .
Raw
Sewacje
7.65
111
98
29.2
336
154
-2.5
.-...- .
Primary
Effluent
7.63
81
58
.28.1
....
Mixed
Liquor
7.94
1315
934
18.4
-
Second.
Effluent
8.31
18
12
12.8
352
155-
2.6
.
Primary
Sludge
6.87
12561
10745
Second.
Sludge
7.86
5599
4299
Aluminum
. Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
678 .
39
' 12
3
1-1-3 .
3
90
12
1399
114
35
27
334
52
409
62
515
39
"12
3
88
2
78
10
1231
111
- 32
20
244
38
350
38
2455 .
31
58
4
. 433. .
2
624
10
193.4
114
233
22
314
26
2145
39
334 .
35
10..
3
97
2
73
11
1223
81
29
9
151
22
282
51
6.90
0.45
0.29
1.94
45.17
0.98
1.61
16.20
12 -.31
0.39
0,97
2.36
46.83
1.19
0.88
13.95
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
201
-------
TABLE A-6. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-6
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
Sulfate,
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
mg/1
mg/1
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.46
42
29
33.7
222
145
0.8
4.5
298
76
124
6
84
2
161
10
1247
103
37
8
369
81
383
153
Primary
Effluent
7.60
46
34
28.6
279
68
53
6
82
2
235
9
1207
107
52
18
308
143
332
159
Mixed
Liquor
7.92
1460
981
14.5
5828
62
249
6
719
2
1859
10
'24348
56
1013
6
1150
98
5600
149
Second.
Effluent
8.34
19
12
10.4,
206
140
1.1
0.3.
287
74
19
5
78
2,
161
10 '"
1138
55
27
8
368 .
97
264
122
Primary
Sludae
7
.01
3045
2279
21
0
1
5
53
3
6
16
.09
.65
.39
.17
.06
.21
.98
.25
Second.
Sludae
7.96
5782
4194
17.19
0.45
0.82
4.64
34.15
3.49
4.99
19.53
All metals concentrations in micrograms/1 except for primary'and secondary
sludges where concentrations are in mg/1.
202
-------
TABLE A-7. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-7
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.27
61
52
36.0
169
133
' -0'. 4
3.9
375
194
63
34
150'
. 5
177
16
1292
98
75
37
1780
79
481
64
Primary
Effluent
7.42
41
32
30.1
311
176
44
32
112
5
159
.13
1080
125
50
33
970
26
. 420
59
Mixed
Liquor
7.82
1943
1215
21.1
12215
. 110
500
30
1392
5
2300
14
28667
109
1675
55
2263
22
5875
39
Second.
Effluent
8.28
16
11
18.1
158
133
0.4
" 0.2
657
162
44
23
130
5
92
12
1208
55
50
30
366
11
292
31
Primary
Sludae
6.47
605
12
26.12
0.51
1.60
2.69
31.00
1.50
1.20
9.88
Second.
Sludge
7.97
4268
2695
-.-.-
10.04
1.10
1.49
:'1.64
4.75
0.64
0.68
3.60
.All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
203
-------
TABLE A-8. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-8
Parameter1
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.32
30
18
14.3
110
108
0'.2
1.8
372
105
143
25
128
5
530
46
1610
52
320
16
1220
497
830
85
Primary
Effluent
7.64
31
20
9.6
303
106
132
11
122
5
254
18
1170
70
210
16
420
420
440
100
Mixed Second.
Liquor Effluent
7.68
2086
1425
11.8
3037
80
502
15
1250
5
4100
14
20000
52
3440
18
1663
331
8675
65
8.23
23
17
10.5
94
123
0.5 "
0.3
327
147
70
19
110
5
290
15
850
79
140
10
472
236
420
32
Primary
Sludge
6.
80
3748
2510
26.
0.
1.
3.
20.
3.
7.
8.
08
58
31
89
10
02
06
39
Second.
Sludge
7.73
1899
1224
16.74
0.59
1.67
5.58
43.20
4.76
10.71
6.10
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
204
-------
TABLE A-9. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-9
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride ,
mg/1
Raw
Sewage
7.20
40
18
9.1
-
Primary
Effluent
7.34
33
- 19
9.2
Mixed
Liquor
7.72
1153
473
7.2
Sulfate, mg/1 ' -
PH fie r^Vi s + &
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
_ /I
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble.
Total
Soluble
Total
Soluble
Total
Soluble
_
..-
851
25
. . .94..-...:
39
838
5
463
29
4000
44
. 186
9
2788
607
733
91
548
23
- 173
54
488 .
5
588
43
2058
237
175
20
850
595
633
124
5725..
35
' 528
57
2475
5
4775
32
27875
164
1525
5
13375
535
21775
68
Second.
Effluent
8.01
27
14
6.6
80
90
0. 5
0.6
393
45
80
50
375
5
350
32
1155
53
100
35
875
457
713
46
Primary
Sludge
6
.91
8160
5020
23
0
3
5
76
4
17
65
.10
.69
.25
.52
.25
.52
.50
.00
Second .
Sludge
7
.68
4430
2605
46
0
3
5
106
6
15
56
.13
.79
.81
.55
.63
.65
.85
.00
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
205
-------
TABLE A-10. IStiMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-10
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/I
Raw
Sewage
7.37
34
19
5.3
-
Primary
Effluent
7.81
37
24
4.4
Mixed
Liquor
7.76
2603
1650
4.2
Sulfate, mg/1
Phosphate,
Ammonia-N,
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
mg/1
mg/1
Total
Soluble
Total
Soluble
Totdl
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Totcil
Solvible
Totdl
Soluble
-
-
932
42
60
9
600
5
150
12
2675
13
150
6
838
240
1583
9
612
61
... 37
34
375
5
150
9
1000
60
163
7
275
150
744
5
8250
14
488
' 40
... 2600
5
5825
12
41875
39
1450
9
16000
175
19525
6
Second.
Effluent
8.03
37
25
5.1
451
261
0.4
0.5
754
24
53
13
. ...413
5
50
12
963
6
75
7
593
147
1205
7
Primary
Sludge
10.29
21894
15430
26.75
0.60
3.12
5.80
87.88
4.02
21.10
53.07
Second .
Sludge
7.77
6635
4055
18.63
0.59
2.55
5.65
45.00
3.72
20.17
25.12
All metals cdricentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
206
-------
TABLE A-11. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO; A-11
Raw Primary Mixed Second. Primary Second.
Parameter Sewage Effluent Liquor Effluent Sludge Sludge
pH 7.63
TSS, mg/1 101
TVSS, mg/1 86
SOC, mg/1 34.7
Chloride, mg/1 336
Sulfate, mg/1 " ' ' 154
Phosphate, mg/1 ,- 2.-5
Ammonia-N, mg/1
7.65
101
69
29.4
7.91
1546
1134
19.6
8.32
16
.11
13.5
358
162
2.5
6.94
14201
11922,
7.87
7487
5515
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble '
Total
Soluble
Total
Soluble
Total
Soluble
383
.. ' 52 . .
28
. 4-
155
,4
429
18
1439
134
57
21
795
187
510
58
367
37
26
3
131
. .4
373
14
1303
.. 98 .
51
14
719
220
'489
58
4348
,32
238
'' 3 .
1003
. , '" '3-.
2960
,12 ,
1873
69
383
40
2477
205
5067
75
289
43
16
4
87
2 :
108
,14
1287
35
29
32
436
231
320
107
11.66
0.63
1.23.
4.41
39.25
2.12
11.15
34.44
20.34
0.49
1.61
3.53
39.33
1.98
5.70
27.60
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in.mg/1.
207
-------
TABLE A-12. (SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-12
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw Primary
Sewage Effluent
7.22
59
45
47.0
222
145
0.8-
4.5
495
81
77
12
159
2
479
18
1641
225
88
16
1002
531
617
253
7.57
47
46
24.1
397
65
66
7
150
2 ..
437
11
1429
162
73
7
938
515
466
245
Mixed Second.
Liquor Effluent
7,91
1969
1261
12.4
-
3709
79
541
6
'1356
2 ....
2942 .
10 ..
32482
38
2056
6
3531
473
10100
211
8.32
20
10
9.7
211
'143 .
0.6
0.3
273
91
41
9
117
2
279
- 1.1
1317
27
30
12
1215
475
336
204
Primary
Sludae
6.92
4069
2819
21.40
1.14
1.86
5.17
67.36
3.65
12.68
22.12
Second.
Sludae
7.90
4503
2978
30.14
1.20
1.65
4.99
50.28
2.30
8.70
8.84
All metals concentrations in microgr'ams/1 except for primary and secondary
sludges where concentrations are in mg/1.
208
-------
TABLE A- 13. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 13
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
, Sulf ate , mg/1
Phosphate
' Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1 .
, mg/1
Total
Soluble
'Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.07
60
82
43.6
169
'133 ;
, -'0:4'
3. .9
500
84
105'"
22
153
. 5
2.71
14
1521
. 246
158
105
869
537
643
67
Primary
Effluent
7.44
57
38
27.9
. *
".'
323
138
>... , 85 .-
33
103
5
197
10
936
127
79
44
854
577
521
49
Mixed Second.
Liquor Effluent
7.74
3133
2190
18.6
17856 .
. . ...81..
'" '' '479 "
23
-.1571 .
5
3743
. 7
62786
37
2886
30
13467
536
17833
56
8.24
17
11
17.4
145 ,
129 .
0.2
0.2
709
120 ...
49
22
117
5
176
16
779
34
86
40
2183
579
314
39
Primary
Sludge
7
.07
1562
16
0
1
4
60
3
14
12
64
.-67
.53
.58
.56
.67
.01
.56
.67
Second.
Sludge
7.93
2528
1761
17.25
0.57
1.53
3.17
24.60
1.14
8.74
10 ..6 2
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
209
-------
TABLE A-14. IsUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-14
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Totkl
Soluble
Totkl
Soluble
Total
Soluble
Total
Soluble
Totkl
Soluble
Totkl
Soluble
Total
Soluble
Totkl
Soluble
Raw
Sewage
7.29
28
19
12.6
110
108
0.2
1.8
295
90
154
31
122
5
625
25
2220
70
170
75
986
718
553
44
Primary
Effluent
7.63
23
15
9.0
202
77
197
22
112
5
360
17
1962
55
140
45
613
659
563
62
Mixed Second.
Liquor Effluent
7.83
1685
1165
9.0
2604
47
578
24
840
5
3740
13
28200
56
2980
18
3900
661
12440
42
8.25
. 22
16
9.3.
108
.124
, .0..4
0.5
215
91
120
14
120
' " 5 ..
260
11
1210
67
90
. 28
710
532
390
28
Primary
Sludge
6.
84
8770
5367
5.
0.
1.
5.
50.
3.
12.
25.
38
61
12
58
80
72
48
30
Second.
Sludge
7.80
2486
1711
19.32
1.02
1.63
6.80
112.38
5.68
16.80
9.75
- . . . * -.,i . .
All metals concentrations in micrograms/i except for primary and secondary
sludges where concentrations are in mg/1<
Did
-------
TABLE A-15. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-15
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Raw
Sewage
7.29
40
20
9.6
-
Primary
Effluent
7.37
70
25
8.3
Mixed
Liquor
7.78
610
378
5.2
Sulfate, mg/1
Phosphate ,
Ammonia-N,
Aluminum
Cadmium
.
Chromium
Copper
Iron
Lead
Nickel
Zinc
'.
mg/1
mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
-
710
30
93
..16
1062
5
338
9
3360
34
150
5
1220
595
1002
132
416
26
89
. . 17
413
. '5
267
'."'- " 6
1153
35
113
7
740
607
765
133
2782
37
491
17
1425
5
2875
9
25875
81
933
5
7375
610
10425
103
Second.
Effluent
8.04
15
9
5.5
88 .
94
0.4
0.8
477
33
65
17
250
5
200
7'
660
31
88
11
965
532
553
92
Primary
S ludae
6.80
8980
5920
14.97
0.75
2.92
5.75
78.00
2.85
17.20
59.25
Second .
Sludge
7.72
4508
2833
28.13
0.74
3.14
5.70
114.00
3.25
16.20
56.00
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
211
-------
TABLE A- 16. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-16
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride ,
mg/1
Raw
Sewage
7.38
30
19
4.9
-
Primary
Effluent .
7.41
43
28
5.7
Mixed
Liquor
f.59
1408
1255
3,9
Sulfate, mg/1
Phosphate ,
Ammonia-N,
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
mg/1
mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
-
678
25
55
30
460
5
240
5
1534
127
90
6
1615
354
1575
6
662
45
46
12
340
5
170
4
744
62
60
5
626
336
903
9
4480
19
348
12
1600
5
1960
5
31600
21
1040
7
lioso
376
7020
6
Second.
Effluent
8.08
100
25
3.0
46
64
0.3
0.5
479
29
45
6
310
5
100
2
470
9
20
9
470
292
2480
4
Primary
Sludge
7.
04.
18543
13085
14.
0.
2.
4.
92.
2.
20.
32.
32
58
78
76
70
58
42
49
Second.
Sludce
7.78
5970
3770
24.10
0.58
2.90
6.00
110.30
4.18
20.78
43.32
-. ,. ,. _.,,.-.-- -,.. ..
All metals dOricentrations in miorogiramS/l Axcept for primary and secondary
sludges whetfa concentrationa are irt ng/1.
R12
-------
TABLE A-17. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO; A-17
Raw Primary Mixed Second. Primary Second.
Parameter Sewage Effluent Liquor Effluent Sludge Sludcre
PH
TSS, mg/1 111
TVSS, mg/1 104
SOC, mg/1 29.2
Chloride, mg/1 336
Sulfate, mg/1 154
Phosphate, mg/1 2.5
Ammonia-N, mg/1
7.72
89
64
29.3
7.92
1409
903
34.8
8.38
18
12
14.3
358
161
2.5
6.92
12254
10258
7.87
10321
7896
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
678
39
12
3
113
3
90
12
1399
. 121
35
29
245
52
409
62
372
39
12
4
79
3
66
14
1251
106
27
33
138
84
367
58
3807
29
265
3
519
2
2536
16
1851
136
520
13
1522
123
3413
71
684
43
11
3
100
2
77
19
1222
67
25
18
207
92
310
107
11.54
0.16
0.33
2.68
38.40
0.96
5.01
34.56
17.22
0.59
1.59
7.07
57.22
2.11
2.12
13.70
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
213
-------
TABLE A- 18. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
.TREATMENT NO: A-18 . . , .
j ,. i. j. -...,.
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1 ,
Phosphate, mg/1
Ammonia-N, mg/1
i
Aluminum Tot^l
Soliible
Cadmium Toteil
Solvible
Chromium Total
Solvible
Copper Tots1!
Soldble
i
Iron Tot^l
Soliible
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Solvible
Raw
Sewage
7.28
31
23
40.8
222
145
0.8
4.5
295
84
137
7
97
2
173
12
1576
131
154
14
352
117
450
231
Primary
Effluent
7.69
53
39
23.3
283
77
80
5
80
2
176
10
944
117
63
6
418
192
388
176
Miited
Ligtior
* te
1431
103'd
11*8
2311
60
<2BO
6
750
2
1914
13
22189
69
930
7
1063
156
7(360
185
Second.
Effluent
8.35
14
9
10.5
201
139
1.0
0.3
197
72
27
5
91
2
...153 '
12
1196
42
37
8
316
113
294
192
Primary
Sludae
6.98
2231
1561
15.89
0.53
1.34
5.40
53.05
3.50
7.64
21.25
Second.
Sludge
7.89
7698
5602
16.18
0.35
0.89
5.36
28.45
3.02
6.07
17.73
. .. .-. .. J . -. --._-. .,-_
All metals concentrations in microgr'ams/i feikcept for primary and secondary
sludges wherfl concentrations are in
1114
-------
rABLE A-19. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-19
Raw
Parameter Sewage
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, .mg/1
Phosphate, mg/1''
Ammonia-N , mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
6.98
230
122
40.9
169
133
0.4
"3.9
677
172
88
22
183
5
453:
16
636
113
267
37
2983
895
1114
5.9
Primary Mixed Second.
Effluent Liquor Effluent
7.51
74
56
' '25.3
..-..-
" . »
579
..... 133
75
12
'.- -111
5
322
10
. '580
139
129
222
880
666
- 571
80
7.70
3428
2574
19.7
.-
16562
, 116
'" "" 472
22
1400
5 ,
. 4786
17.
43429
65
2650
69
14657
513
12367
52
8.26
18
11
14.8
154
127
0.3 .
0.2
643
164
33
15
141
5
186
12
574 '
27
114
47
2093
528
350
42
Primary
Sludcte
6.77
1451
29,
-. .
26.99
0.52
1.62
5.89
70.93
3.66
12.19
17.76
Second.
Sludcre
7.81
6605
4833
** ' * " '
6.76
0.63
1.38
4.97
24.54
2.56
12.49
4.42 .
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
215 .-
-------
TABLE A-20. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-20
Parameter1
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
SolUble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Tota1!
Soluble
Raw
Sewage
7.21
26
14
14.2
110
108
0.2
1.8
520
78
138
25
144
5
625
46
1510
67
475
40
-
810
694
28
Primary
Effluent
7.68
38
22
10.2
357
108 -
101
18 .
93
5
240
16
1980
.38
310
27
590
808
700
59
Mixed
Liquor
7.76
1765
1250
6.9
11163
50 .,
548
12
' 1124
5-
- 7060
25.
45600
46
5340
15
19200
639
21250
42
Second.
Effluent
8.23
23
15
8.6
93
121
0.4 '
0.3
232
... 90
84
14
96
5 .
370
29
1180
105,.
160
31
1340
503
440
35
Primary
Sludae
6.
45
6314
3668
14.
0.
1.
7.
68.
7.
19.
33.
30
63
39
52
00
52
60
70
Second.
Sludae
7.72
6053
4224
32.25
0.84
1.67
7.08
103.13
7.74
20.9
14.38
All metals concentrations in micrograitis/1 except for primary and secondary
sludges where1 concentrations are in mg/1.
-------
TABLE A-21. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-21
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride , mg/1 .
Sulf ate , . mg/1 ...
'Phosphate, mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
.Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.29
32
15
8.2
. -
. -
-
.
661"
. .30.
' 146
13
' 500
5
425
14
3225
32
150
183
1678
632
1025
94
Primary
Effluent
7.44
78
43
.12.2
630
: . ,29 .
84
13
300
5
610
7
1395
66
200
5
1165
630
988
97
Mixed
Liquor
7.80
1880
1165
6.0
10033
. 38
625
20 .
1638
5
4675
17
38333
40
1725
17
14375
482
23200
77
Second.
Effluent
8.04
17
11
5.2
85
92
0.-3
0.5
576"
50
45 '
18
200
5
215
16
1060
77
100
7
1388
410
800
84
Primary
Sludae
6.86
7370
4870
' ' ' ~ . -
52.82
' 0.72
3.41
4.88
103.38
5.57
18.52
44.60
Second .
Sludae
7.67
11058
6753
38.25
0.65" '.''
3.00
5.60
86.00
5.2
16.47
54.50
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
217
-------
TABLE A-22. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-22
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfater mg/1 i
Phosphate, mg/1
Ammonia-N, mg/1
i
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.39
24
14
5.1
-
-
-
-
983
35
54
5
420
5
270
12
2510
16
140
6
1263
422
i860
7
Primary
Effluent
7.47
48
31
6,0
871
24
'43
9
270
5
260
8
1450
16
90
5
1212
407
1970
25
Mixed
Liquor
7.67
3938
2250
3.9
9940
12
391
7
' 1960
5
6040
5
53100
7
2920
5
20260
424
37960
6
Second.
Effluent
8.08
16
10
3.6
44
71
0.3
0.5
1091
33
29
8
'- 330
5
...-.- no
5
940
12
40
5
1090
.364
1594 .
6
Primary
Sludae
7.03
11160
7625
51.30
0.59
3.18
5.90
102.40
6.28
23.94
67.24
Second.
Sludae
7.67
12225
7100
-
37.96
0.57
2.88
5.88
95.80
6.48
21.8
63 . 74
All metals concentrations in micr6grams/l except' for primary and secondary
sludges where concentrations are -in mg/1.
216
-------
TABLE A-23. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-23
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate,, mg/1
Ammonia-N, TOg/1. ,
Raw
Sewage
7.60
95
81
33.1
336
154
/. -2.-S"
,.: '"
Primary
Effluent
7.73
93
64
28.8
* .
'''..'
Mixed
Liquor
7.84
1597
1126
25.8
. . . -
Second.
Effluent
8.34
20
11
14.5
339
. 166.
2.3
-
Primary
Sludge
6.90
10339
8622
Second.
Sludqe
7.79
8896
6635
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total.
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
655 .
54
24
- ' 4
10.6
4
308
15
1378
141
41
14
680
161
564
120
801
'"" 45
23
3
HI
3
' 356
15
1336
107
63
10
699
. 159
550
67
3201
37
247
4
582
2
2759
16
193.6
132
502
19
2560
266
4430
74
64.7
42
23
3
100
2
224
15
1373
40
36
8
747
237
445
56
11.
0.
0..
5.
47.
1.
18.
41.
04
44
80
42 .
80
48
19
30
19.98
0.58
2.00
8.26 .
69.94
2.03
9.77
21.30
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
219
-------
TABLE A-24. StiMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-24 .
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw Primary
Sewage Effluent
7.35
42
31
42.5
222
145
0.8
4.5
385
75
157
9
137
2
460
16
2243
200 -
75
21
653
366
477
203
7.78
43
35
19.8
310
78
90
5
110
2
313
9
1261
86
62
9
589
333
363
134
Mixed
Liquor
7.89
1328
861
12.5
3288
54
442
6
1116
2
2772
9
32738
36
1564
18
2519
347
8575
184
Second.
Effluent
8.33
15
8
9.7
208
135
1.0
0.3
332
82
26
7
111
2
225
9
1205
37
29
18
834
318
295
129
Primary
Sludge
7
.00
4065
2898
18
0
1
4
60
3
12
29
.34
.97
.78
.82
.50
.86
.38
.09
Second.
Sludge
7.85
5201
3530
21.83
0.94
1.49
4.74
43.10
3.61
8.88
15.26
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1. , ...
-------
TABLE A-25. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-25
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1-
Ammonia-N:,. mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.13
258
198
43.6
169
133
.. .0.4
3..9
- 785
140
. 135
18
109
"" 5
367
9
2492
199
221
47
4008
242
514
- 44
Primary
Effluent
7.60
23
15
23.2
,
515
152 .
-.. 82
23
83
' ' 5 "
259
8
1400
117
108
140
1099
195
357 .
' 35
Mixed
Liquor
7.70
2925
18.29
20.1
16693
112
493
20
, 1357
5
5571
- 23
41286
72
2679
58
5533
214.
11950
"' '31
Second.
Effluent
8.29
20
12
17.3
151
128 .
0.5
0.2
618
163
27
31
106
5
173
25
936
22
43
19
777
142
350
19
Primary
Sludge
7.25
395
30
23.30
0..51
1.54
5.84..
49.00
3.54
6.58
22.36
Second.
Sludoe
7.85
7172
4611
6.82
0.51
1.37
4.58
6.80
1.47
2.61
3.67
All metals concentrations in miorograms/l except for primary and secondary
sludges where concentrations are, in mg/1.
221
-------
TABLE A- 26. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-26
Parameter ,
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Totsll
Sol\3ble
Total
Soluble
TotAl
Soluble
Totdl
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.45
28
12
12.2
110
108
0.2
1.8
240
110
128
10
124
5
180
17
1488
74
190
11
-
232
766
62
Primary
EfflUent,
7.69
16
9
11,5
191
126
99
9
108
5 "
270
15
920
53
160
22
160
199
570
69
Mixed
Liquor
7.73
1843
1382
8.6
'* «'
. 7212
73 '
553 .
14
1070
5
... 6100
14 '
35100
61 '
4880
46
8040
.176
12480
79 '
Second. Primary
Effluent Sludge
8.20
24
18
8.5
98 .
.129
0.3 -
0.3
182
139
65
17
93
5
213..
20
675
' 65
75
19"
613
145
400
26
7.
03
4395
3128
8.
0.
1.
6.
39.
4.
7.
22.
56
51
37
28
10
70
32
74
Second.
Sludce
7.79
3167
2362
17.07
0.63
1.58
6.64
56.75
5.20
5.25
13.82
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
222
-------
TABLE A- 27. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 2 7
Raw
Parameter Sewage
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate ,
Ammonia-N,
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
mg/1
Total
Soluble
Total
Soluble
Total
Soluble ''
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
7.32
51
24
9.0
. -
834
52
.77
84
513
' " 5 '
363
12
3200
49
175
6
2050
377
1462
133
Primary
Effluent
7.46
38
23
7.7
, k -, .
633
38
68
81 . '
363 .
5
288
9
1285
25
188
12
630
347
1425 '
141
Mixed Second .
Liquor Effluent
7.83
1518
1040
6.0
, * -S
3641
42 '
419
.' 34-' .
.. 1963
5
4000
11
27250
52
2250
7
8650
302
' 22375
112-
8.03
19
15
4.4
84
93
0.6'
0.4
301 .
54
56
23
288
5
168
8
625
23"
100
5
620
242
663
73
Primary
Sludae
6.77
9430
6180
21
0
3
5
72
4
14
63
.25
.74
.24
.,32
.50
.25
.00
.50
Second.
Sludce
7.75
6888
4593
,- . .
33 . 50
0.46
3.42
5.87
84.25
4.42
9.67
59.88
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
223
-------
TABLE A-28. NUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-28
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, ttg/1
Chloride,
mg/1
Raw
Sewage
7.35
44
30
5.3
-
Primary
Effluent
7.44
24
16
6.6
Mifced
Liquor
7,76
2455
1563
3.3
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Tot4i
SolUble
Totdl
Soluble
Totetl
SolUble
TOtcJl
Soluble
Total
Solvible
Total
Soluble
Total
Soluble
Total
Soluble
-
-
890
28
37
5
530
5
350
9
2350
53
180
6
2132
232
2160
8
692
53
. 67 .
8
380
5
250
4
1310
41
110
5
664
192
1830
5
11600
23
462
13
.2340
5
5920
3
48500
6
272,0
7
13960
190
23810
6
Second.
Effluent
8.04
23
14
3.5
44
66
0.6
0.5
471
38
.. . . .23
4
310
" 5
110
' 5
1250
36
60
7
360
156
980
6
Primary
Sludge
7.
08
6480
4927
32.
0.
3.
5.
105.
5.
21.
63.
00
58
14
84
60
78
26
40
Second.
Sludae
7.63
8870
5995
16.10
0.43
2.63
6.04
76.40
6.10
17.28
47.72
All metals concentrations in micrograms/1 except-for primary'and secondary
sludges where concentrations are in mg/1.
-------
TABLE A- 29. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 2 9
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1
Ammonia-N, .mg/1
Raw
Sewacre
7.68
111
98
29.2
336
154
2.5
. -
Primary
Effluent
7.70
74
50
30.2
Mixed
Licruor
7.99
1154
' 757
18.2
Second.
Effluent
8.27
18
12
14..8
352
161- .
2.7
Primary
Sludge
6.87
16318
13781
. ..
Second.
S ludge
7.81
7344
5626 ,
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
669
39
11
3
113
3
90
12
1399
114
35
27
330
52
409
62
352
34
11
4
69
2
75
8
1231
108 '
27
13
179
52
390
88
2354
56
. .83
3
421
2
623
10
1885
135
214
15
440
.. , -42-
2282
61
338
39
9
3
107
2
61
8
1233
36
20
11
186
29
290
50
9
0
0
2
49
0
0
16
.37
.18
.55
.80
.22
.89
.93
.60
11.88
0.35
1.11
2.65
64.20
1.32
0.58
14.68
_ - .
All metals concentrations in micrograms/1 except for primary and secondary .
sludges where concentrations are in mg/1.
225
-------
TABLE A-30. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
{TREATMENT NO; A-30
Parameter,
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
/ mg/1
/ mg/1
Total
Soljible
Total
Soluble
Tot&l
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
6.68
40
30
34.8
222
145
0.8
4.5
278
77
63
6
62
2
162
10
1527
102
100
8
366
79
440
153
Primary
Effluent
7.67
39
26
28.0
197
72
. 36- . .
6
63. ..
2
' 157'
10
1060
104
20
9
262
111
300
116
Mixed Second.
Liqubt Effluent
7,90
1141
686
13.5
2098
53
219
6
591
2
1577
10 '"
26655
100
1004
9
672
66
7242
77
8.31
18
11
11.8
208
138
1.1
0.4
250
73
10
4
.' 116.
2
134
8
1361
42
16
4
231
45
305
59
Primary
Sludge
7.
08
2681
2103
10.
0.
0.
5.
51.
2.
6.
19.
76
35
98
02
77
81
28
26
Second.
S ludae
7.85
5257
3924
22.37
0.29
0.92
4.97
26.25
2.09
6.14
10.95
All metals concentrations in miCrograms/1 except for .primary and secondary
sludges where concentrations are in mg/1.
225
-------
TABLE A-31. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-31
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate, mg/1"
Ammonia-N , mg/1 .
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total.
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewaqe
7.16
240
185
41,8
169
133
"0.4 '
. . 3.9
567
163
- 69
19
.. 90,
5
213 V
16
936
.. 145
-, . 143.
19
490
' 175
'- 429
24
Primary
Effluent
7.55
37
30
23.3
~- ' .
385
186
67
24
.... -.86
5
200
10
: 743
159
.;71 .
. 58
383
'. "42
.. 486
77
Mixed Second.
Liquor Effluent
7.70
3972
1285
26.6
11466
97
445.
24
1200
5
2757
7
26571
140
2043
19
721
19
6814
49
8.29
12
8
17.5
153
128
0.6
0.3
738
138
29
22
78
5
113
14
525
40
71
22
274
14
321
25
Primary
Sludcre
6.50
237
17
14.57
0.53
1.45
3.95
33.58
2.02
0.88
12.25
Second..
Sludce
7.86
4316
3441.
4.55
0.53
1.36
2.73
6.17
0.84
0.55
3.87
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations' are in mg/1.
227.
-------
,ABLE A-32. NUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT Np:__A-_32,
:;^_- . -
'
Parameter^,
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
___^«Me*_^
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
'
-
Raw
Sewage
7.45
22
14
12.0
110
108
0.2
1.8
216
126
98
9
128
5
210
46
650
95
120
13
438
66
644
92
-
-
Primary
Effluent^
7.68
22
14
11.6
121
120
65 ;
9
. 96 . .
5
'" 140."'"
13
' 560 '
48
70
10
288
45
672
72
Mixed
_ Lictuor :_
7*64
1987
1435
9.9
2533
. 70
442
7
1320
5
.. 4280"..
15
19100
187
3280 -
11
658
36
7960
60
MM^"MMM^"MM~"
1
Second.
Effluent
w^ ^*^*^~
8.23
18
..13
9.0
97
, 127
. 0.3
. '.' -0.3 '
210 -
136
94
7
100
5
130,
17
400.
- 210.
.. 70
11
148
34
440
39
i
Primary
Sludge _,
7
.06
3511
2281
9
0
1
,,4
49
5
2
21
« "«^
.. i
.10
.61
.39
.34
.38
.82
.15
.48
^«««V»**
1 '"
Second.
Sludge _
7.63
3224
4140
6.04
0.45
1.29
5.16
56.75
5.54
0.56
6.63
i.
sludges where concentrations are
22B
-------
TABLE A- 33. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A- 3 3
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate , mg/1
, Ammonia-N , mg/1 '
Aluminum Total .
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.66
107
87
34.2
336
154
., 2'5
.740'
40
15
4
114
3
302
10
1384
80
66
20
603
93
520
114
Primary
Effluent
7.79
84
58
26.4
~ "'**
. ' 464
31
,19
6
77
2
282
7
1352
68
43
9
546
112
473
55
Mixed
Liquor
7.92
1235
822
.17.6
.3516
44
187 .
4
491
2
1631
13
2007
42
365
31
1874
130
4282
126
Second.
Effluent
8.32
18
12
13.7
347
162
'-.' 2.6
450
50
15
3
79
2
80
12
1234
33
28
9
367
111
336
38
Primary
Sludqe
6.90
16010
9334
14.00
0.48
0.90
4.32
33.70
1.62
14.24
38.89
Second.
Sludae
7.84
10645 .
8019
17.23
0.65
2.24
3.72
53.22
.1.84
7.61
23.30
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
229
-------
TABLE A- 34. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TfeJSATMENT NO: A-34
Parameter .
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride, mg/1
Sulfate, mg/1
Phosphate , mg/1
Ammonia-N, mg/1
Aluminum Total
Soluble
Cadmium Total
Soluble
Chromium Total
Soluble
Copper Total
Soluble
Iron Total
Soluble
Lead Total
Soluble
Nickel Total
Soluble
Zinc Total
Soluble
Raw
Sewage
7.17
47
53
46.0
222
145
0.8
4.5
778
83
222
9
253
2
756
18
2322
279
200
17
1522
570
536
315
Primary
Effluent
7.72
52
36
20.7
464
66
89
6
169
2
503
6
1526
104
64
26
1179
661
532
139
Mixed
Liquor
7.91
1337
. 834
12.3
. 3465
56
507
6
1343
2
3050
11
24328
37
1926
14
3514
586
6859
247
Second.
Effluent
8.32
16
9
8.1
202
145
0.7
0.3
311
92
24
6
132
2
300
8
1373
15
41
6
1318
525
436 .
171
Primary
Sludge
6.91
3629
2371
26.65
1.81
2.06
5.45
73.32
4.16
16.49
24.91
Second .
Sludge
7.86
6809
4443
-
25.91
1.60
1.88
4.88
58.50
3.44
8.70
18.41
All metals concentrations in micrograms/1'except for primary and secondary
sludges where concentrations are in mg/1.
230
-------
TABLE A-35. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-35
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride ,
mg/1
Sulfate, mg/1
Phosphate "
> Ammonia-N
Aluminum
Cadmium
Chromium
Copper
'
Iron
Lead
Nickel
Zinc
, ing/ ±
, mg/1 ''
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.00
302
197
45.8
169
133
OA'
i'4
'3.9
1574
140
87
23
140
5
1071
18
2117
174
260
22
708
420
540
61
Primary
Effluent
7.52
51
31
26.6
. - t
652
' " 118
.56
34
155
5
475
24
1490
192
ieo
137
496
523
570
63
Mixed
Liquor
7.83
2248
1344
21.2
21084
149
446
28
1536
5
4586
9
36750
79
2717
50
6388
550
8600
30
Second.
Effluent
8.27
19
12
18.7
149
132
;. ' /» }
* 0. d
0.2
855
188
38
34
127
5
135
15
883
39
75
47
775
436
350
15
Primary
Sludoe
7.12
1525
40 .
32.45
0.45
1.81
..4.60
56.25
3.33
12.35
15.95
Second.
Sludge
7.85
3873
2259
12.46
0.49
1..5.2
5.02
23.50
3.31
1.62
6.97
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
231
-------
TABLE A-36. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-36
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate, mg/i
Ammonia-N, mg/1
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewage
7.31
39
24
10.7
110
108
0.2
1.8
1193
96
102
13
100
5
510
11
. 1510
60
160
35
319
609
463
47
Primary
Effluent ,
7.60
21
32
10.7
1004
97
160
13
85
5
360
17
1250
80
140
43
288
501
488
53
Mixed
Liquor
7.77
3025
1794
9.8
3736
85
692
12
1500
5
6660
25
55500
57
5640
116
14275
555
10410
81
Second.
Effluent
8.22
47
19
8.9
102
123
0.4
0.5
407
120
224
' 14
97
5
750
20
833
69
330
61
780
454
400
50
Primary
Sludcre
6.
89
6564
4528
16.
0.
1.
6.
80.
6.
17.
23.
50
73
83
98
00
72
70
08
Second.
Sludcre
7.71
5858
3645
21.82
1.02
1.49
6.64
22.50
6.60
19.25
8.47
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are In mg/li.
232
-------
TABLE A-37. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-37
Parameter
PH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1 -
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble .
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw Primary Mixed
Sewacre Effluent Liquor
7.79
111
98
29.2
336
154
°-;2-:-5' " "
678
39
11
3
113
3
98
.12
1399
114
35
27
245
52
409
62
7.76
96
65
25.1
.' . ' i-
435
41
11
4
93
3
220
11
1344
89
50
17
216
96
464
39
8.03
1392
978
18.3
3006
44
174
4
702
3
2000
12
1945
62
417
18
1388
128
4179
81
Second.
Effluent
8.31
16
11
15.9
353
160
' 2.5
486
42
11
4
86
2
82
12
1311
62
26
14
122
122
355
49
Primary
Sludge
6
.90
12787
10773
12
0
0
2
40
0
8
40
.88
.49
.62
.04
.95
.71
.25
.00
Second.
Sludge
7.89
9415
708.6
'
13.40
0.52
2.14
3.02
54.89
2.23
0.76
15.05
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in mg/1.
233
-------
TABLE A-38. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-38 ,. _
Parameter
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulfate, mg/1
Phosphate
Ammonia-N
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
, mg/1
, mg/1
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Total
Soluble
Raw
Sewaqe
7.26
38
28
42.7
222
145
0.8
4.5
337
84
87
9
84
2
170
14
1483
126
97
10
373
123
413
145
Primary
Effluent
7.66
36
27
22.0
218
67
51
5
79
2
113
7
1415
93
50
6
469
196
316
BO .
Mixed
Liqu6r
7.98
1261
887
11-8
2727
60
258
5
832
2
2009
12
19996
59
1007
7
1409
135
7654
119
Second.
Effluent
8.36
14
10
9.8
211
141
1.0
0.2
217
84
32
5
107
2
150 '
13
1090
31
23
6
300
102
229
. .143
Primary
Sludge
6.
95
3367
2642
12.
0.
1.
5.
44.
3.
7.
23.
87
43
22
41
10
08
57
31
Second .
Sludce
7.90
4899
3478
19.10
0.32
1.30
5.30
33.09
2.67
6.20
11.69
All metals concentrations in micrograms/1 except for primary and secondary
sludges where concentrations are in
234
-------
TABLE A-39. SUMMARY OF AVERAGE OPERATIONAL CHARACTERISTICS
TREATMENT NO: A-39
Raw Primary
Parameter Sewage Effluent
pH
TSS, mg/1
TVSS, mg/1
SOC, mg/1
Chloride,
mg/1
Sulf ate, mg/1
Phosphate ,
' Ammonia-N,
Aluminum
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
mg/1,
/I
mg/1
Total
Soluble .
Total
Soluble
Total
Soluble -
Total
Soluble
Total
Soluble
Total
Soluble
Total .
Soluble
Total
Soluble
7.05
209
171
60.4
169
: 133
..Ov*- ".''
3. 9
693
209 .
81
22
124
5
269
16
671
170
100
69
619
110
450
62
7.52
33
26
23.0
'.' 3
391
196
55
25
119
5
193
13
508
125
50
133
406
78
364
52
Mixed Second.
Liquor Effluent
7.80
1052
79.7
21.9 .
. - ..
8232
121
' 476 '
29
1193
5
. 3071
16
25186
71
1943
47
1624
68
6271
44
8.20
29
20
20.1
146
131.
0.5
0.4
309
166
49
29
107
5
117
24
458
72
50 '
30
403
59
286
33
Primary
Sludge
6.74
173
44
21.44
0.42
1.69
4.37
30.83
2.76
3.58
10.43
Second.
S ludge
7.63
3028
2236
1.88
0.39
1.49
4.08
5.80
0.76
0.78
2.88
All metals concentrations in microgr.ams/1 except for primary and secondary
sludges where concentrations are in mg/1.
235
-------
APPENDIX B
CORRELATION OF METALS DISTRIBUTION DATA
WITH PREDICTIVE MODELS
I. Correlation of Data with Model 3
II. Correlation of Data with Model 4
236
-------
PART I. FIT OF EXPERIMENTAL DATA TO MODEL 3
Model 3 is expressed, from Section 8, in the form
CmM (VSS/C) = A (VSS) H- 3 Model 3
j. iVi tw> Ifcl
In Figures B.I through B.8, each figure presents a computer
generator plot for one metal in the form process liquids. The
data points plotted are numbers, which correspond to the Runs
(1-6) of the 39 treatments tested in the pilot studies.
Abbreviations are as follows:
AL - Aluminum ... , FE - Iron
. CD -r.Cadmium.,.'.... ..,;.-..' 0_ , PB - Lead'.
CR - Chromium ' NI -'Nickel . - ''':'
CU -' Copper ' ZN - Zinc
RO - Raw Sewage
PE - Primary Effluent
.ML...- Mixed Liquor .
SE - Secondary Effluent , .
TVSS - Volatile Suspended Solids
On the plots, YY is the Y-axis, corresponding to the left hand
.side of the Model 3 equation. Maximum values of YY and TVSS
plotted are indicated on the axes, for scale. Units of both
axes are .mg/1.
PART II. FIT OF EXPERIMENTAL DATA TO MODEL 4
Model 4 is expressed, from Section 8, in the form
.CTM = PCSM + * ' - M°del 4
,In Figures B.9 through B.16, each figure represents a computer
generated plot for one metal in the form process liquids. The
data points plotted are numbers, which correspond to the Runs
(1-6) of the 39 treatments tested in the pilot studies.
Abbreviations are identical to those used on Figures -B.l
through B.8. Units of both the X- and Y-axes are mg/1.
237..
-------
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260.76933 CD CO
VV-MAX 88.42654 CD FE
3
3
11
1
31
11
2 2
542
46*
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VV-KAX 317S.37SOO
1 1
1 >
> 11
S 43
3
3 3
25 2
33 6
6 2
22
6 4
2
5.2
3 4
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TVSS-MAX
CD ML
197.50000 TVSS-MAX 60.81999
JIT-MAX 84.01674 CD SE
3
6 4
36
5 51
22
12
52
3 6
4
3 35
3
4
354
33
5 64
5614
222
TVSS-MAX 3004.00000
TWSS-MAX 72.00000
Figure B-2. Correlation of Model 3 in four process liquids for cadmium.
239
-------
r-hft» 207.03458 CR KO
VY-MAX 70.99383 CR PE
32
&
426
463
46
hAX 30I6.S6885
TVSS-HAX
CR HL
111
3 1
23
36
3
22
36
2
4
3 6
465
46'
197.50000
rV-HAX 73.18033
36
66
23
12
534
2
46
22
52
TVSS-MAX 68.01999
CR St
46
393
66
4
44
3
TOSS-HAX 3004.00C!00
TVSS-MAX 72.00000
Figure B-3. Correlation of Mddel 3 in four process liquids for chromium.
240
-------
YY-MAX 202.95047 CU
YY-HAX 75.21933 CU PE
YY
3 3
31
32
s Z
26
46
46
1 11
1
25
36
3- ..
3 22
36
46S
4 5
' 3
446
MAX . 3012.11670
, TVSS-MAX
CU ML
197.SOOOO TVSS-MAX 6B.6199?
YY-MAX 73.92197 CU SE
36
66
46
63
534
22
25
52
6
3 6
4
t 44
I4A
3',t,
TWSS-HAX 3004.00000
TWSC-MAX 72.00000
Figure B-4. Correlation of Model 3 in four process liquids for copper.
241
-------
YY-MAX 228.77176 FE RO
74.45805 FE fE
I 1
3 II
I
I
3
11
23
226
65
465
2
36
3
3 3 22
3 4
6
26
65
S445
36
44
YY-MA.X 3019.96045
TVSS-MAX 197.50000
FE ML
36
66
4i
23
536
1 2
25
22
52
YY-HAX 73.40562
3
3
t
4 4
1 353
TVSS-MAX
FE SE
68.O1799
TVSfc-HAX 3004.00006
TVES-«AX 72.00000
Figure B-5. Corr61ation of Model 3 in four process liquids for iron
242
-------
YY-MAX 231.95844 PR RO
YY-MAX 100.37207 PB PE
I
11
2 2
2
4426
S
466
1 1
3322
.234
6 5
23
.. .3
"26
' 6
443
4 54
YY-flftX 3032.43423
YVSS-MAX
PB ML ,
197.50000 TVSS-KAX 4B.B1999
YY-MftX 130.90904 P£" EE
34
344
14
534
1 2
125
22
52
1 4
3 4
2 3 3544
234444
3245
5
TVSS-MAX 3004.00000
TWSS-MAX 72.00000
Figure B-6. Correlation of Model 3 in four process liquids for lead.
-------
«t)5.10284 NJ
YY-hAX 228.53307 NX
YY
3 3
11
11 1
1
. i
t
4 2
22 3
S 2
2 2
S 626
6 3
4 22 6 3
4333
5 2
11
1
465
MAX 3000.87012
TVSS-HAX
HL
197.50000
YY
36
! 4
2346
.. 16
2 S3
1
2223
1
46
43
MAX 170.11237
TVSS-HAX
HI SIT
63.8199?
54 6
13644 3
22603 .
3
tVSS-MAX 3004.00006
TVSS-M.-.X 72.OOOOO
Figure B-7. Cori'^latidn of Model 3 in fouf process liquids for nickel,
244
-------
tr-l''.X 237.70793 ZN RO
TY-MAX 78.15605 ZH F£
1 3
3
11
1
1
11
3
2
4
465
46
1 1
J
1
1 31
2
2 5
2 4
3
3 6
4 S
S 46
4
436
YY-HAX 3025.98047
TVSS-MAX
ZN ML
197.50000
YY-MAX 72.13962
TVSS-HftX 68.01999
ZN SE
36
46
23
S36
2 2
25
2
22
22
1 4
1 54
22 246
353
56
3
3 6
TVSS-HAX 3004.00000
TVSS-MAX 72.00000
Figure B-8. Correlation of Model 3 in four process liquids for zinc.
.245.
-------
CTM-MAX 1.57433 AL
CiM-MAX 0.94325 AL FE
3 A
A
A
3 21
i
33 14
J
55
2
3 4 21
422
4 2
3
34
2
. 5
3 1
2 1
1
21 5
33 1
422 1
3 44 2
2
42
2
CSM-HAX 1.43370
CSM-MAX 0.91350
CTM
MAX 21.08416
CTH-NAX 1.09120 AL SE
A 3
43
36
21
21
24
1
16
3 6
1
3
3 1
33
16
' 3' -1
4 SI
2
3 22211
9
2
24
CSH-HAX 20.93472
CSH-HAX 1.03780
Figure B-9. dorrelation of Modal 4 iti four process liquids for aluminum.
246
-------
Ctn-*.'.X 0.22155 CD RO
CTH-HAX 0.19660 CD PE
4 2
5
42
3
24
34
554
33 2
36
2
,. 36 6 2
66
2
1
U
3 54
5 22
3 5
36 22
3
3 46
:-, 6
3 2
11
1 1
.CS11-HAX 0.21X80
CSH-MAX 0.17460
Cln-hAX 0.6V240 C» ML
1
CTH-HAX 0.22440 CD SE
42
4
5 2
342
333
A
34
121
1
11
S 4
3 3 56
352 6
3 61
3 3S2
22
11
12
CSM-MAX 0.60040
csn-n>-.x 0.21000
Figure B-10. .Correlation of Model 4 in four process.liquids for.cadmium.
247
-------
1.04250 CR RO
34
414
22
CTH-HAX 0.4*000 CR PE
I
0
5
A
6
6
55
5
5
6
5
6
5
.. 6
2
3
41
23
22
241
1
CSH-HAX 1.05750
CStt-HAX 0.4SSOO
CTM-HftX 2.72000 CR ML
CTM-MAX 0.412SO CR SE
433
S
423
44
12
2
1 1
11
23
432
322
44
11
CSH-HAX 2.71506
CGM-MAX 0.407SO
Figure B-ll. Correlation of Model 4 ih four process liquids for chromiu:
248
-------
CTrt-MAX 1.07143 CU RO
CTH-HAX 0.61000 CU PE
322
A3
AS
361
423
A
41
25
4
446
33
3
11
CSM-MAX 1.05274 . CSM-HflX O./.0225
. ... M, CTM-HAX 0.75000 CU 6E .
CTM-MAX 7.04000 CU ML
53
54
23
2
21
I
42
22
264
52
4
333 .
343
366
11
CSH-HAX 7.03SOO
CSM-HAX 0.72925
Figure B-12. Correlation of Model 4 in four process liquids for copper,
249
-------
CTM-HAX 4.00000 FE KO
I
CTM-HAX 2.27000 FE PE
55
3
224
324
11
34
24
3 2
3 6
US
1416
121
2 45
26
6
3 4
CSH-HAX 3.95600 CSH-HAX 2.21440
CTH-HAX 62.70569 FE ML CTfi-MAX 1.59000 FE SE
3
212
1 2
61
221
45
32
2
62
35
235
2
.;. 2 "-
' 4
111
I
46
36
'4 3
CSH-HAX 62.74012
CSrt-HAX 1.S5240
Figure B-13. Cdrreldtion of Model 4 iti f6ur process liquids for iron,
250
-------
CTH-HAX 0.47500 PB RO
' 3-
3
3 '
4 *
A 2 .
34'j ..
' 4 '
2322
3 21
11
1
12
CTM-hAX 0.31000 PB
CSH-MAX 0.43500
'43--..
11
2
3
3 42
2 21
112
12
CSM-MAX 0.2O300
CT«-Mflk 5.44000 PB HL
I
I .
I '. . .
CTM-HAX 0.33000 PB 8E
33
35
6
22
1
11
3
5 5
345
3 46
3 4
3 6
32
211
122
2
CSH-MAX 5.52340
CSM-flAX 0.26820
Figure B-14. Correlation of Model 4 in four process liquids for lead,
251
-------
CTrt-HAX A.07300 Ht KO
CTM
CTrt-MAX 3.47200 MI FE
.5 A
S 4
2343
546
4 2
3 1
3 3
23
1
23 6
35
3
2 6
5 1
123
2 13
3
XI
CSM-MAX 5.64220
I-HAX 20.2&OOO HI ML
CTM-MAX 2.1B286 NI SC
CSM-MftX 3.27220
3
3
5
24 3
2
.4 2
3
S 1-
5-
. 43 21 3
-------
CTM-HAX 2.14000 ZN RO CT
I -' .
! '-.--. *
i .
I :_...-
1 . . '
T ' ''-"'-
Ji . ...*'-..
i
I . .'.'
i *
1
i- -. . ' / .
r . -.-''
i - .-" .. .
i . . . . .
* .-..-»
j . *» . - . . -- i
1 ' ' . : 5 3 . . . . :
' i- ' - 4 * ' - - : ' - : .
J- ' -'.-46 ' -- ' . '
-!-.}. ' ' 4 ...'.-. .-.
' I ' 2^4'.- ,' .-. ^ .; . - ' .
1 134
I 22 113
I 222134 . - .
I 2 . ...".
I "' ' : ,.-.'..
i -.-. " .:.'. ' . .
i ' . . .''" - "
i -.- ._ .
*~" CSH-MAX 2.15180
CTH-MAX 37.94000 ZM HL CT
I. - '
.
I '...". ' 4
1
J. ' '-'..
I-
. .
1 . ' "
.1 ' ' '.'
I -
I . -
I ' ' 4
5 ... -- - -
5 ' *
« 55
; . . 45
-I . .
.: . ,-;' '-
X
I .
I '
I .
I ..-* ,-
I 23
I 24
13
1 22 .
1 32
I 11
I 11
11
| ________J
* ~ reM-HAX 37.933SO
-MAX 1.77000 ZM PE .
4
(,
A
3
' '
1 ' . '
' 5
6
'5 .
44
54
33
21
2 431
-, 2233 .
22 3
-
'
CSH-HAX 1.94040
-MAX 2.48000 ZH SE
' ' 4
'
. ' ' 4~
4
4
5
5 4
5 '
5 ' .
2 44
221213
22 3
CSM-MAX 2.17S20
Figure B-16. Correlation of Model...4 .in four process liquids .for zinc.
253
-------
. APPENDIX d
DEVELOPMENT OF PREDICTIVE MODELS
I. Model PW
II. Model FS
254
-------
I. PROCESS MODEL FOR THE PREDICTION. OF METAL REMOVAL
THROUGH THE PRIMARY CLARIFIER MODEL PW
This process model is intended to simulate the heavy metal
removal through the primary clarifier. The effluent heavy
metal concentration is predicted from the influent raw sewage
metal concentration and the removal efficiency of VSS in the
primary clarifier.
In the primary clarifier, the draw-out of the sludge is
often intermittent, while the influent and effluent flow is
continuous. Therefore the exact measurement of sludge mass
flux is difficult. . Thus, Model PW assumes continuous steady-
state draw-out of the sludge in the process model. The measure-
' 'ment of heavy metal content in the .sludge is also difficult.
The Model PW inco-rporates an assumption for the heavy metal
,t content, xP .in mS metal /mg',. VSS, .of the draw-out sludge, as a
' weighted mean of the influent level CRSM/XR and effluent level
CPSM/XP» where XR and Xp are the VSS concentrations in the
raw sewage and in the primary effluent, respectively. A W value
of 1.0 represents the influent condition, and a W value of 0.0
the effluent condition.
VSS and metals balances give Equations (1) and (2).
QX + D (1),
QCRTM = QCPTM + DP K XP (2)'
where, Q = flow rate
D = flux
and the removal efficiency of VSS in the primary clarifier, Zp,
is defined by Equation (3),
Zp =1 - Xp/XR (3).
The expression to calculate XP is described by Equation (4),
Xp = W (CRSM/XR) + (1 - W) (CpsM/XP) (4).
From Equation (2), one obtains Equation (5)
CPTM = CRTM - Dp x Xp/Q (5)
Equation (1) rearranged is, Dp/Q = (XR - Xp). Thus, we obtain
Equation (6).
CPTM = CRTM - Xp x (XR - Xp) (6)
255
-------
Introducing the expression for XP iQ Equation (4), one
obtains Equation (7), .
. c?-m - S - {W(WV + (1 - w) (CPSM/V}
and CpSM and CpTM, i.e., CRSM = P'R CRTM + q'R and CPSM =
P'p CPTM + Q'p- Replacing CRSM and CPSM by these correlations,
we have Equation (9),
predicted. _ , _ _ ^ . _ .
STM "" u VP R) RIM zPWq R
ZR (i - w)qy/{i + zR(i - w)P'p} (9)
Equation (9) predicts the effluent total metal concentra-
tion, CPTM. from the.primary clarifier, based upon the influent
total metal concentration, CRTM» tne removal efficiency of VSS,
Zp, and the coefficients of the Model 4' correlation P'R, p'n,
q'R and q'r,. ZR is calculated from Z i.e., Z = XD/Xp - 1 =
1/(1 - Z ) - 1. P p R
We also predict the heavy metal concentration in the
primary sludge, as follows. Based upon volume flow rate, Qps,
for the sludge draw-out, we can predict the metal concentration
in the sludge by Equation (10),
CPSTM = Q(CRTM ~ CPTM/Qps (10)
Alternately, if we have VSS data for the sludge, X , we get a
prediction CpgTM by Equation (11). ps
CPSTM = V***8 ' (11)
The accurate measurement of Xps is often difficult. Equation (10)
is recommended rather than Equation (11).
256
-------
II. PROCESS MODEL FOR THE PREDICTION OF METAL REMOVAL THROUGH
THE SEWAGE TREATMENT PLANTMODEL FS
The full system model (Model FS) is devised to simulate
the heavy metal removal through the plant. The secondary
effluent total metal concentration, Cg^jj is predicted from the
raw sewage total metal concentration, CR^JJ, and the removal
efficiencies of VSS and SOC through the plant. The operational
characteristic constants of the process are also needed,
including yield factor, Y, recycle ratio, R, VSS values in the
aeration tank, X, and the endogenous constant, k
D and XSS are VSS draw-out rate (mg VSS/hr) and metal content
in the secondary sludge (mg metal/mg VSS), respectively. Xp,
XQ, and X are VSS concentrations of primary effluent, secondary
effluent, and mixed liquor, respectively. SR and SQ are SOC
subtrate concentrations, for raw sewage and secondary effluent.
XR is the VSS generation rate through biodegradation (mg
VSS/hr). Y and R are yield factor and recycle ratio,
respectively.
From Equations (12) and (13), we obtain Equation (15) for
V
Ds = Q(Xp - V + Y(1 + R) Q (Sr - S0) - kdXV (15)
We assume XSS = CSSM/X0» and substitute for xss in Equation (14)
Then, replacing Ds and XSS *n Equation (14) by Ds from
Equation (15), we obtain Equation (16).
CSTM ' CPTM - 'SSM/V {XP - X0 + Y(1.+ R)" (SR ' V ' u
We also have the Model 4' correlation between CgSM and CgTM,
represented by Equation (17).
CSSM = p'sCSTM ~ q^S (17)
Equation (16) is rewritten as Equation (18).
CSTM = CPTM ~ CSSM x J
257
-------
where, j = (xp - x + Y(i + R) (s_ - s.) - k, XV/Q}/X. (19)
U K U U U
By combining Equations (17) and (18), one gets Equation (20).
cpredicted = ^predicted _ . jw/i + D- v n f m
<-STM ^PTM q S J^/(-1 + P s x J) (-0)
CPTM i-n Equation (20) is predicted by Model PW. Equation (20)
is the prediction equation of Co ,,,,-, -nr,
^ biM; fOr -the process Model FS .
J of Equation (19) can be rewritten by using removal effi-
ciencies, Zvgg and Z, as Equation (19a),
J ='{XPZVSS + Y(1 + R)SRZSOC - VV/Q}/{XP(1 - 2VSS)} (19S)
where, ZVSg = 1 - XQ/Xp and ZSQC 1 - SQ/SR.
Xp is related to XR by Z (= 1 - XP/XR), and so Xp = XR (1 - Zp).
c ^
We can predict CSTM t>y Equation (20), knowing CRTM, SOC,
and VSS of raw sewage, VSS of mixed liquor, X, and Zp,
and
The removal rates (metal fluxes) as primary sludge,
secondary sludge and secondary effluent are calculated by
Equations (21), (22), and (23) respectively.
HMPS = Q x (CRTM - GpTM) (21)
HMSS = Q x (CpTM - CSTM) (22)
HMSE = Q x C (23)
Then, percentage removals are given by Equations (24), (25),
and (26).
%PS = HMPS x 100/HOUT (24)
%SS = HMSS x 100/HOUT (25)
%SE = HMSE x 100/HOUT (26)
where HOUT = HMPS + HMSS + HMSE.
258
------- |