United States
Environmental Protection
Agency
Municipal Environmental Research
Laboratory
Cincinnati OH 45268
EPA-6OO/2-79-1 58
December 1979
Research and Development
Computer-Aided
Synthesis of
Wastewater
Treatment and
Sludge Disposal
Systems
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate.further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7, Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ENVIRONMENTAL PROTECTION TECH-
NOLOGY series. This series describes research performed to develop and dem-
onstrate instrumentation, equipment, and methodology to repair or prevent en-
vironmental degradation from point and non-point sources of pollution. This work
provides the new or improved technology required for the control and treatment
of pollution sources to meet environmental quality standards.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/2-79-158
December 1979
COMPUTER-AIDED SYNTHESIS OF WASTEWATER TREATMENT AND
SLUDGE DISPOSAL SYSTEMS
by
Lewis A. Rossman
Wastewater Research Division
Municipal Environmental Research Laboratory
Cincinnati, Ohio 45268
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Municipal Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publica-
tion. 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 was created because of increasing
public and government concern about the dangers of pollution to the health
and welfare of the American people. Noxious air, foul water, and spoiled
land are tragic testimony to the deterioration of our natural environment.
The complexity of that environment and the interplay between its components
require a concentrated and integrated attack on the problem.
Research and development is that necessary first step in problem solution
and it involves defining the problem, measuring its impact, and searching for
solutions. The Municipal Environmental Research Laboratory develops new and
improved technology and systems for the prevention, treatment, and management
of wastewater and solid and hazardous waste pollutant discharges from municipal
and community sources, for the preservation and treatment of public drinking
water supplies, and to minimize the adverse economic, social, health, and
aesthetic effects of pollution. This publication is one of the products of
that research; a most vital communications link between the researcher and the
user community.
The work presented here describes the development and use of a computer-
aided preliminary design procedure for wastewater treatment and sludge
disposal systems. It enables a designer to efficiently synthesize and analyze
large numbers of alternative treatment schemes and rank them with respect to
several different cost, energy, and environmental criteria. Such a design
tool should enhance our capability to develop more innovative and efficient
waste treatment systems in these times of stricter environmental standards
and increasing resource costs.
Francis T. Mayo
Director, Municipal Environmental
Research Laboratory
m
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ABSTRACT
A computer-aided design procedure for the preliminary synthesis of
wastewater treatment and sludge disposal systems is developed. It selects
the_components in the wastewater treatment and sludge disposal trains from
a list of candidate process units with fixed design characteristics so
that criteria on effluent quality, cost, energy, land utilization, and
subjective undesireability are best satisfied. The computational procedure
uses implicit enumeration coupled with a heuristic penalty method that
accounts for the impact of return sidestreams from sludge processing. The
programmed version of the design procedure, called EXEC/OP, has been inter-
faced with the unit process subroutines contained in a previously EPA
developed system evaluation program known as EXECUTIVE. A number of
case study design problems are presented to demonstrate the versatility
of EXEC/OP. Included among these is a preliminary cost/energy-effective-
ness analysis for a hypothetical design problem containing over 15,000
alternative system configurations. The design approach described in this
report will be of interest to engineers and planners involved in the genera-
tion and evaluation of alternative wastewater treatment and sludge disposal
systems.
This report-covers a period from February 1978 to October 1978 and work
was completed as of February 1979.
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CONTENTS
Foreword ""!""
Abstract JY
Figures ..
Tab! es V11
1. Introduction ].
2. Conclusions ,. 6
3. Elements of the System Design Process JJ
4. An Overview of the System Synthesis Model ijj
5. Case Studies ••• 19
50
References • •-
Appendices
A. Mathematical Description of the Model 53
References ^
B. EXEC/OP Users' Guide »'
C. Unit Process Descriptions
References •
D.' Program Listing
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FIGURES
Number
1
2
3
4
5
6
7
Al
A2
Bl
82
B3
B4
B5
Page
Conceptual Flow Diagram of a Waste Treatment System 10
Multi-Option Flow Diagram for a Hypothetical Design Problem 15
Multi-Option Flow Diagram for Case Study 1 22
Multi-Option Flow Diagram for Case Study 2 28
Multi-Option Flow Diagram for Case Study 3 ... 35
EXEC/OP Output for Case Study 4 38
Illustration of the Non-Inferior Set 45
Flow Chart of the Implicit Enumeration Procedure .'.' 56
Overall System Design Algorithm .'.'." 59
Multi-Option Flow Diagram for a Hypothetical Design*Probiem 62
Organization of Input Data for EXEC/OP 54
Input Data for Hypothetical Design Problem 68
EXEC/OP Output for Hypothetical Design Problem .".'!"" 71
EXEC/OP Output for Single Design Evaluation "" 73
VI
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TABLES
Number
1 Evaluation Criteria for The LA/OMA Sludge Management Study
2 Assessment of Waste Treatment System Optimization Models
3 Unit Processes Contained in EXEC/OP
4 Waste Stream Parameters in EXEC/OP
5 Influent Waste Characteristics Used for Case Studies .....
6 Economic Parameters Used for Case Studies
7 Input Process Design Parameters for Case Study 1
8 Input Design Parameters for Vacuum Filtration and
Centrifugation
9 Least-Cost Designs for Case Study 1
10 Input Process Design Parameters for Case Study 2
11 Least-Cost Designs for Case Study 2
12 Input Process Design Parameters for Case Study 3
13 Least-Cost Design for Case Study 3
14 Least-Cost Energy Constrained Design for Case Study 5
15 Non-Inferior Cost/Energy Systems for Case Study 5
Bl EXEC/OP Unit Processes
B2 EXEC/OP Design Criteria
B3 EXEC/OP Waste Stream Parameters
B4 EXEC/OP Economic Parameters
8
12
13
20
21
23
25
26
30
32
34
36
47
48
63
64
65
66
vn
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SECTION 1
INTRODUCTION
The effective synthesis of waste treatment systems is a challenging
engineering task. The term synthesis refers to the speculation of both
a svstem structure - the choice and arrangement of unit processes and
operations - anS the design of the individual units within that structure
so thai a set of design objectives is fulfilled. Although the primary
goal may be the treatment of liquid wastes, .decisions Carding sludge
handling and disposal can have significant impacts on total system
performance, in both an economic and environmental sense.
Tn the oreliminary phases of the system design process the designer
needs to efficient y evaluate the performance of a large number of potential
system designs to identify a smaller number of attractive designs that will
then become the subject of a more detailed and Curate evaluation. This
S
feedback effect of these recycles complicates the numerical calculation
of system performance. In many instances these two factors can combine to
mike a complete evaluation of all possible system designs computationally
™tracta™e If on y intuition were used to select a more manageable
number of designs to examine, one could never be sure that some truly
attractive alternative was not .overlooked.
third reason for the complexity of the screening process is reflected
IltlrnStive sysS deigns that show the kinds of trade-offs that may exist
between various design criteria.
This report describes a computerized system synthesis model , called
EXEC/OP, that can aid the designer in the preliminary stages of the system
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,
of recycled sidestreams from sludge processing. ertects
™ J°]lowin9 sections of this report discuss the various features
vesat? m! ^P^nn3 H"-"156" °f Sample design Prob1ems "hat ?
h ^ ? ] y',, he , aPPendl ces contain a mathematical development of
the model and a Users' Guide and program listing of EXEC/OP?
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SECTION 2
CONCLUSIONS
The EXEC/OP computer program for synthesizing waste treatment systems
provides a useful tool for the preliminary screening of alternative designs.
Its features include an integrated design of both the wastewater and sludge
treatment sub-systems, consideration of multiple components in the waste.
stream!, selection of both the type and design level for unit processes,
and consideration of multiple design criteria. The penalty augmented
implicit enumeration method employed in its optimization algorithm is an
efficient means of searching for the best combinations of process options.
Usually only a small fraction of the computational effort.that would>have
been required for complete enumeration of all possible alternatives is
needed.
The program gives the designer a useful means for exploring trade-offs
between such criteria as cost, energy consumption, land utilization, and
subjective undesinability ratings. These factors can be-weighted and
combined into a single criterion function or can be treated as constraints
placed on the system design. Additional flexibility is provided by the
M next-best design feature of EXEC/OP. This can be used to identify a
group of designs that are close together with respect to one Primary
objective (e.g. cost) but could have varying levels of other secondary
objectives.
The case studies described in this report show that the program is
capable of analyzing design problems with from 15.000 to 21,000 alternative
system configurations in several minutes of computer processor time. The
core storage requirements of the program are modest at 25 K words, me
subroutines that model the performance of the treatment processes have been
employed in a modular fashion so that improvements and new types of unit
processes may be easily added at a later time.
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SECTION 3
ELEMENTS OF THE SYSTEM DESIGN PROCESS
?S .Wit5.most engineering problems, the design of wastewater treatment
and pJffit?nnPr?i Srfeni? inv°lv!s the elements of synthesis, analysis,
and evaluation [3]. The element of synthesis refers to the conception of
a system structure and its operating characteristics. Analysis determines
™!A?1Ven STutem s?ruc*ure wil1 behave under specific design and operating
conditions. The evaluation step compares the performance of alternative
system designs so that a best design may be identified.
The entire design process consists of a linking together of these
three elements in a-n iterative fashion. Information feedback from the
analysis and evaluation phases is used to suggest changes to be made in
the synthesis step. Any new designs so generated are then analyzed and
e*a]ua. ?? as.the Process cycles through another iteration. Also, the level
of detail and accuracy maintained in the analysis will usually increase as
the process proceeds. In the preliminary stages, levels just high enough
to perform an efficient yet reliable screening of a very large number of
alternative system designs is all that is required.
There are two types of decisions to be made when synthesizing a waste
treatment system. The first type involves the system structure - a choice
nLl* -II treatment processes to employ and the arrangement of these
Z,?H hi , h°Jh an°ther ln tf?e system' Examples of such structural choices
would be whether to use activated sludge or a trickling filter for BOD
removal, whether to employ a single treatment train for sludge processina
SDrP^^nVv^-H1'0!' Cerent types of sl^es, and whether to use land
spreading of liquid sludge or landfill a dewatered sludge. The second
type of decision relates to the design of each individual unit process and
operation. It specifies the values of those parameters that describe the
* KPeIu in£ 9haracten'st1cs» a^ performance of each unit. Examples
e ?li * I?6 °f-an overflow rate for a settling tank, the value of
h, in reten*lon ^me to use in an activated sludge unit, or the choice
between 10 cu. yd. and 15 cu. yd. size trucks for hauling sludge.
The identification of a best overall system design requires a search
over the space of feasible structural configurations and over the space
of design options associated with the individual treatment units in each
configuration. The fact that the treatment units are interconnected to
one another makes the performance of any one unit in the system dependent
on the design decisions made for all other units in the system. Thus
I?£hnXaniCle'.the best design of an activated sludge unit cannot be assured
without knowing the performance achieved in primary sedimentation and,
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because of the recycled sidestreams, the design choices in the sludge
processing train.
The evaluation of a system design must be made with respect to a set
of decision criteria. The criteria that seem most relevant to waste
treatment systems can usually be grouped into the categories of economics,
environmental effects, performance, and feasibility of implementation.
Table 1 lists the evaluation criteria that were used in the recent LA/OMA
sludge management study for Los Angeles and Orange Counties in California
[4]. The U.S. Congress, in the Clean Water Act of 1977, has set forth
several goals that could serve as the basis for design criteria. The act
provides monetary incentives for communities to employ treatment technology
that will result in (a) greater recycling and reuse of water, nutrients,
and natural resources; (b) increased energy conservation, reuse, and recycling;
(c) improved cost-effectiveness in meeting specific water quality goals;
(d) improved toxics management•[!].
Criteria such as described above may be expressed in the form of a
performance objective to be minimized or maximized, as constraint relations
with fixed target levels, or as fuzzy combinations of objectives and
constraints (e.g., design a system-that has some resource conservation in it
but is not too costly). Alternative designs that do not meet the constraint
target levels can be discarded as infeasible. Of the remaining designs, if
one ranks better in each objective than all other designs then this is
clearly the best choice. What is more likely to occur though is that one
design performs better than another with respect to one objective but is
inferior with regard to a second objective.
For example, the treatment system that minimizes cost would probably
not be the system that also minimizes energy consumption. In such cases
the objectives are said to be conflicting and a final choice cannot be made
without the designer imposing a subjective value judgment on the relative
worth of one objective versus another. For the cost-energy situation, the
designer would be faced with the question, "how much increase in cost am I
willing to accept as I move from the most cost-effective system to the
most energy-effective system?". Engineering analysis can only Inform the
designer of what trade-offs exist among the objectives in the alternative
designs available. It cannot answer questions involving value judgments
and thus cannot serve as the means for automatically reaching -a final
design decision.
The traditional approach to waste treatment system design has been to
divide the system into its various component stages (e.g., primary treatment,
secondary treatment, sludge stabilization, sludge dewatering, and final
sludge disposal), define performance objectives for each stage, and select
the treatment units that best accomplish these objectives. The result can
be an uncoordinated and wasteful overall system design. More systematic
approaches have recognized the interaction between various treatment
components and have used mathematical models programmed for computer
implementation to evaluate the overall performance of alternative system
designs. The pioneering effort in this area was made by Smith and his
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TABLE 1. EVALUATION CRITERIA FOR THE LA/OMA SLUDGE MANAGEMENT STUDY
Direct Cost
Capital Costs
Operation and Maintenance Costs
Revenues
Indirect Costs
Employment Generation
Induced Land Value Changes
Alterations in Economic Productivity
Energy Impacts
Direct Energy Demands
Indirect Energy Demands
Environmental Impacts
Public Health Hazards
Land Form Alteration
Soil Contamination, Conditioning, Reclamation
Water Quality
Air Quality
Ecosystem Impacts
Resource Utilization
Social Resources
Growth Inducement
Safety
Control of Hazardous Substances
Transportation System Impact
System Effectiveness
Implementability
Flexibility with Time
Reliability
Compatibility with Existing Land Use
Compatibility with Related Planning Programs
Compatibility with Legal Requirements
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co-workers at the U.S. Environmental Protection Agency [5]. This work has
culminated in the development of EXECUTIVE, a computer program that simulates
the steady state performance and evaluates the cost of a number of wastewater
and sludge treatment unit processes that can be arranged into any reasonable
system configuration [2]. Additional simulation programs devoted primarily
to wastewater treatment systems have been reported on by Silveston [6], Chen,
et. al. [7], and Shoemaker and Barkley [8]. Similar works devoted mainly to
sludge management include those of Bennet, et. al. [9], Kos, et. al. [10],
Smith and Eilers [11], Burley and Bayley [12], and the San Francisco Bay
Region [13].
All of the above approaches are evaluative in nature - they rely on
the designer to first specify the system design in advance. For a large
number of alternative designs the computational burden can become excessive
if each alternative-must be synthesized and analyzed separately. To
overcome this limitation, a number of mathematical optimization models have
been developed for waste treatment systems in recent years. With varying
degrees of generality and sophistication these models will select values
for the system design variables that will meet effluent discharge standards
at least total cost.
Table 2 compares a number of these models with respect to several
features thought to be of particular value in the preliminary phases of the
system design process. These features can be summarized as follows:
(1) The design of the wastewater and sludge treatment sub-systems should
be done in an integrated fashion, with consideration given to the
treatment requirements of the sidestreams produced during sludge
processing;
(2) The model should select both a system structure and the design of the
individual process units within that structure, at least from among a
finite number of discrete alternatives;
(3) The capability should exist to handle multiple pollutants or waste
stream parameters and their interactions;
(4) Whenever possible, principles of mass balance and reaction kinetics
should be employed in predicting process performance and resource
utilization;
(5) A capability to consider other kinds of system evaluation criteria
besides cost should be included;
(6) A solution method more computationally efficient than complete
enumeration of all possible system designs should be used.
None of the models in Table 2 contains all six features. It is possible
to discern two types of modeling approaches in these past efforts. The
first, representative of the dynamic programming models, can only synthesize
the wastewater treatment sub-system with respect to one or two pollutants
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using simple process performance relations. The second is representative of
the various nonlinear programming models. It requires that the system
structure first be specified in advance and then proceeds to optimize the
integrated design of the individual units within that structure. It is
capable of dealing with a complex system structure, multiple pollutants and
realistic process performance models. Although a recent paper by Adams
and Panagiotokapoulos [26] addresses many of these same concerns and suggests
an approach for dealing with them, detailed computational results are only
presented for a relatively simple design problem previously considered by
Shih and Krishnan [16].
The decision model described in this report, EXEC/OP, has been designed
to include the six features listed above. In addition, it is capable of
identifying the M system designs that are within X% of the best design,
where M and X are specified by the designer. This feature is especially
useful in sensitivity analysis. In multi-criteria studies, it can identify
system designs that are close to one another with regard to one criterion
(e.g., cost) but could have varying levels of other criteria (e.g., energy
consumption, land utilization). The emphasis has been placed on developing
a useful design tool, capable of generating attractive alternatives that
could warrant more critical evaluation, rather than producing an automated
procedure for arriving at an optimal design.
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SECTION 4
AN OVERVIEW OF THE SYSTEM SYNTHESIS MODEL
This section provides a description of the various conventions and
assumptions employed in the system synthesis model EXEC/OP. It also
discusses the initial steps needed to organize any given design problem
into a suitable form for EXEC/OP. Detailed instructions on the use of
the program can be found in Appendix B.
EXEC/OP views a waste treatment system as consisting of three treatment
trains - one for wastewater, one for secondary sludge, and one for primary
or mixed primary and secondary sludge. Figure 1 shows how these trains are
connected to one another. Note how the liquid sidestreams off of the sludge
treatment trains are recycled back to the wastewater treatment train. The
mixing of secondary sludge with primary sludge is shown with a dashed line
to indicate that the exact point of mixing will depend on the choice of
treatment units in the secondary sludge train.
Wastewater
Treatment Train
Secondary Sludge
Disposal Train
Primary/Combined
Sludge Disposal Train
P = Primary Sludge
S = Secondary Sludge
R = Recycled Sidestream
Figure 1. Conceptual flow diagram of a waste treatment system
10
-------
The building blocks of each treatment train are referred to as treat-
ment stages. For each stage the designer indicates the various unit
process options that are available for selection. These units can be
different types of treatment processes or operations (e.g., activated sludge
versus trickling filter), or different design levels of the same process
(e.g., activated sludge with mixed liquor volatile solids concentration of
2000, 2500, or 3000 mg/1). Table 3 lists the types of unit processes and
operations that are currently available to EXEC/OP. Each of these has a
performance model, in the form of an EXEC/OP subroutine, that will compute
effluent quality, sidestream quality, equipment size, and resource utiliza-
tion (cost, energy, land) as a function of the influent waste quality and
a set of process design parameters. Most of these subroutines have been
taken from the EPA EXECUTIVE program [2]. Appendix C lists the design
parameters associated with .each process, indicates which additional design
information is computed by the process model, and cites references for the
technical details of the models.
It is important to understand that the design level or performance of
each process option is fixed in advance by the analyst by assigning values
to these input design parameters. Thus, for example, if EXEC/OP should
choose gravity thickening over air flotation thickening at some sludge
treatment stage, it does so on the basis of the solids loading rate and
thickened solids concentration that was assumed for each type of thickener.
Of course the designer is free to employ several gravity and/or flotation
thickener options, each with different performance levels, to help refine
the selection.
Within each treatment train only a serial arrangement of processing
stages is possible. Parallel treatment units or recycling of waste streams
among the units of the same treatment train is not allowed. Thus a group
of process units that normally operates with a recycle flow, such as the
aeration basin and final settler of the activated sludge process, is treated
as a single unit with its own performance model and EXEG/OP subroutine.
Added flexibility for creating alternative configurations of units is provided
by means of the "null process" unit. 'This simply indicates that the choice
of no processing at a given stage is also a possibility. Examples illustrat-
ing the use of the null process alternative are shown in the various sample
design problems considered throughout this report.
Each treatment stage has an influent waste stream entering it, an
effluent stream leaving it for the next stagehand, in most cases, a side-
stream generated from the processing activity. For wastewater treatment
stages these sidestreams would be sludge streams while, for sludge treatment
stages they may be filtrates, supernatants, etc. Each sludge stream is
assigned to either the primary or secondary sludge treatment trains for
processing. Of course eventual mixing of these streams is possible depending
at what stage secondary sludge treatment ends. All sidestreams from sludge
treatment are sent to a designated stage in the wastewater treatment train.
The contents of each wastewater and sludge stream are modeled with the
parameters listed in Table 4. In addition to these, each sludge stream is
11
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TABLE 3. UNIT PROCESSES CONTAINED IN EXEC/OP
Wastewater Treatment
Sludge Treatment
Raw Wastewater Pumping
Preliminary Treatment
Primary Sedimentation
Aeration and Final Settler
(Activated Sludge)
Primary Sedimentation, Aeration,
Final Settler with waste activated
sludge returned to the primary settler
Trickling Filter
Rotating Biological Contactor
Chlorination
Gravity Thickening
Air Flotation Thickening
Anaerobic Digestion
Aerobic Digestion
Nonoxidative Heat Treatment
Elutriation
Sand Drying Beds
Vacuum Filtration
Centrifugation
Multiple Hearth Incineration
Truck Transport/Land Spreading
Truck Transport/Landfill ing
Sludge Holding Tanks
12
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TABLE 4. WASTE STREAM PARAMETERS IN EXEC/OP
Q
SOC
SNBC
SON
SOP
SFM
SBOD
VSS
TSS
DOC
DNBC
DN
DP
DFM
ALK
DBOD
NH3
N03
Volumetric Flow, mgd
Suspended Organic Carbon, mg/1
Suspended Nonbiodegradable Carbon, mg/1
Suspended Organic Nitrogen, mg/1
Suspended Organic Phosphorus, mg/1
Suspended Fixed Matter, mg/1
Suspended 5-Day BOD, mg/1
Volatile Suspended Solids, mg/1
Total Suspended Solids, mg/1
Dissolved Organic Carbon, mg/1
Dissolved Nonbiodegradable Carbon, mg/1
Dissolved Nitrogen, mg/1
Dissolved Phosphorus, mg/1
Dissolved Fixed Matter, mg/1
Alkalinity, mg/1
Dissolved 5-Day BOD, mg/1
Ammonia Nitrogen, mg/1
Nitrate Nitrogen, mg/1
13
-------
characterized as to its origin (primary, secondary, or mixed primary and
secondary) and the type of stabilization it receives (no stabilization, lime
stabilization, digestion, digestion plus elutriation, and heat treatment)
This feature allows many of the sludge treatment processes to be assigned
different design parameter values depending on the type of sludge handled.
m In summary, the preliminary steps needed to organize a design problem
into a format acceptable to EXEC/OP are as follows:
(1) Determine the number of treatment stages to employ in the wastewater
and sludge treatment trains;
(2) Decide on what process options and design parameter values to use
at each treatment stage;
(3) Assign each sludge sidestream generated from wastewater treatment to
either the primary or secondary sludge treatment train;
(4) Determine to which wastewater treatment stage the sidestreams from
sludge treatment should be recycled.
In practice it will probably be necessary to perform steps 1 and 2
simultaneously to insure that the desired options are considered in the
proper order.
Figure 2 illustrates what may be the result of applying these prepara-
tory steps to a hypothetical design problem. Noteworthy features of this
example as as follows:
(1) Several design options for the same process are included at stages 3,
T', y, and 12;
(2) Sludge from primary sedimentation is sent to the primary sludge train
while sludge from the activated sludge unit is sent to the secondary
sludge train; J
(3) The null process option is used in several places to increase the
number of possible system configurations;
(4) The mixing point for secondary and primary sludges will depend on at
what stage secondary sludge treatment ends. E.g., if the null process
is chosen by EXEC/OP at stages 6 and 7, mixing occurs at stage 8 If
the null process is chosen at stage 7 but not at stage 6, then stage 9
becomes the mixing point;
(5) The sidestreams generated from sludge processing are returned to
stage 3.
14
-------
Raw Wastewater
Pumping
Preliminary
Treatment
i »*
Primary
Sedimentation 1
Primary
Sedimentation II
1 — ^-
Activated Sludge 1
Activated Sludge II
Activated Sludge IV
Chlorination
T*-
| ' g»
Null Process
Flotation
Thickening
8
Null Process
Gravity
Thickening
(R)-*-
Null Process
Aerobic* •—
Digestion
(R)
9
Null Process
Lime Stabilization
Anaerobic Digestio
Aerobic Digestion
»n
1
1
1
1
1
1
nl
10
Null Process
Gravity Thickening
Elutriation
vr/
11 12
Null Process
Vacuum Filtration
Centrifugation
Sand Dryina Beds
-»-
Truck Transport
& Landfilling
Truck Transport
& Land Spreading 1
Truck Transport
& Land Spreading II
Multiple Hearth
Incineration
Figure 2. Multi-option flow diagram for a hypothetical design problem
Once a multi-option flow diagram such as Figure 2 has been established,
EXEC/OP can be used to select the process option at each stage of the system
that will best meet a particular set of design criteria. The current
version of EXEC/OP contains eight criteria. They are as follows:
(1) Initial construction cost (million dollars);
(2) Annual operation and maintenance costs, including all energy costs
(dollars/million gallons of flow treated);
(3) Total equivalent annual cost consisting of amortized capital costs
plus annual operation and maintenance costs (dollars/million gallons
of flow treated);
(4) Gross energy consumption consisting of the direct electrical energy
needed to operate equipment, the kwh equivalent of all fuel consumed,
and the kwh equivalent of the energy used in chemical production
(kwh/million gallons of flow treated);
(5) Gross energy production as the kwh equivalent of the usable energy
contained in treatment by-products such as digester gas and incinerator
exhaust gas (kwh/million gallons of flow treated);
15
-------
(6) Net energy consumption which is the difference between criteria 4 and 5
(kwh/million gallons of flow treated);
(7) Total land utilization, excluding those process units with small land
requirements (acres);
(8)
Subjective system undesireability rating,
The first seven criteria should be self-explanatory. The system
undesireability rating is arrived at in the following way. Each process
option is given a rating by the designer on some convenient scale, say
u to 10. The higher this rating, the more undesireable the process from
whatever standpoint the designer wishes to view it. For example, anaerobic
digestion may be rated as 10 and truck transport/land application of sludge
be rated at 3 on the basis of reliability. If public acceptance were the
lusi!el ?he?e scores might be reversed. Scoring systems can also be devised
that take into account several forms of undesireable effects. The total
system score is simply the sum of the unit process scores for those options
selected in the system design. This is an admittedly crude attempt to
incorporate qualitative factors into the screening process. Obviously the
designer should use this feature with considerable caution to insure that
treatment options are being compared on an equitable and acceptable basis.
The heating value of all fuels is converted into an equivalent
electrical energy rating by using an assumed conversion efficiency figure
This figure reflects the thermodynamic efficiency of converting heat energy
into electrical energy. Typical values would be from 8,500 BTU/kwh (40 per-
cent conversion efficient) to 11,300 BTU/kwh (30 percent conversion
efficiency).
The criteria listed above can be combined into a weighted objective
function whose value is to be minimized or can be assigned target limits
and treated as constraint conditions by EXEC/OP. The objective function
would have the form
v =
- W5C5
ws c6 + w7 c7 + w8
where the w^ 1=1,..., 8 are weighting coefficients chosen by the designer
and the c-, i=l,..., 8 are the individual criteria values. Note that w. c.
is given d negative value since energy production is to be maximized, fhe5
weights should reflect the relative value that each criterion should contri-
bute to deciding which system design is "best". For example, if reducing
total cost were thought to be twice as important as reducing gross enerav
consumption, then the values of w~ and w.
simply minimizing total cost, one°would S
should be in the ratio 2-1
et w
For
= 1.0 and all other w. = 0
i
Another use for these weights would be to assign a cost credit to any
energy produced through waste treatment. For example, if the value of such
energy net of its conversion cost was $0.011/kwh and one was trying to
minimize total cost then w3 = 1.0 and wg = 0.011 with all other w. = 0
16
-------
Note that the cost of energy consumption is accounted for in the cost
criteria 2 and 3.
When the above criteria are considered as constraints, each is
assigned an upper limit (or lower limit for energy production) which
cannot be exceeded by any feasible system design. An additional set of
constraints consists of the required effluent quality of the wastewater
discharge. EXEC/OP can consider effluent standards for 5-day BOD, total
suspended solids, phosphorus, ammonia nitrogen, and nitrate nitrogen. Limits
on discharges of residuals to the land and air can appear as part of the
design parameter data for the individual process units. For example, the
process model for land application of sludge asks the designer to supply an
allowable nitrogen loading rate in Ib N/acre.
EXEC/OP can operate in- two different modes of calculation. In the
optimization mode it selects the combination of process options that will
best meet the stipulated design criteria. In this mode the program can
also be asked to identify the M next-best designs that are within U of
best, where M and X are chosen by the designer. The single design evaluation
mode allows the user to obtain a detailed description of the performance of
a particular system design. This includes the composition of all wastewater
and sludge streams and the values of all computed unit process design
parameters - information that is not available from the optimization mode
because it would require a prodigious amount of computer output. The same
input data is used for both modes except that the last lines of data for a
single design evaluation lists the process option to be selected at each
stage of the system.
The computational algorithm of EXEC/OP employs two basic features. The
first is the replacement of the'recycle stream from the sludge treatment
trains by a vector of penalty terms. These penalties approximate the change
in the value of each design criterion as a unit of .mass flow of each com-
ponent of the sidestreams from sludge treatment is returned for treatment
in the wastewater treatment train. This device converts the entire treatment
system into a serial one, where the waste stream entering stage i is only
affected by the decisions made at stages 1, 2 i-1.
The second computational feature is an implicit enumeration algorithm
that is used to find the .optimal unit process choices for the penalty-
augmented serial system. It is able to screen out large categories of
possible process combinations by employing a simple bounding property. I he
result is that only a small fraction of the total number of system configura-
tions need to be explicitly evaluated.
Since the values of the recycle penalties depend on the choice of
unit processes in the system, an iterative procedure is employed to update
the values of these penalties. After each iteration has identified a new,
potentially optimal system design by implicit enumeration, the units
selected for that design are used to establish new penalty values and-
another iteration begins. The process stops when a previously generated
system design is once again arrived at. The final design is taken as the
17
-------
ss
a
The use of recycle penalties is a heuristic device and thus thPrP ic
no guarantee that EXEC/OP will be able to identify the true mathematfca
optimum design However the error involved would most certainly be sma 1
since the recycle stream from sludge processing usually represents onT?
JhpT?°J Kf .th,6 *°tal !?aSte Ioad1ng on the s"stem' Also! the sea?chyfor
dSJlSn X ^ ^ f?19"5 SaS6d °n Pena1ty values ma* be able to identify a
design that actually performs better than the best design arrived at in the
optimization portion of the algorithm. An example of this is shown in one
nf 22 "s?.s*udles Presented in the next section. Finally, gi Jen the nature
of the _ preliminary screening process, the designer is less interested in
obtaining a mathematical optimum than in generating a set of attractive
^^ Clea^ ^^or. EXEC/OP was
18
-------
SECTION 5
CASE STUDIES
This section demonstrates the capabilities of EXEC/OP by means of
several system design case studies. It should be understood that the
problems analyzed are purely hypothetical. The values used for the process
design parameters were specifically chosen to make several processes com-
petitive with one another. Therefore, no general conclusions regarding
the general superiority of one type of process versus another should be
drawn from these examples.
The case studies to be presented were chosen to illustrate the following
types of design problems:
(1) Cost minimization of a conventional secondary treatment system with
the emphasis on the choice of sludge management strategies;
(2) A cost minimization of secondary treatment featuring several non-
conventional arrangements of primary sedimentation and activated
sludge units;
(3) Refinement of the design for Case Study 1 with the emphasis on the
choice of design parameter values for a fixed arrangement of process
units;
(4) A detailed performance evaluation of the design arrived at in Case
Study 3;
(5) A cost/energy-effectiveness analysis for the process options considered
in Case Study 1.
All of the case studies are for a 10 mgd system whose influent waste-
water characteristics are given in Table 5. Values assumed for various
economic parameters are shown in Table 6. All examples must provide an
effluent quality of 30 mg/1 of 5-day BOD and 30 mg/1 of suspended.sol ids.
CASE STUDY 1
In this example, the least cost combination of process units will be
found for the multi-option flow diagram in Figure 3. In addition, the
four system designs closest to least-cost will also be sought. Note that
several choices of primary sedimentation and activated sludge design
levels are available. Separate thickening and/or aerobic digestion of
19
-------
TABLE 5. INFLUENT WASTE CHARACTERISTICS USED FOR CASE STUDIES
Component
Value
Volume flow, mgd
Suspended organic carbon, mg/1
Suspended nonbiodegradable carbon, mg/1
Suspended organic nitrogen, mg/1
Suspended organic phosphorus, mg/1
Suspended fixed matter, mg/1
Suspended 5-day BOD, mg/1
Volatile suspended solids, mg/1
Total suspended solids, mg/1
Dissolved organic carbon, mg/1
Dissolved nonbiodegradable carbon, mg/1
Dissolved nitrogen, mg/1
Dissolved phosphorus, mg/1
Dissolved fixed matter, mg/1
Alkalinity, mg/1
Dissolved 5-day BOD, mg/1
Ammonia nitrogen as N, mg/1
Nitrate nitrogen as N, mg/1
10. (37,850 cu m/day)
105.
30.
10.
2.
30.
140.
224.
254.
43.
11.
19.
4.
500.
250.
60.
15.
0.
20
-------
TABLE 6. ECONOMIC PARAMETERS USED FOR CASE STUDIES
EPA Sewage Treatment Plant Cost Index
Wholesale Price Index
Discount Rate
Planning Period
Direct Hourly Wage
Fraction of Direct Hourly Wage
Charged to Indirect Labor Costs
Cost of Electricity
Cost Escalator for Yardwork, Laboratories,
Legal Fees, Engineering and Interest
Efficiency of Converting Heat Value of
Fuels to Equivalent Electrical Energy
2.88 (December 1977)
2.00 (December 1977)
i
0.06375
20 yr.
5.91 $/hr.
0.15
0.033 $/kwh
1.33,
0.31
21
-------
waste activated sludge is also possible. Ultimate sludge disposal can be
accomplished by either land spreading at one of two alternative sites,
landfill ing, or incineration. The process options shown in Figure 3 can
be arranged into 15,360 different system configurations.
Ran Waslonater
Pumping
• »i
Preliminary
Treatment
1 >.
Primary
Sedimentation 1
Primary
Sedimentation II
— ff
Activated Sludge 1
Activated Sludge II
Activated Sludge III
Activated Sludge IV
Chlorination
]r
i
i
.*.
Null Process
Flotation
Thickening
8
Null Process
Gravity
Thickening
-r-
I
i
1
1
Null Process
Aerobic*
Digestion
Ol)
9
Null Process
Lime Stabilization
Anaerobic Digestion 1
Anaerobic Digest!
Aerobic Diaestion
^-^
on II
*-l
1
'
(P)
10
Null Process
Gravity Thickening
Elutriation
(S)
11
Null Proc
Vacuum Filtration
Sand Drying Beds
12
Truck Transport
& Landfilling
& Land Spreading 1
Truck Transport
Si Land'Spreading II
Multiple Hearth
Incineration
Figure 3. Multi-option flow diagram for case study 1
Table 7 provides information on the input design parameters for the
unit process options. Additional data for the vacuum filtration and centri-
fugation options are given in Table 8. Since this example seeks to minimize
total cost, the only non-zero selection criterion weight is for criterion 3,
total cost. All constraint limits on the various criteria have been set to
arbitrarily high numbers (or to 0 for energy production).
The results of running this problem with EXEC/OP are shown in Table 9.
The first portion of the table lists the designs arrived at for each itera-
tion of the optimization phase of EXEC/OP. These are then followed by a
listing of the 5 least-cost designs identified in the sensitivity phase of
the program. These results show that three iterations were needed on the
penalty terms to obtain the least-cost design. It consists of using 60
percent solids removal in primary sedimentation, activated sludge at 3000 mg/1
mixed liquor volatile solids and 30 percent recycle, anaerobic digestion of
mixed primary and secondary sludge for 15 days, thickening to 5 percent
solids, sludge drying on sand beds, and incineration .of dried sludge. The
total cost is 27.6 <£/1000.gal. The somewhat surprising result of choosing
sludge incineration over land disposal may be due to the high cost of land,
22
-------
TABLE 7. INPUT PROCESS DESIGN PARAMETERS FOR CASE STUDY 1
PRI = primary sludge
Process
WAS = waste activated sludge
Design Parameter
MIX = PRI + WAS
Value
Pumping
Preliminary Treatment
Primary Sedimentation I
Primary Sedimentation II
Activated Sludge Ia
Activated Sludge II
Activated Sludge III
Activated Sludge IV
Chlorination
Air Flotation
Thickening
Gravity Thickening
Pumping Head, Ft.
Grit Removal
Flow Measurement
Screening
TSS removal, %
TSS removal, %
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
Chlorine dosage, mg/1
Contact time, min.
Solids recovery ratio
Underflow TSS,.%
Loading, Ib/day/sq ft
Solids recovery ratio
Underflow TSS,
Loading, Ib/day/sq ft
30.0
Yes
Yes
Yes
40.0
60.0
2000.0
0.3
2000.0
0.5
3000.0
0.3
3000.0
0.5
8.0
30.0
0.95
4.0
48.0
0.9
8.0 (PRI)
5.0 (MIX)
16.0 (PRI)
8.0 (MIX)
aAll activated sludge alternatives are also required to attain an effluent
quality of 30 mg/1 BODg and 30 mg/1 TSS.
(continued)
23
-------
TABLE 7 (continued)
Process
Design Parameter
Value
Anaerobic Digestion I
Anaerobic Digestion II
Aerobic Digestion
Lime Stabilization
Elutriation
Vacuum Filtration
Centrifugation
Sand Drying Beds
Incineration
Land Spreading I
Land Spreading II
Landfill ing
Detention time, days
Detention time, days
Detention time, days
Dosage, Ib/ton dry solids
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq ft
Washwater ratio
Loading, gph/sq ft
Chemical dosage, %
Solids recovery ratio
Cake solids, %
Feed rate, gpm
Cake solids %
Storage detention time, days
Mass loading, Ib/hr/sq ft
Heat value of volatiles, BTU/lb
Type of fuel
One way haul distance, miles
Cost of land, $/acre
N application rate, Ib/acre/yr
Site preparation cost, $/acre
Spreading cost, $/dry ton
One way haul distance, miles
Cost of Iand3 $/acre
N application rate, Ib/acre/yr
Site preparation cost, $/acre
Spreading cost, $/dry ton
One way haul distance, miles
Cost of land, $/acre
15.0
20.0
10.0 (WAS)
20.0 (PRI & MIXl
200.0
0.76
4.0
8.0
3.0
10.0
See Table 8
See Table 8
30.0
15.0
2.0
10000.0
oil
10.0
3000.0
400.0
500.0
10.0
30.0
2000.0
600.0
500.0
10.0
10.0
3000.0
24
-------
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the low cost of dewataring on sand beds, the rather severe nitrogen limita-
tion on the nearby sludge spreading site, and the absence of any air
pollution controls on the incinerator. Note that designs 2 and 3 of the
sensitivity phase are only 0.7 percent (0.2 <£/1000 gal) more expensive.
Design 3 uses landfill ing instead of incineration and design 2 employs land
spreading of liquid sludge.
It is interesting to observe the effect of the recycles from sludge
treatment on the design choices in this problem. Had no account been taken
of these recycles, design 1 of the optimization phase would have been desig-
nated as least-cost. Note that its true cost is 29.1 tf/1000 gal. which is
5.4 percent more expensive than design 2.
The total computational effort for arriving at the results in Table 9
was estimated as being only 5.5 percent of that needed to evaluate all
15,360 alternatives one at a time. The solution time for this problem was
approximately 360 seconds on a DEC PDP-11/70 computer.
CASE STUDY 2
The multi-option flow diagram for this example is shown in Figure 4.
It represents an activated sludge system that features the options of
returning the waste activated sludge to the primary clarifier or not using
Primary Sedimentation
& Activated Sludge I
Primary Sedimentation
& Activated Sludge II
Primary Sedimentation
& Activated Sludge III
Primary Sedimentation
& Activated Sludge IV
Null Process
Activated Sludge I
Activated Sludge II
Activated Sludge III
Activated Sludge IV
Null Process
Chlorination
Figure 4. Multi-option flow diagram for case study 2
28
-------
any primary sedimentation. Several different design levels for the activated
sludge units have been considered. Note how, the "null process" is used
at stages 3 and 4 to allow a choice of one or the other of the two types
of activated sludge options. The values of the input design parameters
for the unit process options are given in Table 10. The options shown
in Figure 4 can be arranged into 21,600 different systems. Once again
EXEC/OP will be used to estimate the top five least-cost designs.
The results for this study are displayed in Table 11. Note that
one of the "next best" designs (number 1) is actually lower in cost (by
only 0.5 percent) than the best design found in the optimization phase
(number 2). This is a result of the heuristic nature of the optimization
algorithm of EXEC/OP and it demonstrates another advantage to using the
next-best design feature of the program.
The least-cost design employs activated sludge at a mixed liquor
volatile solids concentration of 3000 mg/1 and a recycle ratio of 0.3
with the waste activated sludge returned to the primary sedimentation
tank. Sludge processing consists of anaerobic digestion, gravity thickening,
sand drying, and incineration. Total cost is 27.8 <£/1000 gal. As
mentioned above, substitution of landfill ing for incineration increases
costs by only 0.5 percent to 27.9
-------
TABLE 10. INPUT PROCESS DESIGN PARAMETERS FOR CASE STUDY 2
PRI = primary sludge
Process
WAS = waste activated sludge
Design Parameter
MIX = PRI + WAS
Value
Pumping
Preliminary Treatment
Primary Sedimentation
Activated Sludge Ia
Activated Sludge II
Activated Sludge III
Activated Sludge IV
Chiorination
Air Flotation Thickening
Aerobic Digestion
Gravity Thickening
Anaerobic Digestion
Lime Stabilization
See Table 7
See Table 7
TSS removal, %
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
See Table 7
See Table 7
Detention time, days
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq. ft.
Detention time, days
See Table 7
(continued)
30
50.0
2000.0
0.3
2000.0
0.5
3000.0
0.3
3000.0
0.5
10.0
0.9
2.0 (WAS)
5.0 (MIX)
6.0 (WAS)
8.0 (MIX)
15.0
effluent
-------
TABLE 10 (continued)
Process
Design Parameter-
Value
Elutriation
Vacuum Filtration
Centrifugation
Sand Drying Beds
Land Spreading
Landfill ing
Incineration
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq. ft.
Washwater ratio
Loading, gph/sq. ft.
Chemical dosage, %
Solids recovery ratio
Cake solids, %
Feed rate, gpm
See Table 7
One way haul distance, miles
Cost of land, $/acre
N application rate, Ib/acre/yr
Site preparation cost, $/acre
Spreading cost, $/dry ton
See Table 7
See Table 7
0.76
2.0 (WAS)
4.0 (MIX)
(WAS)
(MIX)
6.0
8.0
3.0
10.0
See Table 8
See Table 8
10.0
3000.0
400.0
500.0
10.0
31
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TABLE 12. INPUT PROCESS DESIGN PARAMETERS FOR CASE STUDY 3
Process
Pumping
Preliminary Treatment
Primary Sedimentation I
Primary Sedimentation II
Primary Sedimentation III
Activated Sludge Ia
Activated Sludge II
Activated Sludge II
Activated Sludge III
Activated Sludge IV
Activated Sludge V
*
Activated Sludge VI
Activated Sludge VII
Activated Sludge VIII
a
Design Parameter
See Table 7
See Table 7
TSS removal , %
TSS removal , %
TSS removal, %
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
MLVSS, mg/1
Recycle ratio
Value
60.0
50.0
55.0
3000.0
0.3
2500.0
0.3
2500.0
0.3
3500.0
0.3
2500.0
0.35
3000,0
0.35
3500.0
0.35
2500.0
0.25
3000.0
0.25
All activated sludge alternatives are also required to attain
quality of 30 mg/1 BODg and 30 mg/1 TSS
(continued)
34
an effluent
-------
TABLE 12 (continued)
Process
Design Parameter
Value
Activated Sludge IX
Chlorination
Anaerobic Digestion I
Anaerobic Digestion II
Anaerobic Digestion III
Gravity Thickening I
Gravity Thickening II
Gravity Thickening III
Sand Drying Beds
Incineration
MLVSS, mg/1
Recycle ratio
See Table 7
Detention time, days
Detention time, days
Detention time, days
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq. ft.
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq. ft.
Solids recovery ratio
Underflow TSS, %
Loading, Ib/day/sq. ft.
See Table 7
See Table 7
3500.0
0.25
15.0
12.0
17.0
0.9
5.0
8.0
0,9
4.0
12.0
0.9
7.0
5.0
aAll activated sludge alternatives are also required to attain an effluent
quality of 30 mg/1 BODg and 30 mg/1 TSS
35
-------
[R
Raw VYastowater
Pumping
^
Preliminary
Treatment
I
Primary Sedimentation 1
Primary Sedimentation II
Primary Sedimentation III
Activated Sludge I
Activated Sludge II
Activated Sludge IX
Chlorination
Null Process [». Null Process |
Null Process
Anaerobic Digestion I
Anaerobic Digestion II
Anaerobic Digestion III
Gravity Thickening 1
Gravity Thickening II
Gravity Thickening III
(FiV«
*-| Sand Drying Beds
Figure 5. Multi-option flow diagram for case study 3
The significant input design parameter values associated with the unit
process options are given in Table 12. The resulting least-cost selection
arrived at by EXEC/OP is shown in Table 13. Since only the single least-cost
design is desired there is no sensitivity phase listing for this example.
Only two iterations on the recycle penalty values were needed to reach a
design whose total cost is 26.95 if/1000 gal. This gives only a 2.4 percent
savings over the initial design. The improved design indicates that one
should try to remove as much solids as possible in primary sedimentation,
use the highest solids concentration and lowest recycle ratio in activated
sludge, use the minimum amount of digestion time and thicken to the greatest
extent possible. The CPU time required for this problem was 26.63 seconds
on the DEC POP 11/70.
TABLE 13, LEAST-COST DESIGN FOR CASE STUDY 3
Wastewater Process
isign Selections
Sludge Process
Selections
Total Cost
-------
The results of this case study leads one to speculate on the relative
importance of system structural choices versus individual unit design
choices. It may be that in general the former are more critical. This
would follow as a result of the fact that the acceptable ranges of the
design parameters for most processes are usually fairly small and, as borne
out in this example, the total system resource utilization will be relatively
insensitive to adjustments in the design of a single process unit.
CASE STUDY 4
This example demonstrates the single design evaluation feature of
EXEC/OP. Detailed performance is to be obtained for the least-cost design
arrived at in Case Study 3. This simply requires that the EXEC/OP input
data for Case Study 3 be re-run with the addition of two lines of data that
specify the choice of process options at each stage of the system to be
analyzed.
The results of running EXEC/OP on this problem are shown in the printout
reproduced in Figure 6. Summaries of the process options, the selection
criteria, and the economic parameters are first presented. Note that each
process option is identified by a user assigned option number, a standard
process code number, and the number of the stage at which the process.
appears. Next there appears a listing of the criterion values associated
with the process option chosen at each stage of the system. This is followed
by a detailed listing of the input and computed output design parameter
values and the composition of the influent, effluent, and sidestream waste
streams for each process. For reasons of programming economy, the process
design data are listed in the order in which they are specified for each
process description given in Appendix C without any additional explanatory
headings. The headings on the waste stream constituents correspond to the
abbreviations used in Table 4.
CASE STUDY 5
The examples presented up until now have all been single objective
design problems, i.e., minimize total cost. In this case study the conflict-
ing objectives of minimizing cost and minimizing energy consumption will
be considered. It is shown how EXEC/OP can be used to efficiently identify
those system designs that offer meaningful trade-offs between these two
objectives. The implication behind a cost/energy-effectiveness analysis is
that the market price of energy or fuel does not represent its true social
value as a scarce resource and that the least-cost design will not also be
the least-energy design.
The set of design options to be used in this example is the same as
used in Case Study 1 (see Figure 3). The analysis is performed by making a
series of runs with EXEC/OP in which total cost is minimized while energy
consumption is constrained not to exceed a specified target level. As the
target level is varied, systems with different cost-energy combinations
will be generated. None of these designs will be inferior in the sense that
there will exist some other design that has both lower values of cost and
37
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energy. It is only from this reduced, non-inferior set of alternatives
that trade-offs between cost and energy need to be made to arrive at a final
design decision [27].
The concept of the non-inferior or efficient set of alternatives in
a multi-objective design problem is graphically illustrated in Figure 7.
This figure considers a case where there are only ten feasible system designs
and plots the position of each alternative on a set of cost-energy axes.
Consider a comparison between alternative A and alternative B. No meaningful
trade-off exists since B dominates A in both cost and energy. A is said to
be inferior to B and can be discarded from the decision making process,
providing that cost-and energy-effectiveness are the only decision criteria.
The non-inferior or efficient set of designs are those that are not inferior
to any other design. By inspection we see that alternatives B, D, F, and
H form the non-inferior set for Figure 7. All other alternatives could be
ignored.
500
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Figure 7. Illustration of the non-inferior set
45
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For the ten designs considered in Figure 7 it was possible to identify
the non-inferior set by inspection. Had there been a larger number of
alternatives and objectives a more systematic procedure would be needed.
The constraint method, as described above, wherein cost is minimized while
energy is constrained not to exceed various target levels, is one such
procedure. Thus in Figure 7, if we minimize cost and constrain energy to
not exceed 150 kwh/day we obtain alternative H; for energy constrained any-
where between 150 and 220 kwh/day alternative F is identified; and so on
Of course a final choice between systems B, D, F, and H would depend on the
designer s subjective preferences regarding the relative value of the cost
and energy figures for these designs. Although the concept of non-inferiority
was demonstrated here for only two objectives it also applies to higher
dimensional problems as well.
Referring back to the process options of Figure 3, EXEC/OP was used to
perform a cost/energy-effectiveness analysis for two different conditions:
a) no energy recovery was practiced and b) digester gas was converted into
electricity with 31 percent efficiency at a net credit of 0.011 $/kwh (the
commercial price of electricity minus an assumed conversion cost of 0.022
$/kwh). In both cases the same EXEC/OP input data as in Case Study 1
(Tables 7 and 8) was used with the following exceptions. With no energy
recovery, the value of the constraint limit on gross energy consumption was
reduced from one run to the next to obtain a series of least-cost designs
under progressively tighter energy constraints. A similar procedure was
followed for the case of digester gas recovery except that the weighting
coefficient for energy production was set equal to the cost credit of 0.011
$/kwh and the net energy consumption constraint limit was reduced at each
successive run of EXEC/OP.
As an example of the type of results obtained from this procedure,
Table 14 summarizes the EXEC/OP output for the case where net energy con-
sumption (with digester gas recovery) was constrained to be at or below 850
kwh/mil. gal. Note that the first design arrived at in the optimization
phase is infeasible because its net energy consumption is 911 kwh/mil. gal.
The best design is found to be number 2. In comparison with the least-cost,
energy unconstrained design it substitutes landfilling for incineration of
sludge, thus reducing net energy consumption from 888 kwh/mil. gal. to 806
kwh/mil. gal. Note that the objective function values for this design equals
the total cost of the system, 27.76 tf/1000 gal. minus the energy credit of
0.011 $/kwh times 418.9 kwh/mil. gal. (the energy content of the digester
gas), or 0.46 <£/1000 gal. giving a total of 27.3 <£/1000 gal.
The results of the cost/energy-effectiveness analysis are summarized
in Table 15. All of these non-inferior designs utilize the same wastewater
treatment - 60 percent solids removal in primary sedimentation and activated
sludge with 3000 tng/1 MLVSS and 30 percent recycle. As an example of the
kinds of trade-offs that these alternative systems present, consider those
with no energy recovery. Table 15 shows that a maximum energy reduction of
23 percent can be realized at a 16 percent increase in system cost by going
from system la to system 5a. A more attractive trade-off may be with
system 2a which offers a 6.3 percent energy reduction for less than a one
46
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CO tO S-
1^ i- o
et Q- CO
CU
•P
O
48
-------
percent increase in cost. Of course the final choice will depend on the
designer's feelings regarding the relative importance of these objectives
as well as any other criteria that are relevant to the design process.
The computer runs made for this case study averaged about 2.5 minutes
of CPU time each on a DEC PDF-11/70 computer. From three to four iterations
on the penalty terms for sludge treatment recycle streams per run were needed.
In addition, each run was asked to identify the five most,least-costly solu-
tions. It is estimated that on the average, the computatlona effort required
of each run was only 2.5 percent of that needed to evaluate all 15,360
possible system arrangements individually.
Hopefully these case studies have shown how EXEC/OP can be used as a
practical tool for the preliminary synthesis of waste treatment systems.
Perhaps its most productive use would be to generate alternative system
designs that emphasize different objectives in various degrees. It^can also
be used to explore the sensitivity of these designs to uncertainty in various
process performance parameters. The information derived from such analyses
would ultimately enter into .the value trade-off process that culminates in
a final design decision.
49
-------
REFERENCES
1. Eilers, R. G., et al., "Applications of Computer Programs in the
Preliminary Design of Wastewater Treatment Facilities -
Section II", EPA-600/2-78-185b, U. S. Environmental Protection
Agency, Municipal Environmental Research Laboratory, Cincinnati,
Ohio (1978).
2. Hendry, J E., Rudd, D. F. and Seader, J. D., "Synthesis in the Design
of Chemical Processes", AIChE Jour., 19, 1 (1973).
3. "LA/OMA Project Phase I Report", Regional Wastewater Solids Management
Program, Los Angeles/Orange County Metropolitan Area, August (1976).
4. "Clean Water Act of 1977", Public Law 95-217, United States Congress,
\'y//)•
5. Smith, R., "Preliminary Design and Simulation of Conventional Wastewater
Renovation Systems Using the Digital Computer", Water Pollution
Control Research Series Pub. No. WP-20-9, U.S. Department of
Interior, Federal Water Pollution Control Administration,
Cincinnati, Ohio (1968).
6. Silveston, P. L., "Digital Computer Simulation of Waste Treatment Plants
Using the WATCRAP-PACER System", Water Poll. Control. 69., 686 (1970).
7. Chen, G. K., Fan, L. T. and Erickson, L. E., "Computer Software for Waste
Water Treatment Plant Design", Jour. Water Poll. Control Fed., 44,
/ T"O \ 1 ./ / £ J •
8. Shoemaker, T. E. and Barkley, W. A., "Interactive Computer Design of
Wastewater Plants," Jour. Environ. Eng. Div.. Proc. Amer Soc
Civil Engr., 103, 919 (1977). ' '
9. Bennet, E. R., Rein, D. A., and Linstedt, K. D., "Economic Aspects of
Sludge Dewatering and Disposal", Jour. Environ. Eng., Div., Amer
Soc. Civil Engr., 99_, 55 (1973). '
10. Kos, P., Meier, P. M. and Joyce, J. M., "Economic Analysis of the
Processing and Disposal of Refuse Sludges", EPA-670/2-74-037,
U.S. Environmental Protection Agency, National Environmental
Research Center, Cincinnati, Ohio (1974).
50
-------
11. Smith, R. and Eilers, R. G., "Computer Evaluation of Sludge Handling
and Disposal Costs", Proceedings of the 1975 National Conference
on Municipal Sludge Management and Disposal, Anaheim, California,
August 18-20 (1975).
12. Burley, M. J. and Bayley, R. W., "Sludge Disposal Strategy: Processes
and Costs", Hater Poll. Control, 76, 205 (1977).
13. San Francisco Bay Region Wastewater Solids Study, "Screening of Alterna-
tives", Task Report for Task 3-4.7, Oakland, California (1978).
14. Lynn, W. R., et al, "Systems Analysis for Planning Wastewater Treatment
Plants", Jour. Mater Poll. Control Fed., 34, 565 (1962).
15. Evenson, D. E., et al., "Preliminary Selection of Waste Treatment Systems",
Jour. Water Poll. Control Fed.. 41_, 1845 (1969).
16. Shin, C. S. and Krishnan, P., "Dynamic Optimization for Industrial Waste
Treatment Design", Jour. Water Poll. Control., 41, 1787 (1969).
17. Shih, C. S. and DeFilippi, J. A., "System Optimization of Waste
Treatment Plant Process Design", Jour. San. Eng. Div., Proc.
Amer. Soc. Civil Engr., %_, 402 (1970).
18. Ecker, J. G. and McNamara, J. R., "Geometric Programming and the
Preliminary Design of Industrial Waste Treatment Plants",
Waste Res. Research, 7_, 18 (1971).
19. Berthouex, P. M'. and Polkowski, L. B., "Optimum Waste Treatment Plant
Design Under Uncertainty", Jour. Water Poll. Control Fed.. 42,
1589 (1970).
20. Mishra, P. N., et al., "Biological Wastewater Treatment System Design,
Part I. Optimal Synthesis", Canad. Jour, of Chem. Engrg., 51_
694 (1973).
21. CIRIA, "Cost-Effective Sewage Treatment - The Creation of an Optimizing
Model", CIRIA Report 46, Construction Industry Research and
Information Association, London, Gt. Brit. (1973).
22. Bowden, K., et al., "Evaluation of the CIRIA Prototype Model for the
Design of Sewage-Treatment Works", Wat. Pollut. Control, 75_
192 (1976).
23. U.S. Army Corp of Engineers, "Computer Assisted Procedure for the
Design and Evaluation of Wastewater Treatment Systems (CAPDET)",
Draft Report, Office of the Chief of Engineers, Corps of Engineers,
Department of the Army (1976).
51
-------
24. Patterson, K. E., "Dynamic Programming Approach to Cost-Effective Waste-
Water Treatment Alternative Selection", presented at 49th Annual
Conference, Water Poll. Control Fed.. Minneapolis, Minnesota,
October 3-8, (1976).
25. Dick, R. I. and Simmons, D. L., "Optimal Integration of Processes for
Sludge Management", Proc. of the Third National Conf. on Sludge
Management Disposal and Utilization, Miami Beach, Florida,
December 14-16, (1976).
26. Adams, B. J. and Panagiotakopoulos, D., "Network Approach to Optimal
Wastewater Treatment System Design", Jour. Water Poll. Control
Fed., 4£, 623 (1977).
27. Keeney, R. L. and Raiffa, H.,, "Decisions With Multiple Objectives:
Preferences and Value Tradeoffs", John Wiley and Sons, New York
(1976).
52
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APPENDIX A
MATHEMATICAL DEVELOPMENT OF THE SYSTEM SYNTHESIS MODEL
To simplify the notation we consider a system consisting of one waste-
water treatment train and one sludge treatment train with the sidestreams
from sludge treatment returned to the head of the plant. The total number
of treatment stages in the system is N where the first L of these belong
to the wastewater treatment train.
Each waste stream is characterized by its volumetric flow rate and the
mass flow rates of a number of pollutants of interest (e.g., suspended BOD,
dissolved BOD, total suspended solids, volatile suspended solids, various
forms of nitrogen, etc.). In general, these streams could also be^described
by such physical characteristics as temperature, viscosity, specific
resistance, etc. Let each stream contain M components with x. the flow
rate of component m influent to stage i and s, the flow rate 6f component
m in the sidestream generated at stage i. Denote the vector representations
of these waste flows at stage i as X.. and Si, respectively.
To once again simplify the notation, assume that each stage has J
alternative process units available for selection. Let z.. be a decision
variable whose value is 1 if unit j is chosen at stage i and is 0 otherwise.
Also let f.. and g.. be vector valued functions that describe how the
influent wake stream to stage i (X.) is transformed into an effluent stream
(X.,,) and a sidestream .(S.), respectively, when unit j is chosen. These
functions will take into account the type of unit employed, its design
specifications, and the nature of the influent waste stream. No restriction
is placed on their level of complexity.
To complete the mathematical specification of the problem, assume
that K different design criteria must be satisfied. In order to formulate
a meaningful optimization model, assume that one of these is stated as an
objective to be minimized (e.g., total cost) and all others have target
values b, that must not be exceeded. Alternatively, two or more of the
criteria could be combined into a single objective function by forming a
weighted sum.. Let c... be the contribution to criterion k by choosing
process unit j at statjli i. The same remarks made for the functions f.. and
g.. apply to c.... In addition, it is assumed that the c..^ are positive
and non-decreasing with respect to the waste stream components xin).
53
-------
The system design problem can now be written in the following form:
Minimize v,
N J
(1)
subject to v.
N J
(2)
X,
(3)
Si =
j=l
(4)
(5)
= 0 or 1
i = 1.....N
J = I j...5J
(6)
L
Z S
1=1 1
(7)
(8)
where v. is the value of criterion k and Xn is the plant influent waste
stream Vector. °
Equations (1) and (2) represent the design criteria, (3) and (4) express
the stagewise transformation of influent waste flows and the generation of
sidestreams, while (5) and (6) insure that only one process unit is chosen at
each stage. Equation (7) expresses the influent to the sludge treatment train
as the sum of the sludge sidestreams generated in the wastewater treatment
54
-------
train. Finally, equation (8) closes the loop by adding the sludge treatment
sidestreams to the plant influent.
Explicit constraints on effluent discharges from the system need not
appear in the formulation since they can be satisfied by careful specifica-
tion of the unit process alternatives. For example, the process waste stream
transformation functions f.. can be chosen so that effluent concentration is
the fixed design parameter rather than such quantities as percent removal
or size of the unit.
The above model is a nonlinear integer programming problem with decision
variables z.. and state variables x. and s. . Considerable difficulty is
caused by tne presence of the recycle equation (8). Removal of this relation
would make the waste stream vector entering stage i dependent only on the
process units chosen at stages 1 through i-1 and results in a much simpler
problem. Suppose that equation (8) is ignored and instead a penalty is
attached to each component of the sidestreams generated by sludge treatment.
Let the penalty p. be the increase in criterion k per unit increase in
component m of the recycle stream. A method for computing these penalties
is given below. Now replace the original optimization problem with a
penalty-augmented one wherein equation (8) is dropped and penalty terms are
added to equations (1) and (2) to give the following revised system criterion
values:
= v,
N
+ T,
1=L+1
M
S
m=l
pkm
sim
(9)
An efficient implicit enumeration technique is available to solve this
new penalty-augmented problem [Al]. It makes use_of the following bounding
property. Suppose that a feasible system design z.. with criterion values
v.' has been found. If at stage q a different process r is proposed and
q-1 J
Z E
1=1 j=l
cijk
qrl<
for k=l
or
b. for any k
(10a)
(10b)
then process r and all combination of process units beyond stage q can be
excluded from consideration. (Note that penalty terms should be added to
expression (10) if q > L.). This, type of result can considerably reduce the
number of unit process combinations that need to be evaluated. A systematic
procedure for using this property to identify the optimal values of the
is given in Figure Al .
The problem remains of establishing representative values for the
penalties for sludge treatment recycle streams. Since these values will
depend on the choice of process units, which are unknowns, a heuristic,
^ .
55
-------
Figure AT. Flow chart of the implicit enumeration procedure
(continued)
56
-------
Figure Al. (continued)
57
-------
iterative approach that successively generates new system designs and
corresponding penalty values is employed. It is described in the flowchart
of Figure A2.
After each iteration has identified a new candidate design, the
criterion values with and without the sludge treatment recycle stream
(vk(X,) and v. (X ), respectively) for this design are computed. Then
new penalty values can be found from
pkm
M
2
1=1
(Av
(ii)
where (Av.) is the change in v. (X ) when a quantity (AX ) is added to the
m-th component of the plant infTueHt. The (Av. )m are evaluated numerically
by solution of equations (l)-(4) and (7) as X0K1S pertubed by an amount
(AX ) under the current candidate system design. The first term in
brackets is a numerical approximation to the partial derivative of criterion
k with respect to system recycle component m. The second term is an adjust-
ment factor that allows v^(X,) to satisfy a first.order Taylor series
expansion about v,.(X ). '
K 0
Very often a designer would be interested in identifying the system
designs that are within a percent of least-cost (or least-energy, etc.).
The solution procedure previously described can easily be extended to
provide such information. After the best candidate design is identified,
its corresponding penalty values are used once again in solving the penalty-
augmented problem. Only this time the right hand side of the bounding
relation (10a) is multiplied by (l+a/100). Each complete system design
generated during the course of the implicit enumeration algorithm that is
within a percent of the best design is saved. Its true performance is
later evaluated by solving equations (l)-(4), (7) and (8).
58
-------
Begin with all penalty values at zero.
Find a candidate system design by,solving the
penalty-augmented problem 01-17).
the resulting criteria values as v.
Stop with the final
design as the best of-
the candidate designs.
(XQ1.
Yes
Has this design been
•obtained before from
a previous iteration?
No
Evaluate the true performance of the design
by solving equations (l)-(4), (7), and (8).
Denote the resulting criteria values as vC
Determine a new set of penalty values based
on the process units contained in the current
candidate design from equation (]!)•
Figure A2. Overall system design algorithm
59
-------
REFERENCES
AT. Rodrigo, B., F. R. and Seader, J. D., "Synthesis of Separation Sequences
by Ordered Branch Search", AIChE Jour.. 21_, 885 (1975).
60
-------
APPENDIX B
EXEC/OP USERS' GUIDE
As a preliminary step to using the EXEC/OP computer program 1t is
suggested that the user first prepare a multi -option flow diagrant of the
waste treatment system. An example of such a diagram for a hypothetical
design problem is shown in Figure Bl. The following simple rules must be
followed in preparing these diagrams:
(1) Treatment stages are numbered in order, starting with those in the
wastewater treatment train, followed by those in the secondary sludge
train, and then by those in the primary/mixed sludge treatment train.
(2) There must be at least one stage in each of the three treatment trains.
If the possibility of separate treatment of primary and secondary
sludges is not to be considered then the user can simply use a single
stage with the "null process" as its only option in the secondary
sludge train.
(3) The unit process options selected for consideration at each stage must
be from those listed in Table Bl. The same type of process can be
used at several different design levels and at several different
stages in the system.
(4) The sidestreams from sludge processing 'can be assigned to any waste-
water treatment stage except the first stage.
Once a multi-option flow diagram has been prepared, EXEC/OP can be
used to select the process option at each stage of the system that will
best meet a set of design criteria. These criteria are listed in Table BZ.
They can be combined together in a weighted objective function whose value
is to be minimized and they can have constraint levels associated with them
whose values are not be be violated by any feasible design.
Figure B2 shows the general organization of the 1jPu*
A brief description of each category of input along with its FORTRAN format
now follows:
Title Card - contains a descriptive title for the problem (40A2).
Influent Waste Cards - gives the values of the influent waste parameters
listed in Table B3 (8F10..0).
61
-------
Effluent Standards Card - gives the required effluent standards for 5-day
Ds nitrogen' nitrate
Economic^aramete^Cards^ gives values for the economic parameters listed
System Structure Card - lists the number of stages in the wastewater treat-
ment, secondary sludge, and primary sludge trains, respectively, the
total number of process options considered, and the stage where si fe-
streams from sludge processing are returned for treatment (5110)
Cf V 9lVes the fta9e where the sludge from each waste-
Chlorination
1 I
! . 8 9 | 10
|?^ Null Process I
' Gravity ~^
Thickening
L_
Null Process
Lime Stabilization
Anaerobic Digestion 1
Anaerobic Digestion II
Aerobic Diaestion
I Null Process
Gravity Thickening
11
Null Process
Vacuum Filtration
Sand Drying Beds
^—^
12
Truck Transport
8 Landlilling
Truck Transport
a Land Spreading 1
i truck Transport
& Land Spreading II
Multiple Hearth
Incineration
Figure Bl. Multi-option flow diagram for a hypothetical design problem
62
-------
TABLE Bl. EXEC/OP UNIT PROCESSES
Process
Subroutine ID Number
Null Process 0
Preliminary Treatment 1
Primary Sedimentation 2
Activated Sludge (Aeration Basin
and Final Settler) 3.
Anaerobic Digestion 6
Vacuum Filtration 7
Gravity Thickening 8
Elutriation • 9
Sand Drying Beds 10
Trickling Filter - Final Settler 11
Chlorination - Dechlorination 12
Flotation Thickening 13
Multiple Hearth Incineration 14
Raw Wastewater Pumping 15
Sludge Holding Tanks 16
Centrifugation 17
Aerobic Digestion 18
Truck Transport/Land Disposal of Sludge
(Land Spreading or Landfilling) 22
Lime Stabilization 23
Rotating Biological Contactor -
Final Settler 24
Primary Sedimentation - Activated Sludge -
Waste Activated Sludge Returned to Primary
Clarifie 25
Nonoxidative Heat Treatment 26
63
-------
TABLE B2. EXEC/OP DESIGN CRITERIA
1,
2.
3.
4.
5.
6.
7.
Initial Construction Cost, million $
Annual Operation and Maintenance Cost, $/mil. gal
Total Equivalent Annual Cost, $/mil. gal.
Gross Energy Consumption, kwh/mil. gal.
Gross Energy Production, kwh/mil. gal.
Net Energy Consumption, kwh/mil. gal.
Land Utilization, acres.
8. Undesireability Index.
' Single
Design Card
Figure B2. Organization of input data for EXEC/OP
64
-------
TABLE B3. EXEC/OP WASTE STREAM PARAMETERS
Q
SOC
SNBC
SON
SOP
S.FM
SBOD
VSS
TSS
DOC
DNBC
DN
DP
DFM
ALK
DBOD
NH3
N03
Volumetric Flow, mgd
Suspended Organic Carbon, mg/1
Suspended Nonbiodegradable Carbon, mg/1
Suspended Organic Nitrogen, mg/1
Suspended Organic Phosphorus, mg/1
Suspended Fixed Matter, mg/1
Suspended 5-Day BOD, mg/1
Volatile Suspended Solids, mg/1
Total Suspended Solids, mg/1
Dissolved Organic Carbon, mg/1
Dissolved Nonbiodegradable Carbon, mg/1
Dissolved Nitrogen, mg/1
Dissolved Phosphorus, mg/1
Dissolved Fixed Matter, mg/1
Alkalinity, mg/1
Dissolved 5-Day BOD, mg/1
Ammonia Nitrogen, mg/1
Nitrate Nitrogen, mg/1
65
-------
TABLE B4. EXEC/OP ECONOMIC PARAMETERS
EPA Sewage Treatment Plant Cost Index (1957-59=1.0)
Wholesale Price Index (1957-59=1.0)
Discount Rate
Length of Planning Period, yrs.
Direct Hourly Wage, $/hr.
Fraction of Direct Hourly Wage Charged to Indirect Labor
Cost of Electricity, $/kwh
Cost Escalator for Yarkwork, Laboratories, Legal Fees,
Engineering and Interest
Efficiency of Converting Heating Value of Fuels into
Equivalent Electrical Energy
1-st card - tells at which stage the option appears, the identifiratinn
number assigned to the process, the identification number of Se
process subroutine (see Table Bl), the maximum energy production
in??,^frOIY^ pr°Sess (1n e^iva^nt kwh/mil. gal. of plan?
(3110? 2Floao) undesi>eab^"ty rating for the process
2-nd Card - contains a descriptive title for the process (40A2).
3-rd and 4-th cards - lists the values of the 16 input design parameters
for the process (see Appendix C). (8F10.0). parameters
Additional Cards -Contains any supplementary input data (see Appendix
Selection
66
-------
Criteria Constraint Card - gives the upper allowable limit (lower limit for
energy production) for each, selection criterion of Table B2 (8F10.0).
Design Selection Card - gives the values of M and X where the user desires
to identify the M best designs that are within 100X% of one another
(with respect'to the weighted objective function). (110, F10.0).
Single Design Card - lists the identification number bf"the process option
to be selected at each stage .of the system (used only when no optimiza-
tion is to be performed and the user desires detailed performance data
for a particular system design) (8110).
Appendix C provides a description of the input parameters needed for
each type of unit process. A sample input deck for the flow diagram of
Figure Bl is shown in Figure B3. The DEC PDF-11/70 computer on which this
problem was run allows a data field to be terminated by a comma and two
successive commas indicates a value of 0. Thus the input data appearing in
Figure B3 does not line up in field widths of 10 spaces each. The last three
lines of this data indicate that the design problem involves total cost
minimization, that no constraints are placed on the design criteria (values
of the constraint limits are set to very high numbers or zero for energy
production), and that the five best designs whose costs are within 5 percent
of one another is desired.
The resulting EXEC/OP output for this example is shown in Figure_B4.
The first three tables present summaries of the input data. The section
titled "Optimization Phase" lists the designs arrived at while searching
for the first-best solution. The "Sensitivity Phase" section gives the
five top designs with respect to total cost. Each design listing in these
sections indicates the process option used at each stage of the system
(stages using the "null process" are skipped), the amount of sludge either-
generated or handled, and the values of the eight system selection criteria.
The stage where mixing of primary and secondary sludges occurs is shown
with an asterisk. At the end of the output is shown how efficient the
search method of EXEC/OP was in comparison to a complete enumeration of all ,
possible system configurations. (Note: the total number of possible system
configurations equals the product of the number of process options considered
at each stage. This number is 15,360 for the system in this sample problem).
The user is advised that because of the heuristic nature of the optimiza-
tion method used in EXEC/OP, the "best" design arrived at in the "Optimiza-
tion Phase" may be close to but not equal to the true optimum. In such cases
one of the M next-best designs listed in the "Sensitivity Phase" results
may have a better objective function value. Thus the use of the M next-best
design feature provides added insurance that the true mathematical optimum.
is not missed in addition to its other useful informational properties.
Should the user desire a detailed performance evaluation of any parti-
cular design configuration of the multi-option flow diagram, the single
design evaluation feature of EXEC/OP can be used. For example, assume that
this kind of information is requested for the least-cost design in our sample
67
-------
«nc ™ CASE STUDY 1
J?" ??" °" 2" 30" 14°" 229'
., 43., 11., 19., 4., 500., 250., 60.
15•t 0 •
30,, 30., 10000,, 10000., 10000,
2.88, 2., ,06375, 20,, 5.91, ,]5, ,033, 1.33
5, 2, 5, 31, 3
8, 8, 8, 6, 6
1,1,15, 0,, 0,
RAW WASTEWATER PUMPING
30., ,,,,,,,
• ' • i i , , 1.
2,2,1, 0,, 0,
PRELIMINARY TREATMENT
*•»,,,»,,,
'»,,»,»!,
3,3,2, 0,, 0,
PRIMARY SEDIMENTATION (40% SOLIDS REMOVAL)
14, 200., 14,, , , , t t
' t , • • , 1,, 1,
3,4,2, 0,, 0,
.6, 200., 14., , , .
'»,,,,!,,!.
4,5,3, 0,, 0,
ACTIVATED SLUDGE (MLVSS=2000, Rs 3)
7°" nS"fi^°°-; *3' 2°°" -5' ^. •«
7., .05, 800.. 30,, 1,, 1., ,., j
*,D,3, 0,, 0,
3ft CMLVSS=2000, Ha.5)
7 " oJ"B2S°°'5 '5' 2°°" '5' l5" *48
7,, .05, 800., 30., 1,, i., ,., t;
4,7,3, 0,, 0.
(MLVSSslOOO, Pa.3)
7 " n?"ann°°" *3' 2°°" '5' l5" .48
J:s,j"s.!°X:f 30" *••'- l- l-
(MLVSS33000, Rs.5)
? " n^"Q^00" '5' 2°°" '5' ^" »48
5§9 iSS'n V 3°" 1>f J" '^ '
5,9,12, 0,, 0,
CHLORINATION (8 MG/L DOSAGE)
8,, 30,, 220,, 2.5, 180., , , ,
!'''•*•• *•' 1.
6,30,0, 0,, 0,
NULL PROCESS
6/10,13, 0,, 0.
Figure B3. Input data for hypotetical design problem
68
-------
A1P FLOTATION THICKENING (TO 4% SOLIDS)
.95, 40000., 1150,, 48,, 96,, 10,, ,45, 0..
,,,,,,, It
7,30,0, 0,, 0,
NULL PROCESS
7,11,18, 0,, 0.
AEROBIC DIGESTION flO DAYS)
0,, 0,, 0,, 20,, 10,, ,48, ,5, 0.
0,, 7,, ,05, 0,, 0,, 08, 1., I.
8,30,0, 0,, 0,
NULL PROCESS
8,12,8, 0,, 0,
GRAVITY THICKENING (TO 8% FOP PPI, 5% FOP MIXED SLUDGE)
,9, 0,, 700,, 0,, 80000,, 20000,, 50000,, 16,
6«, 8., , , , , , 1*
9,30,0, 0,, 0,
NULL PROCESS
9,13,23, 0,, 0,
LIME STABILIZATION C200 LBS/TON)
200,, 25,, ,,,,,,
, i , , , , i 1,
9,14,6, 2000,, 0,
ANAEROBIC DIGESTION (15 DAYS)
13
,
9,
20
*
,
\
.
,
5
,
JU
, ,
,6»
30
* ,
,
e=>
V
2000
* ,
«5
1
1
.
,
«
1
,
•
1
31,
.
o.
31,
*
, , ,
(20 !
, f ,
,
5A
,
9,16,18, 0,, 0,
AEROBIC DIGESTION (20
DAYS)
20,, .48, ,5, 0,
0., 0,, J,, 1,
0,, 0., 0,, 20,,
0,, 7,, .05, 0.,
10,17,8, 0B, 0,
GRAVITY THICKENING (TO 5% SOLIDS)
.9, 0,, 700,, 0,, 80000,, 20000,, 50000,, 16,
6. ,8, ,,<>,,,],
10,18,9, 00, 0,
ELUTRIATION (WASHWATEP RATIO • 3, THICKENS TO 4%)
.76, 0,, 3a, 800., 0,, 60000,, 20000,, 40000,
16., 6,, 8 g , , , , , 1,
10,30,0, 0., 0,
NULL PROCESS
11,19,7, 0,, 0.
Figure B3. (continued)
69
-------
VACUUM FILTRATION (10 GPH/SQ FT)
0,, 35., 2000,, 0,, 0., 0,, .064, ,0125
0,, .33, 1,, , , , , I,
10,, 10,, 10,, 10,, 10,, 10,, 10,, 10,
10,, 10,, 10,, 10,, 10,, 10., 10,
42,, 0,, 52., 42,, 0,, 40,* 76,, 0.
110,, 68,, 0., 125,, 0,, 0., 0,
176,, Of, 200., 0,,
370,, ,,,,,,
**,*,,,,
0., 0,, 240
0 »
0.
2., 0., 2.
.85
11,20,17, 0., 0,
CENTRIFUGATION
0,» 0,, 35,, 0., 0,,
It* ***** , 1.
,9, ,85, .9, ,9, ,85, .9, .9,
.85, ,9, ,85, .85, 0,, 0,, 0,
32., 10., 19., 30., 18,, 30., 25., 15,
22., 25,, 15., 22., 0., 0,, 0,
3f, 6,, 6,, 3,, 4., 4,, 2,» 4,
4,* 2,, 4,, 4., 0., 0., 0,
90,, 80,, 80,, 160., 160., 160., 100., 80.
80,, 100,, 80,, 80,, 100,, 80., 80.
11*21,10, 0., 0,
SAND DRYING BEDS C30% CAKE SOLIDS)
0,, 2000,, ,3, .3, .3, 15,, 1,, 3000,
»******!.
11*30,0, 0,, 0,
NULL PROCESS
12,22,22, 0,, 0,
LAND SPREADING (10 MILE HAUL, 3000 S/AC, 400 LB N/AC/YR)
2160,, 10,, 6,, .5* 3000,, 0,, ,25* 400.
500,, 10., 0,, 0,, 0., 0., 1., 1.
12*23,22* 0,, 0.
(30 MILE HAUL, 2000 S/AC* 600 LB N/AC/YR)
2160,, 30,, 6,, ,5, 2000,, 0,, ,25, 600,
500,, 10., 0., 0., 0., 0,, lt, 1.
12,24,22, 0,, 0.
LANDFILLING (10 MILE HAUL, 3000 S/AC)
2160., 10., 6., ,5, 3000,, 1,, 0., 0,
****** 1.* 1,
12*25*14, 0., 0.
INCINERATION (2 LB/HP/SQ FT)
2,, 2.* 35,, 5., 0., 10000., 1., ,3
!.*******!.
Oi» 0,* I., 0,, 0., 0,, 0,, 0,
10000,, 10000,, 10000,, 10000., 0,* 10000,, 10000,, 10000,
5, ,05
Figure B3. (continued)
70
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design problem (i.e.,-design 1 in the "Sensitivity Phase" results of Figure
B4) The same input data deck is used with the addition of the single
design cards to the end of the deck. These cards give the number of the
process unit selected at each stage of the particular system configuration
under study. In this example they would be:
1, 2, 4, 7, 9, 30, 30, 30
14, 17, 21, 25
The output produced by EXEC/OP is shown in Figure B5. Each stage of the
system (except those employing the null process) has its influent, effluent
and sidestream waste streams described along with values of the input and_
computed design parameters for the process used at the stage. The abbrevia-
tions used for the components in the waste streams are explained in Table B3.
The meaning of the input and output design parameter values can be found by
looking up the process description in the listing of Appendix C.
The programmed version of EXEC/OP, listed in Appendix D, is dimensioned
to accommodate up to 19 processing stages and 50 different types of process
options. For those unit processes that require supplementary input data
tables (vacuum filtration and centrifugation) a maximum of 5 different
design levels may be used. The program is capable of generating up to 40
next-best designs.
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APPENDIX C
LISTING OF UNIT PROCESS DESCRIPTIONS
The following pages provide a listing of the design parameters used in
the individual unit process model subroutines of EXEC/OP. The processes
are listed in order of their subroutine identification number. Input
parameters are those which must be supplied as input data to the program.
Normally there are 16 parameters for each process. Of these 16, those
that are not described in these pages are assigned the value of 0. Output
parameters are those which are calculated during the execution of EXEC/OP.
The nominal sizes of all equipment are based on providing enough
capacity for normal system operation. Actual sizes are computed by multi-
plying the nominal values by an excess capacity factor. These factors
account for any reserve capacity needed to handle peak flows or for
periodic cleaning and maintenance. They are specified as part of the
input parameter list for each unit process.
Process: Preliminary Treatment
Subroutine ID Number: 1
Input Design Parameters:
1 - program,control number: 0 = grit removal and flow measurement;
1 = grit removal, flow measurement, and screening
16 - excess capacity factor
Output Design Parameters: None
Notes:
1. Cost functions are from Ref. Cl.
2. Energy consumption is from Refs. C2 and C3.
Process: Primary Sedimentation
Subroutine ID Number: 2
Input Design Parameters:
1 - fractional removal of influent suspended solids (.4-.6)
86
-------
2 - ratio of solids concentration in settler underflow to solids
concentration in settler influent (150 - 250)
3 - hours per week that sludge pumps are operated
15 - excess capacity factor for sludge pumps
16 - excess capacity factor for settler basin
Output Design Parameters:
1 - overflow rate for settler, gpd/sq. ft.
2 - surface area of settler, sq. ft./1000
3 - pumping capacity of sludge pumps, gpm
Notes:
1. Relation between fractional solids removal and overflow rate is from
Ref. C4.
2. Cost functions are from Ref. Cl.
3. Energy consumption is from Refs. C2 and C3.
Process: Activated Sludge (Aeration"Basin and Final Settler)
Subroutine ID Number: 3
Input Design Parameters:
1 - effluent BOD, mg/1
2 - effluent suspended solids, mg/1
3 - mixed liquor volatile suspended solids, mg/1 (2000 - 4000)
4 - sludge recycle ratio (.2-.8)
5 - half-velocity constant, mg/1 (20-1000)
6 - true yield coefficient (.5-.7)
7 - maximum substrate removal rate coefficient, I/day (3-20)
8 - biomass decay coefficient at a 1 day sludge age, I/day (.1-.5)
9 - maximum removal rate coefficient for nitrification, I/day (7)
10 - oxygen transfer efficiency of aeration equipment (.05-.08)
11 - overflow rate of final settler, gpd/sq. ft. (600-800)
12 - return sludge pumping head, ft. (10-20)
13 - excess capacity factor for final settler
14 - excess capacity factor for return sludge pumping
15 - excess capacity factor for air blowers
16 - excess capacity factor for aeration basin
Output Design Parameters:
1 - influent 5-day BOD, mg/1
2 - sludge age (solids retention time), days
3 - ratio of settler effluent to settler influent solids concentration
4 - surface area of final settler, sq. ft./lOOO
5 - maximum substrate removal rate coefficient, I/day
87
-------
6 - biomass decay coefficient, I/day
7 - aeration basin volume, million gallons
9 - concentration of active biomass in aeration, rng/1
11 - concentration of refractory organic solids in aeration, mg/1
12 - concentration of nondegradable solids in aerator due to
cell destruction, mg/1
13 - concentration of inert inorganic solids in aerator, mg/1
14 - concentration of BOD removed in aerator, mg/1
15 - sludge return ratio
16 - maximum removal rate coefficient for nitrification, I/day
17 - diffused air requirement for the aerator, scf/day
18 - size of air blower required, cfm
19 - diffused air requirement per gallon of entering wastewater, scf/gal
20 - volume of return sludge stream, mgd.
Notes:
1. BOD removal kinetics, sludge production, and air requirements are based
on the models of Refs. C5, C6, and C7. The input kinetic parameters
(items 5-9) are also based on these models.
2. Nitrification is normally assumed to begin after a sludge age of 5 days.
If nitrification is not allowed then input design parameter 16 should
equal 0.
3. Cost functions are from Ref. Cl.
4. Energy consumption is from Refs. C3 and C8.
Process: Anaerobic Digestion
Subroutine ID Number: 6
Input Design Parameters:
1 - detention time, days (15-30)
2 - sludge temperature in digester, degrees C (30-40)
3 - climate correction factor (1.0 for northern U.S., 0.5 for middle
U.S., 0.3 for southern U.S.)
4 - efficiency in converting BTU content of digester gas into
equivalent kwh
16 - excess capacity factor
Output Design Parameters:
1 - rate constant for digester, I/day
2 - rate constant for biodegradable carbon, I/day
3 - digester volume, cu. ft./1000
4 - methane production, scf/day
5 - carbon dioxide production, scf/day
88
-------
Notes:
1. Kinetics of biodegradable carbon destruction is described in Ref. C4.
2. Cost functions are from Ref. Cl.
3. Energy consumption is from Refs. C2 and C3.
Process: Vacuum Filtration
Subroutine ID Number: 7
Input Design Parameters:
2 - hours per week of operation
3 - suspended solids concentration in filtrate, mg/1 (1500-2500)
7 - cost of ferric chloride, $/lb
8 - cost of Lime, $/lb
10 - cost of polymer, $/lb
11 - identification number of supplementary input tables
16 - excess capacity factor
Supplementary Input Design Parameter Tables:
1 - filter dewatering rate, gph/sq. ft. (8-18)
2 - ferric chloride dosage, Ib/ton (0-200)
3 - lime dosage, Ib/ton (0-400)
4 - polymer dosage, Ib/ton (0-40)
Output Design Parameters:
1 - percent moisture of filtered sludge
2 - filter surface area, sq. ft.
3 - filter cake dry solids production rate, Ib/day
Notes:
1. Supplementary input data tables consist of parameter values for the 15
categories of sludge types shown below and are entered into the program
input by column
Digested Heat
Raw Limed Digested + Elutriated Treated
Primary
Secondary
Primary +
Secondary
2.
3.
4.
Prediction of cake moisture and required surface area is from
Ref. C9.
Cost functions are from Ref. Cl.
Energy consumption is from^Refs. C2 and C3.
89
-------
Process: Gravity Thickening
Subroutine ID Number: 8
Input Design Parameters:
1 - solids recovery ratio (.9-.98)
3 - overflow rate, gpd/sq. ft. (400-800)
5 - underflow thickened solids concentration for primary sludge, mg/1
6 - underflow thickened solids concentration for secondary sludge, mg/1
7 - underflow thickened solids concentration for mixed primary and
secondary sludge, mg/1
8 - solids loading rate for primary sludge, Ib/day/sq. ft. (20-30)
9 - solids loading rate for secondary sludge, Ib/day/sq. ft. (4-18)
10 - solids loading rate for mixed primary and secondary sludqe,
Ib/day/sq. ft. (8-20)
16 - excess capacity factor
Output Design Parameters:
1 - surface area of thickener, sq. ft.
Notes:
1. Cost functions are from Ref. Cl.
2. Energy consumption is from Refs. C2 and C3.
Process: Elutriation
Subroutine ID Number: 9
Input Design Parameters:
1 - solids recovery ratio (.7-.9)
3 - ratio of wash water volume to influent volume (3)
4 - overflow rate, gpd/sq. ft. (400-600)
6-- underflow solids concentration for primary sludge, mg/1
underflow solids concentration for secondary sludge, mg/1
underflow solids concentration for mixed primary and secondary
sludge, mg/1
solids loading rate for primary sludge, Ib/day/sq. ft. (20-30)
solids loading rate for secondary sludge, Ib/day/sq. ft. (4-18)
solids loading rate for mixed primary and secondary sludqe,
Ib/day/sq. ft. (8-20)
excess capacity factor
7
8 -
9 -
10 -
11 -
16 -
Output Design Parameters:
1 - surface area of elutriation tank, sq. ft.
90
-------
Notes-: .-•-•••..
1. Wastewater effluent is used as wash water stream
2. Cost functions are from Ref. Cl.
3. Energy consumption is from Refs. C2 and C3.
Process: Sand Drying Beds
Subroutine ID Number: 10
Input Design Parameters:
2 - suspended*'solids concentration in filtrate, mg/1 (200-3000)
3 - solid fraction of sludge cake for primary sludge (.2-.4)
4 - solid fraction of sludge cake for secondary sludge (.15-.3)
5 - solid fraction of sludge cake for mixed primary and secondary sludge
6 - detention time required for sludge holding tanks, days (5-20)
7 - excess capacity factor for sludge holding tanks
8 - cost of land for drying beds, $/acre
16 - excess capacity factor, for drying beds
Output Design Parameters:
1 - bed area, sq. ft.
Notes:
1. Required bed area is computed as in Ref. C9.
2. Cost functions are from Ref. Cl.
3. Energy consumption is from Ref. C3.
Process: Trickling Filter - Final Settler
Subroutine ID Number: 11
Input Design Parameters:
1 - effluent 5-day BOD, mg/1
2 - water temperature, degrees C
3 - hydraulic loading on filter (without recycle), mgd/acre 0040)
4 - specific surface area of the filter, sq. ft./cu. ft C935)
5 - effluent suspended solids, mg/1
6 - suspended solids concentration in sludge underflow from final
settler (mg/1) (10000-40000)
7 - ratio of recycle flow to filter influent (Hi1
8 - overflow rate of final, settler, gpd/sq. ft. (600-800)
9 - sludge production factor, Ibs sludge/lb BOD removed (.4-.65)
10 - ratio of settled to unsettled effluent BOD (.5)
14 - excess capacity factor for recirculation pumps
15 - excess capacity factor.for final settler
16 - excess capacity factor for filter
91
-------
Output Design Parameters:
1 - surface area of final settler, sq. ft./lOOO
2 - volume of trickling filter, 1000 cu ft
3 - area of filter face, acres
4 - depth of filter, ft.
Notes:
1.
2.
3.
4.
Recycle of filter effluent and use of final settler are optional
Filter is sized according to Ref. CIO.
Cost functions are from Ref. Cl.
Energy consumption is from Refs. C3 and C8.
Process: Chlorination - Dechlorination
Subroutine ID Number: 12
Input Design Parameters:
1
2
3
4
5
14
15
16
dose of chlorine, mg/1 (2-15)
chlorine contact time, minutes (15-30)
cost of chlorine, $/ton
dose of sulfur dioxide for dechlorination, mg/1 (2.5)
cost of sulfur dioxide, $/ton
excess capacity factor for the sulfur dioxide feed system
excess capacity factor for the chlorine feed system
excess capacity factor for the contact basin
Output Design Parameters:
1 - volume of the chlorine contact basin, cu. ft.
2 - amount of chlorine used, tons/yr.
3 - amount of sulfur dioxide used, tons/yr.
Notes:
1. Costs are from Ref. Cl.
2. Energy consumption is from Ref. C3.
Process: Flotation Thickening
Subroutine ID Number: 13
Input Design Parameters:
1
2
3
4
5
solids recovery ratio (-95)
suspended solids concentration of thickened sludge, mg/1
overflow rate, gpd/sq. ft. (700-1200)
solids loading rate, Ib./day/sq. ft. (24-96)
hours per week of operation
92
-------
6 - dose of polymer, Ib./ton
7 - cost of polymer, $/ton
16 - excess capacity factor
Output Design Parameters:
1 - surface area of each thickener used, sq. ft.
2 - number of thickeners used
3 - total surface area required, sq. ft.
Notes:
1.
2.
3.
Thickeners are chosen from among a set of commercially available sizes,
Costs are taken from Ref. Cl.
Energy consumption is from Ref. C8.
Process: Multiple Hearth Incineration
Subroutine ID Number: 14
Input.Design Parameters:
(2)
1 - mass loading, Ib./hr./sq. ft. of hearth area
2 - number of multiple hearth incinerators (2)
3 - hours per week of operation
4 - number of start-up periods per week
5 - wind velocity, mph
6 - higher heat value for volatiles, BTU/lb. (10000)
7 - type of fuel used; 1 = fuel oil, 2 = natural gas, 3 = digester gas
8 - cost of fuel oil, $/gal.
9 - cost of natural gas, $/1000 cu. ft.
10 - efficiency in converting BTU content of exhaust gas into
equivalent kwh
16 - excess capacity factor
Output Design Parameters:
1 - total hearth area, sq. ft.
2 - total fuel usage, Ib./yr.
3 - amount of dry solids incinerated, Ib./day.
4 - cost of electrical power to operate the incinerator, $/yr.
5 - cost of fuel to operate the incinerator, $/yr.
Notes:
1 Hearth sizing and fuel requirements are computed as in Refs. C9 and
Gil on the basis of exit gas temperature of 800°F.
2. Costs are from Ref. Cll. Does not include air pollution control
equipment or ash disposal.
93
-------
3. Energy consumption is from Ref. Cll.
ambient
Process: Raw Wastewater Pumping
Suroutine ID Number: 15
Input Design Parameters:
1 - pumping head, ft. (10-30)
16 - excess capacity factor
Output Design Parameters:
1 - peak flow capacity of the raw wastewater pumping system, mgd
Notes:
1 . Costs are from Ref. Cl .
2. Energy consumption is from Ref. C8.
Sludge Holding Tanks
Subroutine ID Number: 16
Input Design Parameters:
1 - dentention time, days
16 - excess capacity factor
Output Design Parameters:
1 - volume of holding tanks, cu. ft./lOOO
Notes:
1. Costs are from Ref. Cl.
Process: Centrifugation
Subroutine ID Number: 17
Input Design Parameters:
3
6
8
9
hours per week of operation
cost of polymer, $/lb.
minimum number of centrifuges to be used
identification number of supplementary input tables
94
-------
Supplementary Input Design Parameter Tables:
1 - solids recovery ratio (.5-.9)
2 - percent solids of centrifuged sludge .(5-35)
3 - dose of conditioning polymer, Ib/ton (0-15)
4 - sludge feed rate, gpm (10-200)
Output Design Parameters:
1 - design capacity of the centrifuges, gpm
2 - dry solids processed, tons/yr.
3 - capital recovery factor for centrifuges based on a 10 year lifetime
4 - size of each centrifuge used, gpm
5 - number of centrifuges used
Notes:
1. Supplementary input data tables consist of parameter values for the 15
categories of sludge types shown below and are entered into the program
input by column.
Raw
Limed
Digested
Digested
+ Elutriated
Heat
Treated
Primary
Secondary
Primary +
Secondary
2. Determination of the number and sizes of centrifuges is described in
Ref. C9.
3. Costs are from Ref. CT.
4. Energy consumption is based on 1 hp/gpm as quoted in Ref. C13.
Process: Aerobic Digestion
Subroutine ID Number: 18
Input Design Parameters:
4 - sludge temperature, degrees C
5 - process detention time, days (12-22)
6 - biomass decay rate coefficient at a sludge age of
1 day at 20°C, I/days (.1-.5)
7 - true yield coefficient for 5-day BOD l-b-./j
10 - maximum removal rate coefficient for nitrification, I/day (7)
11 - oxygen transfer efficiency (.05-.08)
15 - excess capacity factor for blowers
16 - excess capacity factor for digester
95
-------
Output Design Parameters:
1 - digester volume, cu. ft./1000
2 - size of air blower, cfm
Notes:
1. Complete mix, continuous flow operation is assumed.
2. Solids destruction is modeled as in Ref. C14. Adjustment of biomass
decay rate to reflect solids retention time in both digester and
activated sludge unit is from Ref. C6.
3. Costs are from Ref. Cl.
4. Energy consumption is from Ref. C8.
(600)
Process: Truck Transport/Land Disposal of Sludge
Subroutine ID Number: 22
Input Design Parameters:
1 - working hours per year
2 - one-way hauling distance, miles
3 - amortization period for trucks, years
4 - fuel cost, $/gal.
5 - cost of land, $/acre
6 - program control; 0 = land spreading, 1 = landfilling
7 - storage period for liquid sludge, years (-1-.5)
8 - maximum allowable nitrogen application rate, Ib./acre/yr.
9 - land spreading site preparation cost, $/acre
10 - land spreading application cost, $/ton
15 - excess capacity factor for trucks
16 - excess capacity factor for sludge storage
Output Design Parameters:
1 - number of trips per year per truck
2 - number of trips per year by all trucks
3 - total number of trucks
4 - volume of sludge storage, cu. ft./lOOO
5 - amount of dry solids applied to land, tons/yr.
6 - land area required, acres
7 - equivalent annual interest cost on capital investment in land, $/yr.
8 - capital cost of land, $
9 - capital recovery factor for trucks
96
-------
Notes:
1. Sludge spreading may involve either liquid or dewatered sludge. Only
dewatered sludge may be landfilled. Dewatered sludge has a solids
content of 15% or more.
2. Land used for sludge spreading has a resale value equal to its initial
cost.
3. Costs and energy consumption of truck transport are from Ref. C15.
4. Costs for landfills are from Ref. C16.
5. Costs of storage lagoons are from Ref. Cl.
6. Energy consumption for land spreading and landfill ing is from Ref. C3.
Process: Lime Stabilization
Subroutine ID Number: 23
Input Design Parameters:
1 - lime dosage, Ib./ton of dry solids (200-500)
2 - cost of lime, $/ton
16 - excess capacity factor
Output Design Parameters:
1 - lime addition rate, Ib./day
2 - amount of sludge treated, tons of dry solids/day
Notes:
1. Costs are from Ref. Cl.
2. Energy consumption is from Ref. C3.
Process: Rotating Biological Contactor - Final Settler
Subroutine ID Number: 24
Input Design Parameters:
1 - effluent 5-day BOD, mg/1
2 - number of stages in series for the process (4)
3 - temperature.of the wastewater, degrees C
4 - rate constan-t for BOD removal at 20°C, gpd/sq. ft. (6-9)
5 - rate constant for nitrification at 20°C, gpd/sq. ft. (4-5)
6 - overflow rate for final settler, gpd/sq. ft. (600-800)
7 - concentration of BOD at which nitrification begins, mg/1 (20-30)
10 - sludge production factor, Ib sludge/lb BOD removed
97
-------
8 - concentration of waste solids from the final settler
underflow, percent
9 - cost of installed concrete, $/cu. yd.
15 - excess capacity factor for the final settler
16 - excess capacity factor for the rotating biological contactor
Output Design Parameters:
1 - loading rate for BOD removal adjusted for water temperature,
gpd/sq. ft.
2 - loading rate for nitrification adjusted for water temperature,
gpd/sq. ft.
3 - area per contactor stage, sq. ft.
4 - total active contactor area, sq. ft.
5 - number of stages required to achieve the BOD concentration at
which nitrification begins
6 - number of remaining stages for nitrification
7 - fraction of influent BOD remaining in effluent
8 - percentage ammonia nitrogen removal
9 - overall hydraulic loading, gpd/sq. ft.
10 - surface area of final settler, sq. ft.
11 - solids wasting rate, Ib. dry solids/day
12 - fraction of suspended solids remaining after settling
13 - number of 100,000 sq. ft. shafts per stage
14 - number of 100,000 sq. ft. shafts required
15 - materials and supplies cost, $/yr.
16 - electrical power cost, $/yr.
17 - labor cost, $/yr.
Notes:
1. Waste stream transformation and sludge production is modeled as
in Ref. C8.
2. Cost functions are from Ref. Cl.
3. Energy consumption is from Ref. C3 and C8.
Process: Primary Sedimentation - Activated Sludge - Waste Activated Sludge
Returned to Primary Clarifier
Subroutine ID Number: 25
Input Design Parameters:
1 - option number assigned to primary clarifier unit
2 - option number assigned to activated sludge unit
98
-------
Supplementary Input Design Parameters: '•
1 through 16 - see Primary Sedimentation subroutine
17 through 32 - see Activated Sludge subroutine
Output Design Parameters:
1 through 20 - see Activated Sludge subroutine
Notes:
1. See notes for Primary Sedimentation and Activated Sludge subroutines.
Process: Nonoxidative Heat Treatment
Subroutine ID Number: 26
Input Design Parameters:
4 - operating temperature, degrees C (150-220)
5 - hours per week of operation
6 - number of start-ups per week
7 - fuel cost, $/milli-- TTU
8 - detention time for sludge holding tanks, days
9 - excess capacity factor for sludge holding tanks
16 - excess capacity factor for heat treatment
Output Design Parameters:
1 - capacity of heat treatment unit, gpm
2 - fraction of suspended COD remaining in effluent
3 - fraction of volatile suspended solids remaining in effluent
4 - annual heat requirement, million BTU/yr.
Notes:
1. Reduction in BOD and volatile solids is based on operating temperature
as described in Ref. C17.
2. Costs are from Ref. C18.
3. Energy consumption is from Ref. C19.
99
-------
REFERENCES
Cl
C2,
C3,
C4,
C5.
C6.
C7.
C8.
C9.
Patterson, W. L. and Banker, R. F., "Estimating Costs and Manpower
Requirements for Conventional Wastewater Treatment Facilities",
Water Pollution Control Research Series 17090 DAN 10/71, U.S.
Environmental Protection Agency (1971).
Smith, R., "Electrical Power Consumption for Municipal Wastewater
Treatment", EPA-R2-73-281, U.S. Environmental Protection Agency,
Cincinnati, Ohio (1973).
Wesner, 6. M., et al., "Energy Conservation in Municipal Wastewater
Treatment", EPA-430/9-77-001, U.S. Environmental Protection
Agency, Office of Water Program Operations, Washington, D.C.
\'y/' / •
Smith, R., "Preliminary Design of Wastewater Treatment Systems",
Jour. San. Eng. Div., Proc. Amer. Soc. Civil Engr., 95, 117,
(1969). *- —
Lawrence, A. W. and McCarty, P. L., "Unified Basis for Biological
Treatment Design and Operation", Jour. San. Eng. Div., Proc.
Civil Engr., 96., 757, (1970).
Goodman, B. L. and Englande, A. J., "A Unified Model of the Activated
Sludge Process", Jour. Water Poll. Control Fed. 46, 312 (1974).
Christensen, D. R. and McCarty, P. L., "BIOTREAT: A Multi-Process
Biological Treatment Model" presented at the Annual Conference of the
Water Pollution Control Federation, Denver, Colorado, October 8, (1974)
Eilers, R. G., et al., "Applications of Computer Programs in the
Preliminary Design of Wastewater Treatment Facilities -
Section II", EPA-600/2-78-1856, U.S. Environmental Protection
Agency, Municipal Environmental Research Laboratory, Cincinnati,
Ohio (1978).
Smith, R. and Eilers, R. G., "Computer Evaluation of Sludge Handling
and Disposal Costs", Proceedings of the 1975 National Conference
on Municipal Sludge Management and Disposal, Anaheim, California,
August 18-20 (1975).
100
-------
CIO. Roesler, J. F. and Smith R., "A Mathematical Model for a Trickling
Filter", U.S. Department of Interior, Federal Water Pollution
Control Administration, Advanced Waste Treatment Research
Laboratory, Cincinnati, Ohio (1969).
Cll. Unterberg, R., Sherwood, J., and Schneider, G. R., "Computerized
Design and Cost Estimation for Multiple Hearth Sludge
Incinerators", available from National Technical Information
Service as NTIS-PB-211-264, July (1971).
C12. Smith, R., "Total Energy Consumption for Municipal Wastewater Treatment",
EPA-600/2-78-149, U.S. Environmental Protection Agency, Municipal
Environmental Research Laboratory, Cincinnati, Ohio (1978).
CIS. Haug, R. T., Tortorici, L. D., and Raksit, S. K., "Sludge Processing
and Disposal: A State of the Art Review", Regional Wastewater
Solids Management Program, Los Angeles County/Orange County
Metropolitan Area, April (1977).
C14. Adams, C. E., Jr., et-al., "Modification to Aerobic Digestion Design",
Water Res.. 8, 213 (1974).
C15. Ettlich, W. F., "Transport of Sewage Sludge", EPA-600/2-77-216,
U.S. Environmental Protection Agency, Cincinnati, Ohio (1977).
C16. Wyatt, J. M. et al, "Sludge Processing, Transportation and Disposal/
Research Recovery: A Planning Perspective", EPA-440/9-76-002,
U.S. Environmental Protection Agency, Washington, D.C. (1975).
C17. Heidman, J. A., "Oxidative and Nonoxidative Heat Treatment of Waste-
water Sludges - Part I:, Internal Memorandum, U.S. Environmental
Protection Agency, Municipal Environmental Research Laboratory,
Cincinnati, Ohio, May 3 (1978).
CIS. Ewing, L. J., Jr., et al., "Effects of Thermal Treatment of Sludge on
Municipal Wastewater Costs", Final Report for EPA Contract
No. 68-03-2186, U.S. Environmental Protection Agency, Municipal
Environmental Research Laboratory, Cincinnati, Ohio (1978).
C19. Heidman, J. A., "Oxidative and Nonoxidative Heat Treatment of Waste-
water Sludges - Part II", Internal Memorandum, U.S. Environmental
Protection Agency, Municipal Environmental Research Laboratory,
Cincinnati, Ohio, June 29 (1978).
101
-------
APPENDIX D - PROGRAM LISTING
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
25
30
EXECOP - MAIN
EXEC/OP - OPTIMIZATION VERSION OF EPA EXECUTIVE PROGRAM
SELECTS BEST COMBINATION OF UNIT PROCESSES FOR TREATING
HASTEWATER AND DISPOSING OF RESIDUAL SLUDGES.
SELECTION CRITERIA INCLUDES COST, ENERGY, LAND UTILIZATION.
AND A SUBJECTIVE PROCESS UNOESIREABILITY RATING.
PROGRAM CAN ALSO IDENTIFY UP TO 40 NEXT-BEST DESIGNS,
WRITTEN BY L. RQSSMAN, EPA, MERL, DECEMBER 1978
INTEGER OS1,OS2
COMMON SMATX(20,4S),DMATX(20,50),OMATXC20,50),IP(50),
INP,IO,ISl,IS2,asi,OS2,N,IAERF,CCQST<5),CaSTOC5),
ACaST(5),DHR,PCT,HPI,CCI,RI,AF,RATIO,CKWH,
CF,HER,EEP,ALAND
COMMON/PROC/ NPROC(20),KPRQCUO,20),NWPS,NPSPS,NSSPS,
NTPS»NTPU,JSTRM(22),JSIDE(20),P(20,10),
IPOPT£20),PINFLO(20),EFFSTD(20),ISC(45),IDC(45)
COMMON/COST/ CC20,10),CP(10),RHSC10),«(10),UDR(50),
IPSAVE(40,20),JMSAVE(40),TEPMAX,KMAX,FACTR,TUPE
,EEPMAXC20)
COMMON/TABLES/ DUMMYC600)
DIMENSION CSIM(10),CSAVEC10)
DIMENSION PSAVEC20.10]
EQUIVALENCE CPSAVEC1,1),IPSAVE(1,1)).CJMIX,JSTRM(21))
INITIALIZE COUNTERS AND UPPER BOUND
MITMAXalO
MITER»1
TUPEaO.
TSE^O.
NITSUMaO
ZUB*1.E20
NPHASEal
INP»5
I0»5
CALL SUBROUTINE THAT READS IN INPUT
ISWaO
CALL INPUT(ISW)
INITIALIZE PLANT INFLOW AND RECYCLE PENALTIES
DO 30 1=1,20
SMATX(I.1)*PINFLO(I)
DO 30 K3l,10
PSAVE(I,K)30.
PCI,K)xO.
P(2,5)3-10000,
P<2,6)3»10000.
TEPMAX3H(5)»TEPMAX
102
-------
c
c
c
c
c
c
c
c
40
400
C
C
C
C
45
50
C
C
C
90
100
t05
C
c
c
c
110
240
C
C
C
250
230
C
C
c
235
C
C
C
25*
If SYSTEM DESIGN IS FIXE0, EVALUATE PERFORMANCE
IF CISW ,EQ. 0) GO TO 40
CALL SOLVEfTCSfCSIM,NITER-)
CALL OUTPUTCISH,l,TCS,-l.,-l,)
STOP
OPTIMIZATION PHASE
WRITECI0.400)
FORMAT(////,49X, "OPTIMIZATION PHASE' /49X. 18 ( '_' ))
PERFORM SYSTEM OPTIMIZATION USING PENALTY TERMS TO
ACCOUNT FOR SLUDGE RECYCLE FLOWS.
IF(MITER.GT.MITMAX)GO TO 300
TCOat.OUZUB
CALL OPTIM3X»F12. 3)
IF TCS VALUE WAS REACHED ONCE BEFORE, STOP WITH CURRENT
UPPER BOUND AS OPTIMAL,
CONTINUE
IFtMITER.Efl.DGO TO 250
J2»MITER-1
DO 240 Jj3l,J2
IF(CSAVE(JJ).EQ.TCS)GO TO 295
CONTINUE
PERFORM FEASIBILITY CHECK
IFEAS31
DO 230 Kal.10
IFCC5IMCK) ,GT.RHS£K))IFEAS*0
CONTINUE
SAVE OBJECTIVE VALUE AND PRINT DESIGN OF CURRENT SYSTEM
CALL OUTPUTCISW)
IF (IFEAS.EQ.OJWRITEC 10.235)
FORMAT(//42X,'»*» SOLUTION IS NOT FEASIBLE »**')
CSAVE(MITEH)*TCS
IF(IFEAS.EQ.O)GO TO 260
IF TCS LESS THAN CURRENT UPPER BOUND t REPLACE UPPER BOUND
IF(ZUB.LE.TCS) GO TO 260
ZUBaTCS
DO 255 131,20
DO 2S5 Kal,10
103
-------
260
C
C
C
C
C
C
C
275
285
280
290
C
C
C
295
305
C
C
C
C
300
405
C
C
C
C
C
310
C
C
C
330
MITMINsMITER
MITEPsMlTER+1
IFCMITER.GT.MITMAXJGO TO 300
COMPUTE NEW RECYCLE PENALTY VALUES
CALL PNALTYCCSIM)
RE-ARRANGE ENTRIES IN PROCESS LISTS SO THAT CANDIDATE
OPTIMAL PROCESS TRAIN APPEARS FIRST
DO 290 J»l,NTPS
L1«NPROCCJ)
DO 275 Lai,LI
IF(KPROCCL,J).EQ.IPOPTCJ}>GO TO 285
CONTINUE
LSTARaL
IFCLSTAR,EQ,1)GO TO 290
KSAVE*KPROCCLSTAR,J)
LlsLSTAR-l
DO 280 L»l,LI
L2»LSTAR-L+1
KPPOC(L2»J)3KPROC(L2-1,J5
CONTINUE
KPROC(l,J)aKSAVE
CONTINUE
BEGIN ANOTHER OPTIMIZATION ITERATION
GO TO 45
WRITECIO,30S1 JJ
FORMAT C/45X,»SA"ME SYSTEM AS DESIGN «,I3)
SENSITIVITY PHASE
NPHASE32
IF(KMAX.LE.l) GO TO 360
WRITECIO,405)
FORMAT(////,49X,'SENSITIVITY PHASE•/49X,17£ 1."})
PERFORM SYSTEM OPTIMIZATION USING RECYCLE PENALTY VALUES
ASSOCIATED WITH OPTIMAL SYSTEM DESIGN (PSAVE) AND IDENTIFY
ALL DESIGNS WITHIN 'FACTOR' OF OPTIMUM.
DO 310 1*1,20
DO 310 KalflO
PCIfK)aPSAVE(I,K)
LIHITaKHAX
FACTORmFACTR
TCO«t.Ol»ZU8
CALL OPTIMCNPHASE,LIMIT,FACTOR,TCO,TCP,1FEAS3
JFCIFEaS.EQ.OJSTOP
EVALUATE TRUE PERFORMANCE OF EACH DESIGN
ZUBalOO.E20
DO 350 KSal,LIMIT
DO 330 Jxl,NTPS
IPOPT(J)>IPSAVE(KS,J)
JMIXaJMSAVE(KS)
CALL SOLVECTCS,CSIM,NITER)
TSEaTSE+1.
NITSUMaNITSUM+NITER
HRITECIO.IOO) KS.TCS-TEPMAX
CALL OUTPUT (ISVJ)
IF(ZUB.LE.TCS)GO TO 350
104
-------
350
360
370
C
C
C
390
390
395
C
C
C
C
C
C
C
10
15
20
25
C
C
C
C
ZUBsTCS
MITMINsKS
CONTINUE
WRITE(IO,370)MITMIN
FORMAT(//45X,'BE5T DESIGN IS NUMBER ',13)
COMPUTE SEARCH EFFICIENCY
FEMst,
DO 380 Jal.NTPS
FEM»FEM»NPROCCJ)
FEM»FEM*NTPS*NITSUM/TSE
,5EFFsTUPE/FEM*100»
WRITE(IO,390)SEFF
FORMAT(//33X,'SEARCH EFFORT WAS ",F8,4»'% OF TOTAL',
« ENUMERATIONi)
WRITE(IO,395)
FOPMAT(//47X,' * SLUDGE MIXING POINT')
STOP
END
INPUT SUBROUTINE
SUBROUTINE INPUT(ISW)
INTEGER OS1,OS2
COMMON SMATXC20,45),DMATX(20,50),OMATXC20,50),IPC50),
1 INP,IO,ISl,IS2rOSt,OS2»N,IAERF,CCOST(5),COSTO(5),
2 ACOST(5),DHR,PCT,WPI,CCI,RI,AF,BATIO,CKWH,
3 CF,EER,EEP,ALAND
COMMON/PROC/ NPROCC20),KPROCC10,20),NWPS,MPSPS,NSSPS,
2 NTPS,NTPU,JSTRMC22),JSIDEC20),P(20,10),
3 IPOPT(20),PINFLOC20),EFFSTD(20),ISC(4S),IDC(45)
COMMON/COST/ CC20,10),CP(10),RHS(10),ri(10),UDR£50J,
2 IPSAVEC40,20),JMSAVEC40),TEPMAX,KMAX,FACTR,TUPE
3 «EEPMAX(20)
COMMON/TABLES/ VACFiC3,S,5),VACFS(3,5,5),VACF6<3»5,5)rVACF9(3,5,5)
2 ,CENTU3,5,5),CENT2C3,5,5),CENTS(3,5,S),CENT7C3,5,5)
DIMENSION TITLEC40)
EQUIVALENCE (JMIX, JSTRM£21J)
READ IN JOB TITLE
READ(INP,10)TITLE
FORMAT£40A2)
WRITE(IO,15)
FORMAT(X49X,'EXECUTIVE PROGRAM I/46X,'CQPTIMIZATION VERSION)'
2 /56X,'FOR»/33X,'PRELIMINARY SXNIHESIS OF WASTE ',
3 "TREATMENT SYSTEMS')
WRITECIQ,20)
FORMAT(//39X,"U.S. ENVIRONMENTAL PROTECTION AGENCY'X36X,
2 'MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY'X39X»
3 'SYSTEMS AND ECONOMIC ANALYSIS SECTION'X46X,
4 'CINCINNATI, OHIO 45268')
WRITECI0.25) TITLE
FORMAT£////I7X,40A2)
READ IN INFLUENT WASTE CHARACTERISTICS, EFFLUENT
DISCHARGE STANDARDS, AND ECONOMIC DATA.
PINFLO(1)»1,
PINFLOf20)30,
READCINP.l) (PINFLOCI),1x2,19)
PEADCINP.U (EFFSTO(I),I»1,5)
READCINP,!) CCI.MPI.PI,YRS,DHR,PCT,CKWH,RATIO,CF
AFaRI*(l.+RI)*«YRS/C(l.+RI)e#XRS-l.)
105
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
30
c
c
c
c
32
c
c
c
33
35
40
READ IN t HASTEWATER PPQCESS SI AGES,
I SECONDARY SLUDGE PROCESS STAGES,
* PRIMARY SLUDGE PROCESS STAGES, TOTAL * PROCESS UNITS,
SLUDGE RECYCLE STAGE, AND STABILIZATION STAGES FOR
SECONDARY AND PRIMARY SLUDGES.
READCINP,2)NWPS,NSSPS,NPSPS,NTPU,JRECYC
ESTABLISH SEGMENT ID NOS. FOR PROCESS STAGES AMD ZERO OUT
LENGTH OP PROCESS LISTS.
NTPS»NWPS+NPSPS+NSSPS
1-2
3-4
5-fr
7-10
11-12
13-14
15-17
20
21
22
JSTPH CODE
HASTEWATER PROCESSING STAGES
SECONDARY SLUDGE PROCESSING STAGES
PRIMARY SLUDGE PROCESSING STAGES
SLUDGE STREAM FROM HASTEWATER TREATMENT
SECONDARY SLUDGE RECYCLE STREAM
PRIMARY SLLUDGE RECYCLE STREAM
HASTEWATER, SECONDARY AND PRIMARY SLUDGE EFFLUENT STREAMS
RECYCLE RETURN STAGE
SECONDARY AND PRIMARY SLUDGE MIXING STAGE
NULL PROCESS NUMBER
JSTRM(l)al
J5TRM(21aNWPS
JSTRM(3JaJSTRM(2m
JSTRM(4)aJSTRMC3)+NS5PS-l
JSTRM(S)aJSTRMC4m
JSTRM(6)aJSTRM(5J+NPSP5-l
JSTRM ( 7 ) a JSTPM C 6 ) +• 1
JSTRM(10JaJSTRMC7)+NKPS-l
JSTRMCUJaOSTRMUOm
JSTRM(12)3JSTRM(U>+NSSPS-1
JSTPH (135aJSTPM{ 12) -H
JSTRMC14)*JSTRM(13)+NPSPS-1
JSTRHC15)3JSTRM(14J-fl
JSTRM < 1 6 ) aJSTRM U S ) + 1
JSTRMCl7)aJSTRM(16)+l
JSTRMC20)aORECYC
DO 30 J»1,NTPS
NPROCCJlaQ
NaO
READ IN STAGES ASSIGNED TO SLUDGE STREAMS FROM
HASTEWATER TREATMENT
REAOCINPr2) (JSIDECI),Izl,NHPS)
UJaJSTRM(3)
DO 32 I3JJ,NTPS
JSIDECIJaJRECYC
READ IN INFORMATION ON ALTERNATIVE PROCESS UNITS
DO 33 I*1,NTPS
EEPMAX(I)aO.
HRITECIO,35)
FORMAT(////47X»lPROCESS ALTERNATIVES' /47X»20C '_') 5
HRITE(IO,40)
FORMATC//' OPTION PROCESS STAGE SIDESTREAM»/
» HO, HO. NO. DESTINATION", i7X-, • REMARKS-'
»
'
t
106
-------
45
50
51
53
55
60
C
C
C
115
120
125
130
135
C
200
210
00 60 K»1,NTPU
READ(INP,3)J,N,IPROC,EMAX,UDRCN)
IF (EMAX »GT. EEPMAXCJ)} EEPMAXCJJaEMAX
READ(INP,45) (TITLE CJJ),OJ= 1,40 3
FORMATC40A23
WRITE(IO,50) N,IPROC,J,J5ID£C>J), (TITLE (JJ) ,JJ=1 , 40)
FORMATC1X, 14,318, 9X.40A25
NPROC(J)»NPROC(jm
KPROCCNPROC(J),J)=N
IP(N)»IPROC
IF(IPRQC.EQ,0)JSTRMC22)sN
IFUPROC.EQ.OJGO TO 60
READUNP,1)CDMATXU,
IF(IPROC.EQ.7}GO TO 51
IFCIPROC.EQ.17)GO TO S3
IFCIPROC.EQ.25JGO TO 35
GO 'TO 60
LaDMATXCil»N)
16)
READ(lNP.l)
READ{INP,ntCCENT5(I,JJ,L),Ial,3),JJ=U53
READ(INP.lK(CENT7CI,JJ,L)»I»lf3),Jj3l,5)
GO TO 60
NSAVE3DMATXO,N)
READ(INP,1) (DMATX(I,NSAVE),Isljl6)
NSAVE»DMATX(2,N)
REAOdNPd) CDMATXC I, NSAVE3 ,131,16)
GO TO 60
CONTINUE
READ IN SELECTION CRITERIA DATA
00 115 Kal,10
W(K)sO,
WRITECIO,120)
FORMAT(X///48X, iSELECTION CRITERIA ' X48X, 18 ( l_! 3//)
«RITE(IO,125)
FORMAT (32X» ICRITERION i ,2SX» 'WEIGHT » , 1 IX, 'LIMIT i ,
READaNP,l)CW(K),K3l,8)
HRITE(IO,130)(W(K),RHS(K),K3l,4)
FORMAT(25X,' 1. INITIAL CONSTR. COST, MS I,6X,2CF10,2,8X),
2 /25X,' 2. ANNUAL 0 i H COST, SXMG',9x,2(F10.2,8X),
3 /2SX,' 3. TOTAL ANNUAL COST, SXHG',9x,2-
107
-------
220
C
C
C
160
C
C
C
C
C
C
C
C
70
80
1
2
3
4
C
C
C
C
C
C
20
WRITE (10, 220) RATIO, CKHH,CK,RI,AF
FORMATC33X," COST ESCALATOR FOR MISC. FEES' ,6X,F12.4/
33X,'-COST OF ELECTRICITY, S/KWH > ,9X, F12.4/
33X,l FUEL CONVERSION EFFICIENCY ' ,9X,F12.4/
33X, I DISCOUNT RATE > , 22X,F12. 4X
33X,« CAPITAL RECOVERY FACTOR < , 12X,F12.4)
CCHCCI/1.506
WPIaWPI/1.122
INITIALIZE TARGET VALUES OF CRITERIA LIMITS
TEPMAXaO.
00 160 Jxl,NTPS
TEPMAX*TEPMAX+EEPMAXCJ)
RHSt5)»TEPMAX-RHSC5)
RHS(6)aTEPMAX+RHSC6)
READ IN * NEXT BEST DESIGNS WANTED WITHIN iFACTR' OF
OPTIMAL DESIGN.
READ(INP,4) KMAX,FACTR
FACTR*U+FACTR
IF SYSTEM DESIGN IS TO BE FIXEO, READ IN PROCESS UNIT NOS,
AND FIND MIXING STAGE FOR SECONDARY AND PRIMARY SLUDGES.
READCINP,2,END380MIPOPTCJ),J3l,NTPS)
ISW»1
JT»JSTRMC4)
JMIXaJSTRH 1 5 ) +NSSPS
IF CIP(IPOPTCJTJ) ,NE. OJ GO TO 80
JHIX»JMIX-1
JT*JT-1
ir CJT .GT. JSTRM(2)1 GO TO 70
JHIXaJSTRHCS)
RETURN
FORMATfSFlO.O)
FORHATC8IIO)
FQRMAT(3I10r2F10.03
FORHAT£HO,F10.0J
END
OUTPUT SUBROUTINE
SUBROUTINE OUTPUT CISWJ
INTEGER OS1,OS2
COMMON SHATXC20,45),DMATXC20,50),OMATX(20,SO),IPt50),
INP,IO,IS1,IS2,OS1,OS2,N,IAERF,CCOSTC5),COSTOCS),
ACOSTC5J,DHR,PCT,.WPI,CCI,Rl,AF^RATia,CKWH,
CF,EER,EEP, ALAND
COHMON/PROC/ NPROC£20),KPROCC10,20),NWPS,NPSPS,NSSPS,
HTPS,NTPU,JSTRM( 22 J,JSIDEC20:,P (20,10),
IPOPTf 20 ) , PINFLO ( 20 ) , EFFSTD ( 20 ) , ISC C 45 } , IDC ( 45 )
COMMON/COST/ CC20,10),CPC10),RHSC10),WUO},UDRC50),
1PSAVE(40,20)»JMSAVE(40),TEPMAX,KMAX,FACTR,TUPE
,EEPMAX(20)
DIMENSION CTOTC10)
FORMAT(//» STAGE
2 » NO,
3
JMIX»JSTRM(21)
PROCESS
OPTION
SLUDGE, " ,38X, 'SELECTION CRITERIA'/
TONS/DAYI,2X,8(6X,I1,4X)X
108
-------
30
C
C
50
C
60
90
C
95
110
120
130
140
190
200
210
220
230
240
250
255
STOTaO.
DO 30 K»l,10
CTOTCKJaQ,
DO 120 J»1,NTPS
SMIX*' '
NalPOPTCJ)
IPROCaiPCN)
IFaPRQC.EO,0)GO TO 110
IFCJ.GT.JSTRMC2))GO TO 50
OS2»NTPS+J
S2»S«ATX (2 ,OS2)»SMATXC10,OS2)»8.. 33/2000.
STOTBSTOT+S2
GO TO 60
S2aSMATX £ 2, J)»SMATXC10,J)*8. 33/2000.
IF(J,EQ.JMIX)SMIXa«»«
WRITE t 10, 90) J,SMlX,N,S2,(CCJ»K),Ksl,8)
FORMAT(2X*I2,A1,5X,I2,5X,F8,2,2X»8F11.2)
DO 95 K=l,10
CTOT /40X,35C '•' }
//49X,»VQI,UME FLOW, MGD ' /49X, ICONCENTPATION , MG/L«/15X,
'CONSULT PROGRAM REFERENCE MANUAL FOR MEANING OF PROCESS INPUT* ,
• AND OUTPUT DESIGN DATA.1//)
DO 260 J»1,NTPS
N»IPOPTtJ)
IPROC*IP,9X,
'SFHi,8Xr'SBOD' ,9X, 'VSS1 ,9X, 'TSS' )
WRITE (10, 240) (SMATXC I, IS 13,1=2,10), CSMATX C I ,031 ) , 1=2 , 10 ) ,
£SMATX(I,OS2),I*2,10)
WRITECIO,255)
FORMAT(7X,"DOC«,8X, 'ONBC ' • 10X, 'ON ' , 10X, ' DP ' , 9X, ' DFM ' , 9X, ' ALK I ,
8X,"DBODi,9X,iNH3i,9X,>N03')
WRItE(IOr240)-(SMASX
-------
260
145
C
C
C
C
90
10
C
C
C
C
C
35
40
C
C
C
SO
C
C
C
too
CONTINUE
RETURN
END
SYSTEM SOLUTION SUBROUTINE
SUBROUTINE SOLVECTCS,CSIM,NITER)
INTEGER OS1.0S2
COMMON SMATXC20,45),DMATXC20,50),OMATXC20,50),IP(50),
INP,IO,IS1,IS2,OS1V"OS2,N,IAEPF,CCOSTC5),COSTOC5),
ACOSTC5),DHR,PCT,WPI,CCI,RI,AI?,*ATIO,CKWH,
CF,FER,EEP,ALAND
COMMON/PROC/ NPROCC20),KPROCUO,20),HWPS,NPSPS,NSSPS,
MPS,NTPU,JSTRMC22),asiOE(20),PC20,lO),
IPOPT(20),PINFLO(20J,EFFSID(20),ISCC4S),IDCC45)
COMMON/COST/ C(20,10),CPC10),RHSC10J,W(10),UDP(50),
IPSAVEC40,20).JM5AVEC40J,I£PMAX,K«AX,FACTR,TUPE
,EEPMAX(20)
DIMENSION CSIMC10),TRECYCC20),RECYCC20)
DATA NITMAX/20/,EPS1/.001/,EPS2/,0001/
00 5 I»l,20
RECYCCDsO.
DELSUMaO.
NITER*0
EVALUATE TOTAL SYSTEM PERFORMANCE WITH CURRENT VALUE
OF RECYCLE FLOW
NITERSNITE.R + 1
IFCNITER.GT.NITMAX) GO TO 100
DO 10 lal,20
TRECYCCI)«RECYCCI)
SMATX(I,1)=PINFLOCI)
SUMOLDsDELSUM
CALL SYSTEM(TCS,CSIM,RECYC)
MIX RECYCLE FLOWS OFF OF SLUDGt UNITS TOGETHER
THE VALUE OF J SHOULD BE SET TO THE DIMENSION OF THE SECOND
INDEX OF SMATXU.J).
J2»JSTPM(61
J3=JSTRMf20)
CALL SMIX(J,J1,J2,J3)
00 40 1*2,20
PECYCtI)3SMATX(I,J)
CHECK FOR CONVERGENCE
ISTOP»1
DELSUMaO.
DO SO 1=2,20
DELaABS(PECYC CI)-TRECYC(I))
DELSUMxDELSUM+DEL
IF(DEL,GT.EPS1»RECYC(IJ) ISTOP^O
CONTINUE
IF(A8StDELSUM-SUMOLD).LT.EPS2*DELSUM) GO TO 100
IFCISTCP.EQ.l) GO TO 100
GO TO 90
CONVERGENCE IS ATTAINED.
RETURN
END
110
-------
V.
c
c
c
c
c
c
c
c
c
c
c
c
15
20
25
C
c
c
c
30
C
c
c
c
c
OPTIMIZATION SUBROUTINE
IMPLICIT ENUMERATION OPTIMIZATION PROCEDURE
SUBROUTINE OPTIMCNPHASE, LIMIT, FACTOR, FMIN, PMIN, IFEAS)
INTEGER QS1.QS2
COMMON SMATXC20,45),DMATXC20,50),OMAtXC20,50),IPC501,
1 INP,IO,IS1,IS2,OS.1,OS2,N,IAERF;CCOSTC5),COSTOC5),
2 ACOSTC5),DHR,PCT,WPI,CCI,RI,AF»RATIO,CKHH,
3 CF,EER,EEP, ALAND
COMMON/PROC/ NPROC(20),KPROCUO,20),NWPS,NPSFS,NSSPS,
2 NTPS,NTPU,J5TRMC22J,JSIDE(20J,PC20.10),
3 IPOPTC20),PINFLOC20),EFFSTDC20),ISCC45),IDCC45)
COMMON/COST/ CC20, 10) ,CP< 10) ,RHS C 10} i »C 10) ,UDR{50) ,
2 IPSAVEC40(r203.JMSAVEt40)»TEPMAX,KMAX,FACTR,TUPE
3 .EEPMAXC20)
DIMENSION LSAVE(20),FSAVEt40)»IPMIN(20)
DIMENSION FTEMPClO),PTEMpaOJ«FJC20,10),PJC20,10)
EQUIVALENCE (JLEFF, JSTRHC15) ) » (JSSEFF, JSTRMC16) ) ,
2 L
N«KPROCCL,J)
IPROC»IP(N)
ISlaJ
IS2«0
IF(J.EQ.JSTRM£2))OSl3jLErF
IF(J.EQ,JSTRM(4) lOSlstJSSEFF
IF(J.EQ.JSTPM£6))OS13JPSEFF
IF(J.EQ.JSTRM(3))GO TO 30
IFCJ.EQ.JSTRMCSnGO TO 40
GO TO 60
JaSTART OF SECONDARY SLUDGE PROCESSING.
MIX SECONDARY SLUDGE STREAMS TOGETHER.
J13JSTPM(1)
J23JSTRMC23
CALL SMIXCJ,J1.J2,J5
IDCCJ)*!
ISCCJ)30
IF CSMATXt2rJ3 .GT. 0.) ISCCJ)*2
GO TO 80
JaSTART OF PRIMARY SLUDGE -PROCESSING.
MIX PRIMARY SLUDGE STREAMS TOGETHER.
FIND MIXING POINT OF PRIMARY AND
SECONDARY SLUDGES t JMIX) .
m
-------
c
40
45
50
C
60
C
C
C
65
70
C
C
C
C
C
80
75
85
C
C
C
C
95
C
C
C
JlsJSTRMCl)
J2»JSTRM(2)
CALL SMIXCJ,J1,J2,J)
IDCCJ)»1
ISC(J)»0
IF (8MATXC2.J) .GT. 0.) ISCCJJel
JT*JSTRM(4)
JMIX»JSTRM(5)+NSSPS
IFCIPCIPOPTCJT)).NE.O)GO TO 60
JMIX«JMIX-1
JT«JT-1
IFCJT.GT.JSTPMC2))GO TO 50
JMIXSJSTRMC5)
IF (J .EQ. JMIX) GO TO 65
IFCJ.NE.JMIX .OR. L .GT. DGO TO so
J*JMIX. MIX PRIMARY AND SECONDARY SLUDGES TOGETHER,
TEMPlasMATXC2,JSSEFF)+SMATXC2,J)
00 70 1x3,20
TEMP2sSMATX(2,J)»SMATX£l.J)+SMATXC2,JSSEFF)#SMATXCI,JSSEFF)
SMATXCIrJJ*TEMP2/TEMPl
SHATXC2/JJ3TEHP1
ISCCJ)3lSCCJ)-HSCCJSSEFF)
IF aSC(JJ ,EQ. 2) IDCCJ)*IDC(JSSEFF3
IF CISCCJ3 .EQ, 3) IDCt J)*10*IDCC JJ+IDCCJSSEFF)
EVALUATE SELECTION CRITERIA FOR CURRENT PROCESS AND ADD
TO SYSTEM VALUES, CHECK IF CONSTRAINTS ARE MET AND
CURRENT UPPER BOUND NOT EXCEEDED,
CONTINUE
CALL UNIT(J,IPROC)
PSUM*0,
FSUMaO.
DO 85 K>1,10
PTEMPCK)»Pa(J,KJ+CP(K)
IFCK,EQ.5JFTEMP
-------
100
c
c
c
105
C
C
110
C
c
120
C
C
C
130
C
C
C
140
C
C
160
170
180
C
C
C
C
C
90
C
C
c
190
JMMtroajMIX
DO 100 Jal.NTPS
IPMIN(JJsIPOPTCJ)
IF THIS IS SENSITIVITY PHASE* INSERT NEW SOLUTION ' INTO LIST
IF(NPHASE,EQ,1)GO TO 180
IFCKOUNT,EQ.O)GO TO 120
LOCATE POSITION OF LARGEST ENTRY IN LIST
FMAXnO.
DO 110 Mal.KOUNT
IF(FTOT.EQ.FSAVECM))GO TO 180
IF(FSAVECM),LE.FMAX)GO TO 110
FMAXaFSAVECM)
KFMAXaM
CONTINUE
IFCFTOT.GE.FMAXJGO TO 140
IFtKOUNT.EQ.LIMITJGO TO 130
LENGTH OF LIST IS LESS THAN LIMIT
KINaKOUNT+1
KQUNTSKIN
GO TO 160
LENGTH OF LIST EQUALS LIMIT. REPLACE LARGEST ENTRY WITH
NEW SOLUTION.
KINaKFMAX
GO TO 160
NEW SOLUTION IS LARGER THAN LARGEST ENTRY IN LIST. IF LENGTH
OF LIST EQUALS LIMIT, REJECT NEW SOLUTION.
IF(KOUNT,EQ,LIMIT)GO TO 180
FMAXaFTOT
KFMAX«KOUNT+1
GO TO 120
PLACE NEW SOLUTION INTO LIST.
FSAVECKlNJaFTOT
DO 170 Jal.NTPS
IPSAVE(KIN,J)*IPOPTCJ3
JMSAVE(KIN)aJMIX
CONTINUE
JaNTPS
MOVE TO NEXT PROCESS OPTION AT CURRENT STAGE
IF NO MORE OPTIONS THEN MOVE BACK ONE STAGE
LaLSAVE(J)+l
IF(L.LE,NPROCCJMGO TO 25
IF(J.GE,JSTART)GO TO 90
IF CANNOT MOVE BACK ANY MORE STAGES THEN STOP THE SEARCH
JMIXsJMMIN
00 190- Jal.NTPS
IPOPTCJ)aIPMINCJ)
LIMITaJCOUNT
RETURN
END
113
-------
c
c
c
5
C
C
C
C
c
c
c
10
C
C
C
c
30
C
C
C
c
SYSTEM EVALUATION SUBROUTINE
SUBROUTINE SYSTEMCTCS,F, RECYC)
INTEGER OSUOS2
COMMON 5MATXC20,45),DMATXC20.50),OMATXC20,50),IPC50),
^P£™a'IS1'IS2'OS1'OS2'N'IAERF'CCosTC5),COSTCHS>,
ACOST<3),DHR,PCT,WPI,CCI,RI,AF,RATIO,CKWH,
CF,EER,EEP, ALAND
COMMON/PROG/ NPROCC20) ,KPROCC 10,20) ,NHFS,NPSPS,NSSPS,
NTPS,NTPU,JSTRM{22),JSIDE120),P(20,10),
„„,.,,„„ IP°PT<20)'PINFLOC203,EFFSIDC20),ISCC45),IDCC453
COMMON/COST/ CC20.103 ,CPC10) ,RHSC10} ,w'ciO} ,UDR<503 ,
IPSAVEC40,20},JMSAVEC40),IEPMAX,KMAX,FACTR,TUPE
9 EEPMAX C 20 }
DIMENSION FU03.RECYCC20)
EQUIVALENCE (JLEFF, JSTRMflS) ) , ( JSSEFF, JSTPH( 16J ) , (JPSEFF,
JSTRMC17)),CaRECYC,J5TRM(20)),(JMIX,JSTRMC21)}
TCS»0.
DO 5 Kal,10
FCKJ30,
EVALUATE SELECTION CRITERIA FOR THE PROCESS SELECTED
AT EACH STAGE,
DO 90 J«1,NTPS
N3IPOPTCJ)
IPROC»IP(N)
IS1»J
IS2>0
OS1»J+1
IP(J.EQ,JSTRM(2))OSl5BJLEFF
IF(J.EQ.JSTRMt4})OSlsJSSEFF
IFCJ.EO.JSTRHC63)OS1«JPSEFF
OS2»NTPS*J
IFCJ.Efl.JRECYOGO TO 10
IFCJ.EQ.JSTRM(33)GO TO 30
IFCJ.EQ.JSTRMC5))GO TO 40
GO TO 60
J3RECYCLE MIXING POINT. MIX INFLUENT AND RECYCLE STREAMS.
TEMP1»SMATX(2,J)+RECYC(2)
DO 20 133,20
TEMP2«SMATXC2,J)»SMATXCI,J)+RECYCi:2)*RECYCCl)
SMATXCI,J)3TEMP2/TEMP1
SMATXC2,J>aTEMPt
SMATXCl,J)»l.
GO TO 80
J»START OF SECONDARY SLUDGE PROCESSING.
MIX SECONDARY SLUDGE STREAMS TOGETHER.
JlatJSTRMtn
J2sJSTRM(23
CALL SMIXCJ,J1,J2,J)
IDCCJ)»1
ISCCJJaO
IF (SMATXf2,J) ,GT. 0.) ISC(J)*2
GO TO BO
J»5TART OF PRIMARY SLUDGE PROCESSING.
MIX PRIMARY- SLUDGE STREAMS TOGETHER.
114
-------
40
60
C
C
C
70
C
C
C
30
81
82
35
90
95
C
C
C
C
JlaJSTRMU)
J2*JSTRM£2)
CALL SMIX£J,J1.J2,J)
IDC£J)«1
ISC(J)»0
IF £SMATX£2,J) .GT. 0.) ISC(J)»1
IF(J.NE.JMIX)GO TO 80
JsMIXING POINT OF PRIMARY AND SECONDARY SLUDGES.
TEMP1*SMATX£2,JSSEFF).+SMATX£2,J)
DO 70 1*3,20
TEMP2sSMATX£2,JSSEFF>»SMATX£I,JSSEFF)+SMATX£2,J}*SMATX£I,vn
SMATX(I,J)»TEMP2/TEMP1
SMATX£2»J)sTEMPl
ISC£J)»ISCf J)+ISC£JSSEFF)
IF ClSCtJ) .EQ. 2) IDC(J3aIDC(JSSEFF)
IF (ISCtJ) .EQ. 3) IDC£J)slO*IDC£vmiDC£JSSEFF)
DETERMINE PERFORMANCE OF PROCESS N AT STAGE J.
CALL UNIT£J,IPROC3
DO 85 KS1.10
IFCK,EQ.5)GO TO 81
IF£K.EQ.6)GO TO 82
F(K)3F(K)-)-CCJ»K)
GO TO 85
F(K)»F(K)+EEPMAX£J)-CCJ,K3
GO TO 85
CONTINUE
CONTINUE
DO 95 K'1,10
C
C
C
RETURN
END
UNIT PROCESS EVALUATION SUBROUTINE
SUBROUTINE UNIT£J,IPROC)
INTEGER OS1,OS2
COMMON SMATX£20,4S),DMATX£20,50),OMATXC20,50),IP£SO),
1 INP,IO,ISl,IS2,OSl,OS2,N,IAERF,CCaST£5),COSTO£5),
2 ACOST£S),DHR,PCT,,WPI,CCI,PI,AF,RATia,CKWH,
3 CF,EER,EEP,ALAND
COMMONXPROCX NPROC £20) ,KPROC £ 10,20),NWPS,NPSPS,NSSPS,
2 NTPS,NTPU*JSTRM£22),.JSIDEC20),P£20,10),
3 IPQPTC20),PINFLO£20),EFFSTD£20),ISC£4S),IDC£45)
COMHON/COSTX C(20 , 10") ,CPf 10) ,RHS£ 10) , W(10) ,UDR£50 ) ,
2 IPSAVE£40,20),JMSAVE£40),TEPMAX,KHAX,FACTR,TUPE
3 ,EEPM&X(20)
COMMONXTABLESX VACFl£3,5,53,VACF5£3,5,5),VACF6£3,.5,5),VACF9£3,5,5)
2 ,CENT1£3,5,S),,CENT2(3.5,5),CENTS£3,5,S),CENT7£3,5,5)
DIMENSION SAVEC20)
EQUIVALENCE £J2LP» JSTRM£2)-) » £JLEFF, JSTHMf 15) ) ,
2 £JMIX,JSTRM£21))»£JSSEFF.JSTRM£16))
TUPE»TUPE*1.
INITIALIZE ERROR INDICATOR
IAERF*0
115
-------
c
c
INITIALIZE OUTPUT STREAMS ANO dtLtCTION CRITERIA
15
16
C
C
C
C
C
C
10
C
C
C
20
C
c
c
30
C
C
C
60
C
C
C
C
C
C
70
C
c
71
73
ISCCOSt
IOCCOS1 )slDC(ISl)
00 5 1*1,20
SMATXCT»OSnsSM4TX(I. IS1)
s--.Arxcr,os2)=o.
00 15 Tt=t,5
CCOST(II)sO,
cosTocri)=o.
ACOSTCTHsO.
CONTINUE
DO 16 K=l,tO
CP(K)sO,
CCJ,K)aO.
EER=0.
EEP=0,
ALAND=0.
DETERMINE WHICH PROCESS SUPROUIINt TO CALL
IFCIPPOC.EQ.OGO TO 560
IF(SMATXC2,I51) .EQ.O. )GQ TO 560
GO 10 (10, 20, 30, 560, 560, 60, 70. SO, 90, 100, 110, 120, 130, 140, 150, 160,
170, 180, 560 ,560, 360,220, 230, 2 40, 250. 2eO) ,IPPOC
GO TO 560
PRELIMINARY TREATMENT
CALL PPEL
GO TO 500
PPIMAHY SEDIMENTATION
CALL PRSET
GO TO 500
ACTIVATED SLUDGE
IF CDMATA(1,N) .GT. . 75* (S-MATXC8 » IS1J +SMA TX C1 7 , IS1 ) )
GO TO 225
CALL AERFS
IF(IAEPF)50U,500,22S
ANAEROBIC DIGESTION
CALL DIG
UPDATE SLUDGE DIGESTION CODE
IDCC051)s3
GO TO 500
VACUUM FILTRATION
DETERMINE SLUDGE TYPE
I1=ISC(IS1)
DETERMINE TYPE OF STABILIZATION
GO TO (71,71,73),II
JlsIOCCISl)
J2=J1
«lsl,
«2=0.
GO TO 74
IFCIDCflSU.LT. 10)60 TO 71
JlsIDCf ISD/10
JisIDCfJS6EFF)
116
-------
c
c
74
C
C
C
C
80
81
C
C
82
C
C
83
C
C
c
c
90
C
c
c
c
c
c
c
c
100
105
TEMPlsSMATX(2,JMIX)*SMATXC10,JMIX)
TEMP2»SMATXC2,JSSEFF}*SMATXC10»JSSEFF)
W2*TEMP2/TEMP1
W1»1,-W2
DETERMINE PROCESS PARAMETERS
L«OMATX(U,N)
DMATXa,N)»Wl»VACFiai,Jl,L)+W2*VACFKU,J2,L)
DMATXt5»N)»Wl*VACF5airJlrL)+W2#VACF5CIi,J2,L)
DMATX(6,N)»Wl*VACF6CIl,JtrL)-t'W2»VACF6(Il,J2,L)
DHA'EX(9»N)«Hl»VACF9(ll,aUL>*»2*VACF9(Il,J2,L)
CALL VACF
GO TO 500
GRAVITY THICKENING
SAVE SECONDARY INFLUENT STREAM
IFC1S2.EQ.ODGO TO 82
DO 81 I32>20
SAVE
-------
c
c
c
c
c
c
c
c
c
c
c
110
c
c
c
140
145
C
c
c
ISO
c
c
c
160
C
C
c
c
no
CHECK THAT MINIMUM INFLUENT SOLIDS CONC. IS MET
IF(3MATX(10,IS1)/10000..LT.1.12)GO TO 225
FIND COST OF SLUDGE HOLDING TANKS
SAVE£1)»DMATXC1,N)
SAVE(16>»DMATXU6,N)
DMATXCl,N)aDMATX(6,N)
DMATXU6,N)»DMATXC7,N)
CALL SHI
CCOSTC2)«CCOSTUJ
COSTaC2)«COSTOU)
DMATXCl.NJaSAVE(l)
DMATXtl6»N)aSAVEC16)
DETERMINE SLUDGE TYPE
DETERMINE CAKE SOLIDS CONC.
DMATX{l,N)aDMATXC2+Il,N)
CALL SEEDS
FIND COST OF LAND
CCOSTC3)»DHATXC8,N3*ALAND
GO TO 500
TRICKLING FILTER
IF CDMATXClrN) ,GT. .75*(SMATX(8» IS1 J+SMATXC 17. IS1 J ) )
GO TO 225
CALL TPFS
IF CIAERF .GT. 0) GO TO 225
GO TO 500
C
c
c
120
C
C
C
130
CHLQRINATION
CALL CHLOR
GO TO 500
FLOTATION THICKENING
IF(1S2.GT.OJSAVEC2)»SMATX{2,:
CALL TFLOT
IF(IS2.GT.O)SMATXC2,IS2)*SAVEC2)
GO TO 500
INCINERATION
CALL MHINC
DO 145 1*1,20
SHATX(T,OSn»0.
GO TO 500
RAH HASTEWATER PUMPING
CALL RHP
GO TO 500
SLUDGE HOLDING TANKS
CALL SHT
GO TO 500
CENTRIFUGATION-
DETERMINE SLUDGE TYPE
I1*ISC(IS1)
118
-------
c
c
171
173
C
C
174
C
C
C
C
180
183
185
186
C
C
C
C
C
220
C
C
C
C
227
C
C
220
DETERMINE TYPE OF STABILIZATION
GO TO (171.171. 173), II
JlsIDCf ISt )
J2=J1
Wlal.
GO TO 174
IFdOCf ISn.LT. 10)60 TO 171
JlalDCf ISD/10
J2*IDC{JSSEFF)
rEMPl=sMATX(2.JMIX)*SrtArX(10.JMIX)
TEMP2=SMATX(2,JSSEFF)*SMATX(10,JSSEFn
K2sTeMP2/TEMP1
DETERMINE PROCESS PARAMETERS
L=OMATX(9,N)
DM ATX (1 ,N)=Ht»CENTl lll.jl . L ) +*2»CENT U 1 1 »J2 , L )
TEMP=W1*CENT2CIl,J1 .L) M2*.CENT2 ( 1 1 , J2 . L )
DMATX(2.N)aTEMP*t ,E4
DMATX(5«N}sWt»CeNT5(Il,Jl.LJ+«2»CENT5(ll,d2,L)
DMATXC7.N)=Wl*CENT7CTl,Jl,l;)+W2*CENT7(ll,J2,L)
CALL CENT
GO TO 500
AEROBIC DIGESTION
FIND SRT OF ACTIVATED SLUDGE UNIT
DO 183 Jl=t.J2LP
LsIPCIPOPTCJt) )
IF(L,EQ.3)GO TO 185
IF(L.EQ.25)GO TO 185
CONTINUE
DMATX(3,N)=0,
GO TO 186
CONTINUE
CALL AEROB
UPDATE SLUDGE STABILIZATION CODE
IOCCOSl)s3
GO TO 500
LAND DISPOSAL
IF(OHATX(Of N) .EQ.DGO TO 227
CHECK TO SEE THAT SLUDGE IS STABILiZtD
IF UDCCIS1) ,LE. 1) GO TO 225
IF CIDC(ISl) .LT.10) GO TO 228
IF (IDC(ISI)XIO ,LE, 1) GO TO 225
IF flDC(JSSEFF) .LE. 1) GO TO 225
GO- TO 228
CHECK THAT SOLIDS IS OVER 15% IF LAMOF1LLING
TEMpsSMATXUO,ISi)»l.E-6
IF(TEMP.LT.0.15)GO TO 225
CHECK IF FINAL DISPOSAL INCURS ANK COST
IFCDMATXf 16,N),EQ.O, )GO T.O 560
CALL
119
-------
229
C
C
225
226
C
C
C
230
C
C
C
C
C
240
C
C
C
C
250
C
C
C
C
260
C
C
261
262
DO 229 1=1,20
SHATXCI, 031)30.
GO TO 500
INFEASIBLE OPTION, MAKE OBJECTIVE VALUE VERY HIGH.
DO 226 Kal,lQ
CCJ,K)«100.E20
CCJ.5)»0.
RETURN
LIME STABILIZATION
CALL LIME
ADJUST SLUDGE STABILIZATION CODE
ZDC(OSl)s2
GO TO 500
ROTATING BIOLOGICAL CONTACTOR
CALL RBC
GO TO 500
PRIMARY SEDIMENTATION - ACTIVATED SLUDGE - WASTE
ACTIVATED SLUDGE RETURNED TO PHIMAR* SETTLER
CALL PSASFS
IFCIAERF.GT.OJGO TO 225
GO TO 500
HEAT TREATMENT
FIND COST OF HOLDING TANK
IF(DMATX(8,N),EQ.O.)GO TO 261
SAVEC1)«DMATXC1,N)
SAVEC16)»DMATXU6»NJ
DMATXCl,N)aDMATXC8,N)
DMATXC16,N)3DMATXC9,N)
CALL SHT
CCOSTC3)sCCOST(l)
C05TO(3)3COSTOU)
DMATX(1,N)*SAVE£1)
DMATX(16»N)3SAVEC16)
CHECK IF SLUDGE DIGESTED AND FIND STREAM NOS. AT MIXING POINT
DMATX(3rN)>0.
IF(IDC(IS13.EQ.3) OMATXC3»N)d.
I1»JMIX
I2-JSSEFF
IFCJ.GE.JMIXIGQ TO 262
IlaJ
12*0
IFCJ.GE.JSTRMCSnGO TO 262
12*0
DMATX(1,N3*I1
DMATXC2.NJ3I2
FIND COST OF THERMAL REACTOR
CALL HEAT
IDCCOS1)«5
GO TO 500
120
-------
C
C
500
510
520
530
540
C
C
C
550
553
C
C
C
560
570
C
C
C
600
SUM SUB-UNIT AMORTIZATION AND O&M COSTS.
DO 540 Hal, 5
CCOSTClI3»CCOSTai3»CCI»RATIO
CaSTOClI3*COSTO(II3»RATIO
IFCACOST£II33520,510,520
ACOST£II3*CCOST£II3«AF/£3650,»PINFLO£23}
SO TO 530
ACOST£II3«ACOSTUI3*CCI»RATia
C£J,33sC£J,33+ACOST£II3+CCISTO£II3
C£j,i3«c£j,i3>ccosT£in
CfJ,2}»C£J,2>+COSTOUI3
CONTINUE;
EVALUATE SELECTION CRITERIA
C£«J,2)«C(J,23»10,
C£J,33«C£J. 33*10.
CCJ»5)»EEP/PINFLOC2)
C£J,63«C(Jf4)-C(J,5)
CCJ,75aALAND
C(J» 8}aUDPCN)
EVALUATE PENALTIES FOR EACH CRITERION
ir(J.LE.J2LP)GO TO 600
DO 555 Ksl.10
CPtK)ap(2,K)»SHATX(2,OS23
TEHPlasMATX(2,OS2)
DO 550 I»3,19
TEMP23SMATX(I,OS2)
IFCI.EQ.9)TEMP2aSMATXC9,OS2)-SHATX(3»a52)-SMATXC5,QS2)-SMATXC6/OS2)
IFCI.EQ.13)TEMP2»SHATXCl3,OS2>-SMATX(l8,QS2}-SMATX(19,aS23
IFtTEHP2.LE.O. )TEMP2»0,
CONTINUE
60 TO 600
PROCESS IS NULL PROCESS WITH ZERO COST
DO 570 1*1,20
SMATX(I,OSl)3SMATXCI,ISn
SMATXd, 032)30.
N»JSTRMC22)
GO TO 600
CHECK EFFLUENT STANDARDS ON BOO* TSS, TKN, HQ3, AND P
IF(J.NE.J2LP)RETURN
IFtSMftTX(9,JLEFF3+SMATX(17,JLEFF).GT.EFFSTDa))GO TO 225
IF(SMATXaO,JLEFF).GT,EFFSTD<23)GO TO 225
IF(SMATX(5,OLEFF)4-SMATX(i8,JLEFF)»GT»EFFST.DC3)3GO TO 225
IF(SMATX(10,JLEFF3.GT.EFFSTD(433GO TO 225
IF(SMATX(14,aLEFF3.GT.EFFSTD(533GO TO 225
RETURN
END
121
-------
c
c
c
c
c
c
10
c
c
c
c
IS
c
c
c
c
c
c
c
c
20
c
c
c
40
c
c
c
so
PENALTY SUBROUTINE
SUBROUTINE PNALTYCCSIM)
COMMON SMATX'C20,4S),DUMMY1C2000),IDUMMK58J,
DUMMY2C27)
COHMON/PPOC/ ID(225),JSTRMC22},JSIDEC20),,P(20,10),IPOPTC20),
PINFLOC20),EFFSTD(20),ISC(45),IDCC4S)
DIMEHSION TPECYCC20),DCC20,10),CRECYCC10),COPTaO).TSUMC10)
,CSIMUO),RECYCC20:)
00 5 K«l,10
TSUM(K)30.
DO 10 132,20
RECYC(I)«0.
DO 10 K*1,10
PU,K)aO.
DCCI,K)»0,
EVALUATE SYSTEM PERFORMANCE WITH NO RECYCLES
MIX RECYCLE FLOWS TOGETHER
CALL SYSTEM(TCS,COPT,RECYC)
JaJSTRMdV)
J1«JSTRMC3)
J2>JSTRM(6)
J3»JSTPMC20)
CALL SMIXCJ.J1,J2,J3)
DO IS 1^2,20
TRECYCCI)aSMATXCI»J)
EVALUATE MARGINAL TREATMENT CRITERIA FOR RECYCLE
COMPONENTS BY FINDING CHANGE IN CRITERIA FOR TREATING EACH
COMPONENT SEPARATELY.
Q1»TRECVC(2)
02».001*PINFLO(2) . '
IFCTRECYCC2).LT,Q2)RETURN
DO 200 132,19 v
GO TO (200,20,200,40,50,60.70,80,200,200,200,120,200,140,200,
140,170.190}, I
GO TO 200
RECYCC2)*TRECYCC2)
GO TO 180
SNBC
RECYCC2)»Q2
RECYCC4)aTRECYCC4J*Ql/Q2
RECYC£3)»RECYCC4)
PECYCt9)*RECYCC4J
FECYCtlO)*RECYCC4J
GO TO 180
SON
RECYCC5J»TRECYC(S)*Q1/Q2
RECYCt9)»RECYCC5)
RECYC(lO)3RECYCjt53
GO TO 180
122
-------
c
c
60
C
C
C
70
C
C
C
80
C
C
C
90
C
C
C
120
C
C
C
130
C
C
C
140
C
c
c
170
C
C
C
190
C
C
180
135
SOP ;
RECYCC23sQ2
RECYC<63»TRECYC< 63*01/02
RECYCC93*RECYCC63
RECYC(10)aRECYCC63
GO TO 180
SFH
RECYCC23=Q2
RECYCC7)»TRECYCC73»Q1/Q2
RECYCC103aRECYCC73
CO TO 180
SBOD
PECYC£2)sQ2
RECYCC83sTRECYCC83*Ql/02
RECYC(3)*RECYCC8)/1,87
RECYCC93SRECYCC8 3/1.87
RECYC(103>RECYCC8)/1,87
GO TO 180
OTHER VSS (NOT SOC, SON, OR SOP)
RECYC(2)=Q2
RECYCC93»CTRECYC(9)-TRECYC(33-TRECYC(5)-TRECYC(6))»Q1/Q2
RECYC(10)aRECYCC9)
GO TO 180
DNBC
RECYC(2)»Q2
RECYCC12)»TRECYC( 123*01X02
RECTfC£113«RECYCC123
GO TO 180
OTHER DN tHOT NH3 OR N03)
RECYCC23«Q2
RECYCU 3 3»{TRECYCC 13 3-TRECYCt 18 3-TRECYCC 19))*Q1/Q2
GO TO 180
DP AND ALK
RECYC£I)3TRECYC( 13*01/02
GO TO 180
DBOD
RECYC(23aQ2
RECYCC173»TRECYC( 173*01/02
RECYCCll3»RECYCa7)/l,87
GO TO 180
NH3
RECYC£23»02
PECYCtl8)aTRECYCfl8)«Ql/Q2
RECYCC13)»RECYCC18)
DO 18S 1132,20
IFCRECYC(II3.IjT.O.)RECYC(II)aO.
CALL SYSTEMCTCS,CRECYC»RECYC3
123
-------
186
194
195
200
C
C
210
C
C
C
C
C
C
220
C
C
C
225
230
C
C
C
C
C
10
20
30
40
50
60
DO 186 K*l,10
DC(I,K)*CRECYC(K)-COPT(K)
TSUM(K>«TSUM(K)4>DCa»K)
DO 195 11*2,20
RECYC(II)*0.
'CONTINUE
DO 210 1*1,20
RECYCm»TRECYC(I)
PENALTY • MARGINAL CHANGE IN CRITERION / MASS FLOW *
ADJUSTMENT FACTOR
ADJUSTMENT FACTOR a (CRITERION WX RECYCLE - CRITERION W/0
RECYCLE) / SUM OF MARGINAL CHANGES
DO 230 K«l,10
IF(TSUM(K).EQ.O.)GO TO 230
DEL* (CSIMCK) -COPT CK) )/TSUMCK)
P(2,K)«DC(2,K)*DEL/TRECYC(2)
DO 220 1*3,20
IFCTRECYC(I).EQ.O.)Gd TO 220
P(I,K)*DCU,K)#DEL/(TRECYCC2)*TRECYC(I))
CONTINUE
CORRECT COMPUTED PENALTIES FOR OTHER VSS AND ON
TEMP*TPECYC ( 9 } -TREC YC c 3 J-TREC YC < s ) -TPEC YC c t> )
IF(TEMP.LE.O.)GO TO 225
P(9,K)*P(9,K)*TRECYC(9)/TEMP
TEMP«TRECYC (13) -TRECYC £18) -TREC YC (19)
IF(TEMP,LE.O.)GO TO 230
CONTINUE
RETURN
END
STREAM MIXING SUBROUTINE
SUBROUTINE SMIX(J,J1,J2,J3)
COMMON SMATX(20,45),DUMMY1(2000),IDUMMY(58),
DUMMY2(27)
COMMON/PROC/ ID(223),NTPS,NTPU,JSTRM(22),JSIDEC20),P(20,10),
IPOPT(20),DUMMY(40),ISC(45),IDC(45)
SMATX(1,J)«1.
TEMPl'O.
DO 10 JT*J1,J2
IF (JSIDECJT) .HE. J3) GO TO 10
TEMP1*TEMP1+SMATX(2,JT+NTPS)
CONTINUE
IF(TEMpl.EO,0.)GO TO 50
DO 30 1*3,20
TEMP2»0,
DO 20 JT*J1,J2
IF (JSIDECJT) .HE. J3) GO TO 20
lEMP2*TEMP2+SMATX(2rJT+NTPS)»SMATX(I,JT+NTPS)
CONTINUE
SMATX(I,J)«TEMP2/TEMP1
SMATX(2rJ)*TEMPl
RETURN
DO 60 1*2,20
SMATX(I,J)*0,
SMATX(2,J)>0.
RETURN
END
124
-------
SUBROUTINE PREL
PRELIMINARY TREATMENT
INTEGER OS1,GS2
COMMON SMATXt20,45),DMATX{20,50),OMATXC20,50},IP(SO),
. INP.IO,IS1,IS2,OS1,052,N,IAERF,CCOST(S),COSTOC5),
. ACQSTCS),DHR,PCT,WPI»CCI,RI,AF»RATIO,.CKWH(.
. CF,EER,EEP,ALAND
DO 10 132*20
10 SMATXU,OSn»SMATX(I«ISl)
IPREL3DMATXC1,N)
X3ALOGCSMATXC2,ISn»DMATX<16,N))
IFCIPREL) 30,20,30
20 CCOST(1)*EXPC2.S66569+.619151*X3«1000,
G.O TO 40
30 CCOSTC1)»EXP<3.2S9716+.6191S1»XJ*1000,.
40 X3ALOGCSMATXC2,IS1))
OHPS»EXPC6.398716+.230956*X+.164959*X*«2-,014601»X**3)
XMHRS3EXP(5.846098+,206513*X+.068842*X*»2+,023824»X#»3=
. .004410*X**43
TMSU»EXP(7.23S657-|..399935»X-,224979*X»»2-t-.110099»X»»3-
. ,011026*X»*4)
COSTOCn««OHRS+XMHRS>«DHR«(l.+PCT)VrMSU»WPI)/SMATX(2,n/3650.
EEP«EXPt2.64866*.53261l»X-.034378»X**2t.007274»X#*3)
EER»EER+EXP(.47668+.256486»X-.051504*X**2+.02465»X**3)
EER*EER+DHATXC1,N)#3.01«EXP(.249*X)
RETURN
END
SUBROUTINE PRSET
PRIMARY SEDIMENTATION
INTEGER OS1.QS2
COMMON SMATXC20,45),DMATXC20,50J,OMATX(20,50) ,IP(503,
. INP,IO,IS1,IS2,OSI,OS2,N,IAEPF,CCOST(5),C05TO(5),
. ACOST(5),DHR,PCT,WPI,CCI,RI,AF,RATIO,CKKH,
. CF.EER.EEP,ALAND
HPWKsDMATX(3»N>
SMATX(2»OS2)=DMATX(1,N)*SMATXC2,IS1)/DHATX(2,N)
SHATX(2,QSl)=5MATX(2,ISn.SMftTXC2,OS2)
TEHPla(l.-DMATX(l,N))»SMATXC2,ISl)/SMATXC2,OSn
TEMP23DMATXCl,N)»SMATXC2,,ISn/SMATX{2,GS2)
DO 10 1=3,10
SHATXC1,OS1}»TEMP1*SMATXaTEMPl*SMATX(20,131)
SMATXC20»QS2)»TEMP2»SMATX(20,151)
DO 20 1311,19
SMATXCI,OS2)nSHATX(I,ISl)
20 SMATXCI,OS1)3SHATXCI,OS2D
GPS»-2780.»ALOG(DMATXtl,N)5-551.7
APS3SHATXC2,IS1)»1000./GPS»DMATX(16,N)
XBALOG(APS)
CCOST(1)«EXP<3.716354+,389861»X+.084S60»X»*2-,004718#X»#3)#
. 1000,
X3ALQG(APS/DMATXC16fN))
OHPS3EXP(5.846565>,254813*X-t'.ll3703»X»«2-.010942»X»»3)
XMHRS3EXP(5.273419+.228329*X+.122646»X«*2-.011672*X»*3)
THSU»EXP(5.669881+.750799*X)
CaSTO
-------
c
c
c
c
c
c
c
c
c
101
c
c
102
c
c
15
103
X3*IiOG
CBaCBl»SRT»*-.415
BOD2*CS»tl.+CB»SRTJ/(SRT»(CY»CK-CB3-l.)
INDEX33
GO TO 25
IFCABS(BODEFF»BODLIM}.LE.O,001*BODLIH)(>0 TO 30
126
-------
c
c
20
C
C
25
C
C
30
C
C
STEP 4 • DISCARD OLD POINT THAT IS ON SAME SIDE OF BODLIM AS IS THE
CURRENT 8QDEFF AND RETURN TO STEP 3
IFm»(BODEFF-BODLIM),LE.O.)GO TO 20
C
C
40
C
c
c
c
so
c
c
60
70
XlaX2
Y2«BODEFF-BODLIM
X2»SRT
GO TO 15
COMPUTE HRT. SETTLER EFFICIENCY, AND BODEFF
HRT»SRT/MLVSS*(MLRSS+CY*(BOD1-BOD2)/(1.+CB#SRT)*(1,+.2#CB*SRT))
XRSSBTSSt,I«/£MLVSS-t-MLISS*SRT/HRT)
BODEFFsBOD2+,97».8*XRSS»SRT/HRT«CY*{BODl-BOD2)/(l,+CB*SRT)
GO TO (101,102,103), INDEX
COMPUTE CONC. OF SOLIDS IN AERATOR
MLASSsSRT/HRT*CY*(BODl-BOD2)/(l.+CB«SRT)
MLRSSs.2»CB*MLASS*SRT+SRT/HRT*MLRSS
MLISS»SRT/HRT*MLISS
MLSSsMLVSS+MLISS
FIND HASTE SLUDGE SOLIDS CONC. AND FLOW RATE
SMATX(10,OSl)sTSSLIM
SMATX(10,OS2>*MLSSXRTURN*(1.*RTURN-HPT/SRT)
SMATX(2»OS2)«SMATX (2, IS1)*(MLSS*{1. +RTUR«)-SMATX( 10,051 )-
2 SMATX( 10, OS2)*RTURN)/(SMAIX(10,OS2)-SMATX(1 0,051))
SMATX(2,OSl)aSMATX(2,ISl)-SMATX(2,OS2)
COMPUTE CONC. OF SOLIDS SPECIES IN OVERFLOW
SMATX(4,OSl)3XPSS*(,2*CB»MLASS«SRT/2.46+SMATXU,ISl)*SRT/HRT)
SMATX(4,OSl)sSMATX(4,OSl)+XRSS«.2»MLASS/2.46
SMATXC3,OSl)»SMATXC4,OSl)-t-XRSS«.8»MLASS/2.46
SMATXC5,OSl>aXRSS»C.12*MLASS+.06»MLRSS)
SMATX(6,OS1)»XRSS*.025»MLASS
SMATX(7,OSl)sXRSS«MLISS
SMATX(0,OS1)3BODEFF-BOD2
SMATX(9,OS1 }sXRSS»MLVSS
SHATXC20,OS1 )=XRSS*MLASS
COMPUTE CONC. OF SOLID SPECIES IN UNDERFLOW
P»SMATX(10,OS2)/SMATX(10,OSl)
DO 40 I»3,9
SMATX(I,OS2}3SMATX(I,OS1)*R
3HATXC20,OS2)3SMATXC20,QS1)»R
COMPUTE DISSOLVED C, N, P, AND FIXED MATTER
SMATXClt »OS1)»SMATXC 12, IS1)+BC)D2/BOD1»(SMATXU1,IS1)-SMATXC 12,151 ))
SMATX(12,OSl)aSMATX(12,ISl)
SMATX(13,OS1>3SMATX(13,IS1)+SMATX(5,IS1)-SMATX(5,OS1)#SMATX(2,OS1)/
2 SMATX(2,IS1)-SMATX(5,OS2)»SMATX(2,OS2)/SMATX(2,IS1)
SMATX(14,OS1)»SMATX(14,IS1)+SHATXC6,IS1)-SMATX(6,QS1)«SMATX(2,OS1)/
2 SMATX(2,IS1)-SMATX(6,OS2)#SMATX(2,OS2)/SMATX(2,IS1)
SMATX(1S,OS1}BSMATXC1S,IS1)
CHECK FOR NITRIFICATION (SRT ,GE. 5 DAYS)
IF(SRT.LT.5.)GO TO 50
IP(SRT»,05«CKN,LE.1.)GQ TO 30
SMATXU 8,051 )»!.*( 1,/(CSRT».05*CKN )-!,))
SMATX(19,OSl)aSMATX(13,-OSl)-SMATX(lS,OSl)
GO TO 60
SMATX(19,OS1)*SMATX(19,IS1)
SMATX(18,OS1)>SMATX(13,081)»SMATX(19,OS1)
ADJUST ALKALINITY AND DISSOLVED BOD
SMATX(l6,aSl)*SMATX(16,ISl)-7.14#
-------
c
c
c
c
SIZE THE AERATION TANK
VAER«HRT»SMATXC2,1S13*DMATXU6,N3
X»ALOG(YAER*1000./7.48)
CCOSTC133EXPC2, 41 4380+.175682*X+.084742»X*#2-.002670»X**3 3*1000.
COSTOC1330.
COMPUTE AIR REQUIREMENTS
ARCFD*C1. 5-1, 42»cy)»5MATXC2.ISl 3* (BOD 1-80023*8.33
ARCFD3ARCFD+1.42»CB*.8*MLASS*VAER*8,33
ARCFD«ARCFD+4.6*CSMATX<19,OS1)-SMATXC19,IS133*SMATXC2,IS13*8.33
ARCFD3SRCFD/AEFF/.232/.075 ,
BSIZE»ARCFOX 1 440 . *DHATX U 5 , N 3
CFPCL3ARCFD/1,E6/SMATX(2,IS13
XaALOGtBSIZE/lOOO.)
CCOSTC2)3EXPC4,145454+.633339*X-I-. 031 939»X»*2-. 00241 9*X»*3 3*1 000,
X*AIiOG(BSIZE/1000./DMATXC15,N))
QHRSaExP(6.900586+,32372S#X+.059093*X»*2-.004926*X**3J
XHHRS3EXPC6.169937+.2948S3*X+-.175999»X**2-.040947*X**3+
2 ,003300»X*»4)
HP3BSIZE/DHATXC15,N)»8.l*144./(33000,*t8)
XKN*.8*HP
XKWPX»XKW»24.*365.
ECOST3XKWPY*CKWH
SCOST3EXP(.62138+,482047*X)*1000,
THSU»ECOST+SCOST»WPI
COSTO(2)=(COHRS+XHHRS3»DHR*(1.+PCT)+TMSU)/SMATXC2,1)/3650.
COMPUTE RETURN SLUDGE PUMPING REQUIREMENTS
QRxRTURN«SMATX(2,ISl)*DMATX(14,N)
72
74
76
78
80
C
C
CCOSTC333EXP(3.4815S3+.37748S*X+.093349*X*»2-.006222»X#*3)»1000.
X3AI,OGCQR/DMATX(14,M))
OHRS3EXP(6.097269+.2S3066»X-.193659*X*»2+,078201»X**3-
.006680*X**43
XMHRS3EXPC5.911541-.013158*X*.076S43*X»*2)
IF (QR-1.44J 72,74,74-
PEFF»,7
GO TO 80
IF (OR-10.08) 76,78r78
GO TO 80
PEFF3.83
YKwPY3QR»l.E6«HEAD/l440r/3690./pEIfF/,9*,7457»24,»365.
ECOST3YKWPX«CKWH
SCOST»EXP(S.051743+.r301610*X*.197183*X»*2-.017962*X**3J
TMSU3ECOST+SCOST»WPI
COSTOC3)3£tOHRS+XHHRS3#DHR»(l.*PC1ir)+TMaU)XSMATXC2|l)/3650,
COMPUTE FINAL SETTLER REQUIREMENTS
AFSaSMATXC2,aSl)*1000./GSS*DMATXU3,N)
AFS2».04»SMATXC2,IS13*tl.+RTURN)»HLSS/lOOO.»(SMATX(10faS2)X1000,3**,6
IFCAFS2.GT.AFS3AFS3AFS2
X3ALOGCAFS)
CCOST t4>=EXP (3. 71 6354*. 389861 *X+« 08 4560*X**2-. 0047 18»X»*3 )»1000,
X3ALOG(AFS/DMATXC13,NJ 3
OHRS*EXPC5.84656S-t-,2S4813*X-t-,113703*X»*2-.010942*X»*33
XMHRS»EXP(5.273419*.228329»X-t',122646*X»»2-.OU672»X»»33
TMSU«EXPt5.669881+.750799*X3
COSTO(43a(CaHRS+XMHRS3»DHR*(l.+'PCT3+TMSU»WPI3/SMATXt2,13/3650.
128
-------
C
C
FILL tN OUTPUT MATRIX VALUES
OMATXC1»N) 3BOD1
OMATX-CZrN) «SR$
OMATXC3»N) sXRSS
OMATXC4.N) sAFS
OHATX(5»N) aCK
OMATXC6.N3 3CB
OMATXC7.N) aVAER
OMATX(8»N) aVAER
OMATXC9.N) aMLASS
QMATXdO.NJaO.
OMATX(ll»N)=MLRSS
OHATX(12,N)s.2#CB«SRT»Ml,ASS
OMATX(l3,M)aHLI5S
OHATX(l4»N)»BOOt-BOD2
QHATX(IS,N3=RTURN
OMATXU6,tO=CKN
OMATX(l7,N)sARCFO
OMATXC18,NJ=BSIZE
DMATX(19,N)3CFPGL
COMPUTE ENERGY REQUIREMENTS
1000
RETURN
lAERFal
RETURN
END
SUBROUTINE DIG
C SINGLE STAGE ANAEROBIC DIGESTION
INTEGER'osi,os2
COMMON SMATX<20,45),DMATX(20,50),OMATX(20,503,IP(50),
. INP.IOflSl,IS2,OSl,OS2,N,IAERFfCCOST(5)»COSTOC53.
. ACOST(5),DHR,PCT,HPI,CCI,RI,AF,RATIO,CX*H,
. CF,EER»EEP»ALAND
C1DIG*.28XEXP(.036»C35.-DMATXC2,N)))
C2DIG*700,*EXP(,10»(35.-DMATX(2,N]))
DIG12aSHATX(3,ISl)-SMATX(4,ISl)+SMATXCU.ISn-SMATXC12,ISl)
TDaDMATX(l.N)
DIGt3s«C2DIG/tClDIG»TD-l.)
20 TEMPlatOIG12"DIG13)/(SMATX(3»ISl)+SMATXCll»ISin
SMATX(2lQSl)sSMATXC2.Isn
SMATX(3.0S1)=SMATX(4,IS1)+.75«OIG13
SMATXt4,OSl}aSMATX(4,ISl)
SMATX(5,OSn3(l.-TEMPl)*SMATX(SfISl)
SMATX(6>OS1)3C1.-TEMP1)«SHATX(6,IS1)
SMATXt7,OSl)aSMATX(7,ISn
SMATXC8,OSn3{SMATX(3,OSl)-SMATX(4»OSl))»1.87
SMATX(9,OSl)3SMATXt3fOSl)*2,38
SMATX(10,OSl)sSMATX(9,OSn*SMATX<7,OSl)
SMATX(U,OSl)3SMATXU2,ISl)-t',25»DIG13
SMATX( 12, OSnaSMATXC 12.ISI)
SMATXC13,OSn3SMATXU3,ISl)+SMATXC5,Isn»,65»TEMPl
SHATX{ 14,051 )»SHATX ( 14. IS1) -CTEMP 1»SMATX (*> > ISi }
SMATXC15,OS1)3SMATXU5,IS1)
SMATXC16,OSl)=SMATXC16.ISn+(SMATXtl3,OSi}-S«ATX(13,ISl)J*3,S7
SMATXC17,OSlJs(SHATX(11»OS13-SMATXC12,051))*1.87
SH ATX C 18, OSU=SM ATX C 18,131)
SMATXC19,asl)=SMATXC19,ISn
SMATX£20,OSl)3SMATXt20,Isn*.7S»DlG13/CSMATXC3,ISl)-
2 SMATX(4,ISin
CH43163.3S«(OIG12-DIG133*SMATX(2,IS1)
C02»24909*CDIG12-DIG13)*SMATX£2,IS1)-CH4
VDIGaSMATXC2,IS13»TD«1000.y7,48»DMATX(16»N)
X»ALOG(VDIG3
IF(VDIG-20.) 22.25,25
129
-------
20
22 CCOSTU3aEXPC4.594215+.127244«X..004001*X»*23»iOOO.
GO TO 28
25 CCQST(l)aEXPe7.679634-1.949689»X+.402610»X»*2-.018211»X»*33»
• 1000.
28 X3ALOGCVDIG/DMATXC16,N33
IFCVDIG-20.3 30,40,40
30 OHRS«EXP(6,163803 + .U6305»X-.012470*X*»2J
XMHRS3EXPC5.726981*.113674*X3
TMSU3EXPC6.531623-|..198417»X+.02J,660*X»*23
GO TO 50
40 OHRS*EXPC9.129250-1.816736*X+.373282»X*»2-.017290»X**33
XMHRS3EXPC 8, 566752-1. 768l37*X-t-. 363173»X»*2-.016620*.X*»3 3
TMSUaEXPf8.702803-1.182711*X+.282691»X»*2-.013672»X**3)
50 "STOO 3.UaHRS+XMHRS3*DHR*U.+PCT3+TMSU*WPI)/SMATXC2, 13/3650.
OMATX(l»N3aClDIG
OMATXC2,N33C2DIG
OHATXC3,NJaVDIG
OHATXC4,N)aCH4 ,
OHATXC5,NJ3C02
E1»9.589*VDIG/DMA7XC16,N)
TDIG3l.8*DHATX(2,N3+32.
CAP»SHATXC2,IS1)*8.33*1.E6»CTDIG-60.)
XX»ALOG(CAP/1000./24.3
E2a.75*24.*.7457»EXPC2.00069-1.02649*XX+.127492*XX#»2)
E3*,000293«CAP*CF/r75
E4aVDIG/DMATXC16,N)»62500.*DHATXC3,N)#,000293*CF/.75
EER3E1+E2+E3+E4
EEP»CH4*600,».000293*DMATX(4,N)
RETURN
END
SUBROUTINE VACF
VACUUM FILTRATION
INTEGER dSl,OS2
COMMON SMATX(20,45},DMATX(20,50),OMATX(20,50),IPC50),
, INP,IO,IS1,IS2,OS1,OS2,N,IAERF,CCOSTC5),COSTO(5),
. ACOST(5),DHR,PCT,WPI,CCI,RI,AF,RATIQ,CKHH,
. CF,EER,EEP,ALAND
FECL33DMATXC5,N)
CAO«DMATXC6,N)
CFECLsDHATXC7,N)
CCAO>DMATXC8,N)
DPOIiVaDHATX(9,N)
CPOLVaDMATXtlO.N)
SAVE1=SMATXC7,IS1)
SAVE23SHATX£10,IS1)
SMATX(7,ISl)sSMATXC7,I51)+CFECL3+CAO+DPOLY)*SMATXaO,ISl)/2000.
5HATX(10, ISl )sSMATX( 10, ISlJ-f-(FECL3*CAO+DPOLI3»SMATXC 10,1313/2000.
SMATXC10,OS233DMATXC3,N3
Wps88./CsMATXC10,1313/10000.3**.123
SHATXC10,0513s(100.-HP3*10000.
SMATX(2,OS133(SMATX(2,IS13*SMATX(10,IS133/(SMATXtlO,OS13-
. SMATXC10,OS233
SMATXC2,OS23sSMATXC2,ISl3-SMATXC2,OS13
TEMP23SMATXC10,OS13/SMATX(10,IS13
TEMP33SHATXC10,0323/SMATX(10,1513
DO 10 1=3,9
SMATXCIrOS13sTEMP2»5MATXCI,IS13
10 SMATX(I,OS23sTEMP3*SMATX(I,IS13
DO 20 1311,19
SMATX(I,OS13sSMATX(I,IS13
5MATXCI,OS2)3SMATX£I,IS13
SMATX(20,OS133SMATX(20,I513»TEMP2
SMATXC20,OS2)3SMATXC20,IS13*TEMP3
5F«SMATXt10,1513/10000.
SCalOO.-WP
FVF*DMATX(lrN3/11.99/£l./SF-l./SC)
AVF«SMATXC10,IS13»SMATXC2,IS13*58.31/FVF/DMATXC2,N3*DMATX(16,N3
IVACF31.
P5DD»SMATX(10,IS1)*SMATX(2,IS13»8.33
130
-------
XsALOGCAVF)
CCOSTCUsEXP £3,288028+, 194537*X+.038313*X*»2)»1000,
X3ALOG(PSDD»365./2000»)
IFUVACF) 40,30,40
30 aHRSaEXPC6.0694t9-,,009894»X+,042699»X»*2i
GO TO 50
40 OHRSsEXPc3.714368>.850848*X-.074615*X**2+.005085»X»»3)
50 XMHRSaEXP(4.306UO-,09369S*X*.047738»X**2)
SUPPaEXP(-3,U35l54-,718466*X5*iOOO.
CHEMaPSDD*36S,/2000.»CFECL3»CFECLtCAO»CCAO+DPaLY»CPOLY)
COSTO < 1 ) •» C (OHRS+XMHRS ) *DHR* ( 1 . -t-PCT ) +SUPP»WPI+CHEM ) /
. SHATXC2,1}/3650.
OMATXC1,N)3WP
OMATXC2,H)»AVF
OHATX(3,N)*PSOO
X»ALOG(AVF/DMATX(16,N)}
EER3EXPC3.21323*.378196»X+.036877»X**2)
EER»EER-(.PSOD*(FECL3*,44>CAO*.36+DPaLX*.14)/2000.
XSALOGCSMATX (2, isi)*sMATxtto,isn*8. 33/2000.)
,0057516«X»*3)
SMftTXC7,ISl>aSAVEl
SMATXC10,IS1)=SAVE2
RETURN
END
SUBROUTINE THICK
GRAVITY THICKENING
INTEGER 051,032
DIMENSION SMATC20)
COMMON SMATXC20,45),DMATXC20,50)rOMATXC20,50),IPC50),-
. IMP,IO,IS1,132,051,052,N,IAERF,CCOST(53»COSTO(5),
. ACOST(5),DHR,PCT,WPI,CCI,RI,AF,RATIO,CKKH,
. CF,EER,EEP,ALAND
DO 2 1*1,20
2 SMATCI)=0.
IFCIS2) 7,7,4
4 DO 6 1=1,20
6 SMAT(I)3SMATX(I,IS2)
7 SMATXCIO,OSI)3DMATX(2,N3
SMATXC2,OSl)sDMATX(l,N)*(5MATXC2,ISl)#SMATXC10,ISl)+SMATC2)»
. SMAT(10))/SMATXtlO,OSl)
TEMP>DMATX(4,N)XDMATX(3,N)*1000000e/8.33
IFCIS2) 9,8,9
8 WRTaO,
GO TO 10
9 WRT3(SMATXUO,ISt)-TEMP)/(TEMP-5MAT£10))
SMATXC2,IS2)a«RT»SMATXC2,IS13
SMAT(2)3SMATX(2,IS2)
10 SMATXC2,OS2)=SHATX(2,ISn+SMATC2)-SMATXC2,OSn
TEMP»SMATX(2,IS1)»SMATX(10,IS1)+SMATC2)*SMATC10)
SMATXC10,OS2>3CTEMP-SMATX(2,OSl}»SMATXC10,OSl))/SMATXC2,OS2)
TEMPaTEMp/(SMATXC2,ISl)*SMATC2))
TEMPlaSMATX(10,051)/TEMP
TEMP2aSMATXC10,QS2)/TEMP
DO 15 I»3,9
TEMP33tSMATXt2,ISl)*SMATXtI,ISl)+SMATC2)»SMAT(I))/
. (SMATX(2»IS1)+SMAT(2))
SMATXCI,OSn=TEMPl»TEMP3
15 SMATX(I,OS2)aTEHP2»TEMP3
TEMP3»(SMATXC2,I31)»SMATX£20,IS13-t-SMATt2)*SMAT(20)3/
2 {SMATX(2,IS1)+SMAT(2))
SMATXC20,OS1)3TEMP1»TEMP3
SMA'TXC20,OS2>aTEMP2*TEMP3
131
-------
DO 20 I'll, 19
SMATXCI,OSl)3CSMATXCI,IS13»SMATXC2,ISl3.t.SMATCI3*SMAT(233/
. C5MATX(2»IS13+SMATC233
20 SMATXCI,aS233SMATX(I,OS13
ATHl»*DHR»(l.+PCT)+TMSU*WPI)/SMATX(2,l)X3650.
OHATXC2fN)aHRT
X3AIiOG(ATHM/DMATX(16,N))
EER*EXPC 5. 50543-1. 597 89*X-t-, 2061 2 1*X**2-. 0056 17»X»*3)
RETURN
END
SUBROUTINE EIiUT
ELUTRIATION
INTEGER OS1,OS2
COMMON SMATXC20,45),DHATXt20,50),OMATX(20,50),IP(503,
INP,IO,IS1,IS2,OS1,OS2,N,IAERF,CCOSTC5),COSTQ(5J,
ACOSTCS),DHP,PCT,WPI,CCI,RI,AF,RATIO,CKWH,
CF,HER,EEP,ALAND
SMATX(10,OSl)aDHATXC2,N)
SAVEaSHATXC2,152)
SMATX(2,IS2)3DMATX(3,N)»SMATX(2»IS1)
AE1»(SMATX(2,IS1)+SMATXC2,IS2J3»1000000./DMATX(4,N3
AE2»SMATXC2,IS1)*SMATX(10,IS1)*8.33/DMATX(5,NJ
AE2*AE2-t-(SMATX(2,IS23*SMATX£10,IS2)»8.33/DMATX(:5,N3)
IF(AE1-AE23 20,20,10
AEaAEl»DMATX(16,N3
GO TO 30
20 AEsAE2»DMATXC16,N)
30 SMATX(2,OSt)sDMATXtl»N)»SHATXC2,ISl)»5«ATXC10,IS13/SHATXC10,OS13
SMATXC2,OS23»SMATXt2,IS13+SMATX(2,lS23-SMATX(2,OS13
TEMP«SMATXC2,IS13«SMATXC10,IS13+SMATX(2,IS23*SMATXC10,IS23
SMATXC10,OS23a
TEHPaTEHP/{SMATX(2,IS13+SHATXC2,IS233
TEMPlaSMATXt10,0313/TEMP
TEMP2aSMATXClO,OS2)/TEMP
DO 40 133,9
TEMP3»{5MATXC2,IS13*SMATX(I,IS13+SMATX<2,IS23*SHATX£I,IS2)3/
. CSMATXC2.IS13+SMATXC2,IS233
SHATXCI,OS1)3TEHP1»TEMP3
40 SMATXCI,QS23=TEMP2»TEMP3
TEMP33CSMATXC2,IS13*SMATXC20,IS13+SMATX£2,IS23»SMATXC20,IS233/
2 (SMATXC2,IS13+SMATX(2,IS233
aHATXC20,OS13aTEMPl»TEMP3
SHATXC20,OS2)aTEMP2*TEMP3
132
-------
DO 50 1*11,19
SMATX(I.OSI)»(SMATX(I,1S1)»SMATX(2,1S1,)+SMATX(I,IS2)»SMATX(2, IS2))
» /(SMATX(2,IS1)+SMATX(2,IS2))
50 SMATX(I,OS2)*SMATX(I,OS1)
XaAIiOGCAE/1000.)
CCQST(1)*EXP(3.725902+.397690*X+.075742»X*»2-.001977»X**3-
. ,000296*X»*4)*1000.
X*ALOG(AE/1000./DMATX(16,N))
IF(EXP(X}-1.) 60,70,70
60 DHRSS350..
XMHRS»190.
TMSU»250,
CO TO SO
70 OHRSaEXP(5,84656S*.2S4813»X+.113703»X»«2-,010942»X»»3)
XMHRSaEXPtS. 273419*. 228 329»X*,122646»X»*2-. 01 1672*X*»3)
TMSUsEXP(S,66988H>.750799*X)
80 CQSTOtl}»e(QHRS+XMHRS)»DHP>*(l.'t-PCT)-('TMSU«WPI)/SMATX(2,l)/3650.
OHATX(l»N)aAE
EEP»EXP( 5. 50543-1, 597 89*x+. 206 12 1#X*#2-.OOS617»X**3)
SMATX(2»IS23*SAVE
RETURN
END
30
40
SUBROUTINE SBEDS
SAND DRYING BEDS
INTEGER OS1.QS2
COMMON SHATX(20.45),,DMATX(20»50},OMATX(20(,50),IPt50),
, INP,IO»IS1,IS2,OS1,OS2,N,IAERF,CCOST(5)»COSTOC5J,
, ACOST(5),DHR,PCT,WPI.CCI,RI,AF,RATIO,CKWH,
,. CF,EER,EEP,AI,AND
SMATXC2,OS2)*SMATXC2,IS1j
SMATXC10,OS2)=DMATXC2,N)
TEMP»SMATXC10,OS2)/SMATX(tO,Isn
00 10 1*3,9
10 SMATX(I,OS2)aTEMP»SMATX(I,IS13
SMATX(20,OS2)sTEMP*SMATX(20,151)
DO 20 1311,19
20 SMATX(IrOS2)sSHATX(I,ISl)
SF»SMATX(10,131)/l0000.
SC*OMATXtl,NJ#100.
FSB»(29.84»SF-33.3J/SC
TEMP«SMATX(2,IS1)*SMATX{10,IS1)*249.9
ASB*TEMPyFSB*DMATXU6,N3
PSDD«SHATX(10,IS1)*SMATXC2,IS1)»8.33
SMATX(10,OS1)»DMATX(1,N)*1.E&
TEMP«SHATXC10,051)/SMATX(10,IS1)
SMftTXt2,OSl)»SMATX(2,ISl)/T^MP
DO 30 133,9
SMATX CUOSl)aTEMP*CSMATX(1,151)-SMATXU,OS2J)
SMATX C20,OSl)aTEMP» (SMATX ( 20, ISD-SMATXC20, 052))
DO 40 1311,19
SMATX(I,OS1)>SMATX(I,IS1)
XaAliOdCASB/1000.)
CCOST(1J»EXP(1.971125*.083841*X+.1467S1*X»»2-.007718*X»*3}*
. 1000.
X»AI,OG(PSDD»365./2000.)
OHRS»EXP(6.345052-.476780*X*»101319»X»»2)
XMHRS3EXP(4,290089-,098293*X*.075453»X**2)
TMSU«EXP(.693148+1.000000*X)
COSTOU)»((OHRS+XMHRS)*DHR»U.+PCT)VrMSU»WPI)/SMATX(2.1)/3650.
OMATX(l«N)aASB
GPMaSMATX(2,ISl)*l.E6/1440.
EER»(,4355«GPM*SF»*1.116r*293,»CF/365.
ALAND3ASB»2.2E-5
RETURN
END
133
-------
c
c
20
C
C
10
C
C
C
C
SUBROUTINE TPFS
TRICKLING FILTEP - FINAL SETTLfcF
INTEGER 061,032
COMMON SMAIX(20»45) , DMA IX ( 20. bO ) » OMATX C20« 50 ) t IP (50 ) »
2 INP. 10, IS t, 152,03 1,032 , N , I AERF ,CCOST (5 ),COSTO(5).
3 ACOST(b),DHR,PCT,WPI,CCI.PI,AF,P.ATIO,CKWH,
4 CF/EER.EEP, ALAND
lAERFsO
BUD5SDMATXC1.N)
DEGCsDMATXC2»N)
HUsDMATXC3,N)
SAREAsDMATXC4.Nj
TS57=DMATXCb,N)
RR=DMATX(7,N)
GS53DMATX(8,N)
VIELDsDHATX(9,N)
SRATIO=DMAXXC10,N3
TSS5=4.5+,51«BOU5
IF CTSS5 ,GT. OMAIXC5,N)) GO TO 200
COMPUTE REMOVAL EFFICIENCY UF ULIER TO MEET EOU LIMIT
BOD2sSMATX(8,ISl)*SMATX(17,ISn
BOD4=BODS/SPATIU
F=BOD4/BQD2
IF (F ,GT, 1. .OR, F .LI. 0.) GO TO 200
COMPUTE FILTER AREA AND DEPTH
BETA=.0245M.U33»*CDEGC-20,)
XNs,91-6,45/SAHfA
A=ALOG((l.*F»HRJ/(F+F»PR)5/BETA/SARtA
RHQ=((RR+1.)«HQ)«»XN
DEPTH=PHQ*A
IF (DEPTH ,I/E. 30.) GO TO 10
DEPTH=30.
BHQS30.XA
HG=(l./(RR+l.))»RHQ**tl./XN)
FAPEAsSMATXC2,ISl)/HQ»43560.
COMPUTE SLUDGE PRODUCTION
PDSDsDHATX(9,N)*CBOD2-BQD5)*SMATXt2«Isn*8.33
COMPUTE FLOW PATES IN EFFLUENT AND SLUDGE STREAMS
SMATX(2»OS2)=PDHD/TS57/8,33
5MATX(3»OSl)=SMATXC2.ISl)-SMATX(2.aS2J
c
c
SMATXC10,OS2)=TSS7
COMPUTh CONC. OF SOLID SPECiES IN EFFLUENT
R=TSSS»SMATXC2,1S1)/(TSS5*SMATXC2,OSI)+TSS7*SMATX(2,OS2))
SMATXC4,051)=R»SMATX(4,IS1)
SHATX(8.0Sl)=(SMATXf 10«OS1)-4.S)«.8«7
SHATXC3,Osn=SMATX(8,OSn»l,6/2.7+SMATX(4,asn
SMATX(S,OSl)=.l»SMATX(3,OSn
5MATX(6i051)=.01*SMATXC3,OSl)
SHATX(7,OS1)3R»SMATXC7.IS1)
SMATX(9.0S1)=SMATX(10,OS1 )-SMATX 1 7 ,OS 1 )
COMPUTE. CONC, OF DISSOLVED SPECIES IN EFLLUENT
SMATXC17.0S1 )=8QD5-SMATXC8,Osn
SMATX(ll,OSl}sSXATX(12fISn+5MATXClV,aSl)*1.6/2,7
134
-------
50
70
C
C
95
C
C
C
C
110
SMATXC12,OS1)=SMATX(12,IS1)
TEMP3SMATX(2,OS1)XSMATX(2,IS1)
TEMpsTEMP+5MATX(2,OS2)XSMATX(2,ISl)*SMATX(10,OS2)XSMATX(10»OSl)
SMATXC13,OS1)BSMATX(5,IS1)+SMATX(13,IS1)«TEMP»5MATX(5,OS1)
SMATXU4,OS1)=SMATX(6,ISU+SMATXU4,JS1)-TEMP*SMATX(6,051)
SM ATX (15, OS1)=SM ATX (15,151)
BODLD=BOD2«5HATXC2,I51)«8,33/FAREA/UfcPTH/1000,
RNst,-EXP(-.05»BODLD)
SMATX(18,OS1)=RN«SMATX(18,IS1)
SMATX(19,OS1)=(1.-RNJ»SMATX(18,IS1)+SMATXU9,IS1)
SMATX(16,OSl)=5MATXU6,ISl)-10,»(SMATXtt8,ISl)-SMATX(18,QSl))
SMATX(20,OS1)=0,
120
130
140
COMPUTE CONC, OF SPECIES
RlaSMATX(10,OS2)XSM ATX C10,OS1)
DO 50 1=3,9
SMATX(I,OS2)=Rl»SMATX(I,OSn
DO 70 1=11,19
SMATX(I,OS2)=5MATX(I,OSl)
SMATX(20,OS2)=0.
COMPUTE FILTER COSTS
VOL=FAflEA«DEPTH*OMATX(16,N)
X=AtOG(VOL/lQOO.)
IN SLUDGE STREAM
,004587*X«»3)*1000.
X=ALOG(5MATX(2,IS1)/HQ»43560./1000.)
OHPS»EXP( 4. 536510-. 0957 31»X+. 1737 18#X#e2-. 0101 14«X«*3)
XMHPSsEXP(4.312739..052122*X+.157473*X««2-.010245*X«»3)
TMSU=EXP(5,105946+,465100»XJ
CQSTO(l)a((OHBStXMHR5)»OHR«(l,+PCT)+TMSU«WPI)/SHATX(2,l)/3650.
COMPUTE SETTLER COSTS
AFSsSMATX(2,ISl)«t.Eb/GSS»DMATX(15,.N)
X=ALOG(AFS/1000.)
CCOST(2)aEXP(J.716354+t3898bl»X+,08456*X**2-.004718*X«*3.)
•1000.
X=ALOG(AFS/1000./DMATX(15,N))
OHRSsEXP(b.84b565+.254913»X+.U3703»X»«2-.010942*X«*3)
XMHRSsEXP (5,273419*. 228329«X*,12264b*X«*2-.011672»X»*3)
TMSU=EXP(5.S6S831+,750799«X)
COSTO(2)«
-------
ISO
IbO
C
C
C
C
170
200
GO TO 160
PEFF=.83
YRKW3SMATXC2,IS13#(l.+RR)«l.Eb*(DEPTH+b,3
XRKW3XRKW/144U,/3960./PEFF/.9*.7457*i4,»3b5.
ECOSTsXRKW*CKWH
SCOST=EXPC5.851743+,30161«X+,197183»X*«2-,017962*X*»33
TMSUsECOST+SCOST»wPI
COSTOC333(CaHKS+XMHRS3»OHR*Cl.+PCT3+TMSU3/SMATX(2. 13/36bO.
fill IN OUTPUT MATRIX VALUES
OHATXC1»N3SAFS/1000,
OMATX(2,N3=VOL/1000,
OMATXC 3, N 3 =F AREA/43560.
OHATX(4,N)=DEPTn
COMPUTE ENERGY AND LAND CONSUMPTION
XsALOG(AFS/1000./DMATX(15,N))
EERsEER+EXP(2.8248+,30093»X+.022308«X»«2+.0035144#X»*3)
ALAND=FAREA/4J5oO.
RETURN
IAERF=1
RETURN
END
SUBROUTINE CHLOR
CHLORINATION - DECHLORINATION
INTEGER 031,032
COMMON SMATX(20,45),DHATXC20,50),OMATX(20,50),IPC50),
. INP,IO,IS1,IS2,OS1,OS2,N,IAERF»CCOSTC53,COSTO(53,
. ACOST<5),DHR,PCT,WPI,CCI,RI,AF, RATIO, CKWH,
. CF,EER,EEP, ALAND
DCL2«DHATXC1,N)
CCIi2»DHATX£3.N)
DS02»DHATX(4.N)
CS02»DMATXt5,N3
BVOLaSMATXt 2, IS 1)*TCL2/ 1.44/7. 48*1000. *DM ATX Cl 6, N)
XaALOGCBVOL/1000.)
CCOST(1)3EXP £2. 048061*. 52 1909#X-. 00267 4*X*»2+. 004 159#X»*3)»
. 1000.
COSTOCDaO.
CU5EaSHATXC2.ISl)*DCL2*8. 33*365. X2000.
SUSE»SHATXC2(.ISn*DS02»8.33*365./2000,
FACTR*CUSE/CCUSE+SUSE)
X*AlJOG(CUSE*2000./365.*DHATX(15.N3-fSUSE»2000./365.*DMATX(14,N))
XCOSTaEXP (2. 264294-, 04427 1*X+.065029*X**2-.002536*X**3}#1 000.
CCOSTC2)»FACTP*XC05T
OHRS»EXP(4,538517+.543669*x)
XHHRS3EXP(3.7S2071-.224812*X+,158849*X»»2-.0060b4*X»*3)
TMSU3EXPC6.126105+.287016*x)
OC*FACTR*OHRS
XC«FACTP»XHHRS
THSUCaCUsE»CCL2+FACTR»THSU
COSTOC2)sC(OC+XC)*DHR»(l.-l-PCT)+THSUC)/SMATX (2, 13/3650.
IFCDS023 10,10,20
10 CCOSTC33»0.
COSTOC3)«0.
GO TO 30
20 CCOSTC3)aXCOST-CCOST(2)
OS*OHRS-OC '
XS»XHHRS-XC.
TMSUS»SUSE«CS02-K 1 ,-FACTP3*TMSU
CDSTO(3)*(COS-».XS3*OHR*C1.+PCT3+THSUS3/SMATXC2, 13/3650.
136
-------
50
60
30 DO 40 I*2>20
40 SMATX(I,OS1)3SMATXCI.IS1)
OMATX(1,N)3BVOL
OMATXC2»N)*CUSE
OMATX(3,N)3SUSE
PCL2DSOMATXC 1,N ) »SMATX C2,IS 1)*8 ,33
X3ALOG{PCL2D)
EER»1.5*CUSE*2000./365,
IFCDMATX(4,N),GT,0.)GO TO 50
EER»EER+EXP(-.07l827+.44044»X+.076407*X»*2-.0030Q3*X»*3)
GO TO 60
EER»EEH+EXP(-.259363*.69229»X+.025652»X*»2)
EEPsO.
RETURN
END
SUBROUTINE TFLOT
FLOTATION THICKENING
INTEGER 031,052
DIMENSION YC12),SMATC20)
COMMON SMATX(20,45J,DMATXC20,50),OHATXC20,50),IP(50),
. INP,10,131,IS2,QS1,OS2,N,IAERF,CCQSTO)>COSTOC5),
. ACOST(5),DHR,PCT,WPI,CCI,RI,AF,PATIO,CK»«H,
. CF,EER,EEP,ALAND
DATA Y/2S.,50.,100.,ISO,,200,,250,,300,,400.,500.,600.,800,,1000./
DO 5 1=1,20
5 SMAT(I)=0.
IFCIS2) 20,20,10
10 DO 15 131,20
15 SMAT(I)3SMATXCI,IS2J
20 SMATXC10,OS13=DMATXC2,N)
SMATXC2,aSl)sDMATXU»N)*(SMATX(2,ISl)*SMATXUO,ISl} +
. SMAT(2)»SMAT(10))^SMATXC10,OS1)
ATHl*(SMATXC2,ISn*SMATX(10,ISl)+SHATC2)-»SMATC10))»
. 8.33/DMATX(4,N)*168./DMATX(S,N)
IFCIS2) 30,25,30
25 ARCYaO.
GO TO 35
30 ARCYa.00288*ATHl
SMATX(2,IS2)sARCY
35 SMAT(2)»ARCY
SMATXt2,OS2)=SMATX(2,ISl)+SMATC2D-SMATX(2,051)
ATH2*SMATX(2,OS2)*1000000./DMATX(3,N)»16<(./DMATXC5,N]
IFCATH1-ATH2) 40,50,50
40 ATHM»ATH2»DMATX(16,N5
GO TO 60
50 ATHM«ATH1*DMATXU6,N)
60 TEMP*SMATX(2,IS1)*SMATXUO,IS1)+SMAT<2)*SMATUO)
SMATXC10,OS2)3(TEHP-SMATXC2,031)»SMATX(10,OSl)3/SMATXt2,OS2)
TEMPsTEMPX CSMATX{2,151)tSMAT C 2 J)
TEMPIsSMATX(10,051)/TEMP
TEMP2aSMATX(10,OS2)/TE»P
DO 70 I»3,9
TEMP33(SMATX(2,ISl)»SMATX(I,Isn-»-SMATC2)*SMAT(I))/
. (SMATX(2,I51)+SMAT(2))
SMATXCIrOSl)*TEMPl*TEMP3
70 SMATX(I,OS2)aTEMP2»TEMP3
TEMP3a(SMATX(2,ISl)»5MATX(20,ISl)+SMATC2)»5MAT(20))/
2 (SMATX(2,IS1)>5MAT(2))
SMATX(20,OS1)=TEMP1*TEMP3
SMATX(20,OS2)tiTEMP2»TEMP3
DO 80 1311,19
SMATX(I,OSl)s(SMATXtI,ISl)*SMATX(2,ISl)-t-SMAT(I)*SMAT(2))
. /(SMATX(2,IS1)+SMAT(2))
80 SMATX(I,OS2)sSHATX(I,OSt)
137
-------
ATHMlsATHM
XNaO,
XX»0.
DO 100 1=1,12
IF(ATHM-Yd)) 90,90,95
90 ATHMzYtl)
GO TO 110
95 IFCI-12) 100,96,100
96 ATHHs*C12)
100 CONTINUE
110 IFCATHM-25.) 120,120,130
120 ATHH*25%
XNsl.
GO TO 180
130 IF(ATHH1-1000.J 170,170,140
140 XNaATHMl/1000.
KSXN
XXaK
1F(CXN-XX)*1000.-500.) 150,150,160
150 XNsXX+,5
GO TO 180
160 XNaXX+1.
GO TO 180
170 ATHHmATHM/2.
XN*2.
180 XsAIiOGCATHM)
CCOSTCDsEXPC 1.7 17538 +.453735*XJ»1 000. *XN
X3AIiOGCATHM/DMATXC16,N)»XN}
OHRS3EXP(4.992517-.325053*X+.084026*X»*2)
XHHRS=EXPC4.832373-.336S04»X+.083020»X»*2J
HPDaEXP(-1.254959-t..852347»X)
EIiEC=HpD*.746»365.»CKWH*DHATX(5,N)/7.
PWASs(SHATXC2,lSn*SMATXC10,ISl)+SMAT(2)*SMAT(10})*8.33
POIjCsPWAS*365,/2000.*DMATX(6,N3«DMATXC7,N}
COSTOCl)aCCOHRS+XHHRS)*DHP»(l.+PCT)+ELEC+POLC)/
. SHATX(2,l)/3650.
OMATXCl,NJsATHM
OHATXC2,N3=XN
OHATXC3,N)3ATHH1
EER=HPD».746»OMATXC5,N)/7.
RETURN
END
SUBROUTINE HHINC
HUtTIPLE HEARTH INCINERATION
INTEGER 051,032
DIMENSION SFHAC59)
COMMON SMATXC20,45),DMATXC20,50),aMATXC20,50),IP(50),
INP,IO,IS1,IS2,OS1,OS2,N,1AERF,CCQSTC5),COSTO(5),
ACOSTC5),DHR,PCT,WPI,CCI,RI,AF,RATIO,CKWH,
CF,EER,EEP,ALAND
DATA SFHA/85.,98.,112.,125.,126.,140.,145.,166.,187.,193.,208.,
225.,25"6.,276.,288.,319.,323.,35l.,364.,383.,411.,452.,510,,560.,
575.,672.,760^,845.,857,,944.,988.p1041.,1068.,1117.,1128.,1249.,
1260.,1268.,1400.,1410.,1483.,1540„,1580.,1591.,1660,,1675,,
1752.,1849,,1875,i1933,,2060,,2084,,2090.,2275.,2350.,2464.,
2600.,2860.,3120,/
PSDOsSMATXC10,ISl)»SMATXC2,ISl)*8.33
FHATa58.3l»SMATXC2,ISl)»SMATXC10,ISn/DMATX(3,N3/DMATXU»N)#
, DMATXU6,m
XXsFHAT/DMATX(2,N)
DO 20 1=1,59
IFCXX-SFHACin 10,10,20
10 FHAsSFHA(I)
GO TO 30
20 CONTINUE
138
-------
FHA*3120.
30 IFCFHA-200.) 40,40,50
40 CYT318.
GO TO 100
50 IFCFHA-1700.) 60,60,70
60 CYTsl3.+.024»FHA
GO TO 100
70 IFCFHA-2300.) 80,80,90
80 CYT=,09*(FHA-1100,)
GO TO 100
QO f*VT5Sl08
100 PASH3(SMATXC10,IS1)-SM*TXC9,,IS1))/SMATXC9,IS1)
HASH=68.*PASH
PWAT3( 1000000, -SMATXUO,IS1))/SMATX (9, IS1)
HWSL=1404.3*PWAT
SAERA=64.03»FHA**.51
VSPH»SMATXC9,lSl)*SMATXC2,rSl)*58.31/DMATXC3,N)
=. .,
OTRAN3ao279H>HC)»100r»SAEp.A*DMATXC2.N)/VSPH
QCOOL=267,*FHA*OMATX(2,N)/VSPH
QNETs2725,-i-HASH+HWSL+QC001J*QTRAN-DMATXC6,N)-«-246»
IF(QNET) 105,105^106
105 QNETsO.
106 TEMP3SMATXC9,Isn»SMATX(2,ISl)*8. 33*365.
QNET3<3NET*TEMP
YS8H38.»C*T/9,+8736.-52.»OMATX(3,N)»7./9e
,
QHUP*YHUH*1913.»FHA»DMATX(2,»)
QSB=YSBH»315,«FHA*DMATXt2,N)
QTOT3QHOP+QS8>QNET
IFCDMATX(7,N)-1.) 130,130r140
130 WFYR=QTQT/15019.
FCOST=WFYR/7.481»OMATXC8,N5
GO TO 130
140 IF(DMATX(7,N)-2.) 150,150,160
150 WFYR=QTOT/15581,
FCOST3WFYR/45.8»DMATXC9rN)
GO TO 180
160 IF(DMATX(7,N)-3,) 170,170,180
1-70-
180 Typ=SMATX(10,ISl)*SMATXC2,ISl)*1.52
WTON3554.24/FHA»*.3572
190
DPTONsFCOST/ CPSDO*365 ,/2000 . )
XaALOGCPSDD/24.«DHATX(16,N))
CCOSTtl)sEXPC2.377364-t..598986*X)*1000.
X= ALOG(PSDD*365./2000.*CSM ATX (9, ISl ) /S«AIXt 10,
QHRSaEXPf 3. 402537 + 1. 215130»X-,157203»X**2-(.. 00977 1*X**3)
XHHRSsEXPC 3. 90655 3+. 70247 1»X-.088337*X*»2+.006827»X»»3)
TMS03EXP(7.864729-.338816«X+,054026»X»»2)
COSTO(l>3CCOHRS+XMHRS)»DHR»(l.+PCT)-l-TMSU»WPH-ECOST+FCOST)/
, SMATX(2,1)/3650.
OMATX(1,.M)3FHA
OMATX(2,N)=WFYR
OMATXC3pN)3PSDD
OMATX(4,N)sECOST
OHATXt5fN)=FCOST
OHATX(6,N)=CFDG
EERsECOST/CKWH/3*S.+QTOT/365.*.000293»CF
FSsSMATXC10,ISl)/l.E6
FVsSMATX(9,ISl)/SMATX(10,ISl)
WSaCl.-FS)/CFS*FV)
EX3-((QNET-2725,)-2113.)yi223.
IF(EX.LT.,5)EX3.5
IFCEX.GT.1.5)EX=1.5
OPDVSst800.-60.)»C.505»WS+2.55+2.09»EX)
IF30MATX(7,N)
GO TO (190, 200, 210), IF
Fl=4.13a
F23WFYR
139
-------
GO TO 220
200 FlsS.63
F23WFYR
GO TO 220
210 F133.151
F2=CFDG».069S
220 QPD3QPDVS*SMATX(9,I51)»SMATXC2,IS1)*8.33
OPD=OPD-t-C800.-60.)/365,»Fl»P2
EEP3QPD*.000293*DMATXC10,NJ
RETURN
END
SUBROUTINE RWP
RAH WASTEWATER PUMPING
INTEGER OS1.0S2
COMMON SHATXC20,453,DMATX(20,50),DMATX(20,50),IPC50J,
. INP,IO,IS1,IS2,OS1,OS2,N,IAERF,CCOSTC5),COSTOC5),
. ACOSTC5).DHR,PCT,WPI,CCI.Rl,AF,RATIO,CKViiH,
. CF,EER,EEP,ALAND
DO 10 1^2,20
10 5MATXCI,OS1)3SMATX(I,I51)
HEADsOMATXCl.N)
QPsl.78-*SMATXC2,ISl)*».92
XsALOG(QP»DMATXC16,N))
CCOSTCn3EXPC4.004828+.519499»X+.082262»X*»2-.006492»X»»3)«
. 1000.
XaALOGCSMATXC2fISD)
OHRS3EXP(6.097269+.253066*X-,193659*X»»2+.078201*X«»3-
. .006680*X»*4)
XMHRSsEXPC5.911541-.013158»X+.076643»X*»2)
IFCSMATX(2,IS1)-1,44) 20,30,30
20 PEFF=.70
GO TO 60
30 IFCSMATX(2,ISn-10.08) 40,50,50
40 PEFFs.74
GO TO 60
50 PEFFs.8-3
60 YRKWsSMATX(2,IS1)*1000000.«HEAD/1440./3960./PEFFX.9*.7457*24.»365,
ECOSTsYPKrt«CKWH
SCOSTsEXP{5.851743+,301610»X+.197183»X»»2-,017962»X#»3)
TMSUaECOST+SCOST-»WPI
COSTOC1) = CCOHRS-|-XHHRS)*OHR*(1.+PCT}+.T«SU)/SMATXC2,1J/3&50.
OMATXC1,N)=QP
EER3YRKW/365,
RETURN
END
SUBROUTINE SHT
SLUDGE HOLDING TANKS
INTEGER QS1,OS2
COMMON SMATX(20,45),DMATXC20,50),OMATXC20,50),IP(50),
. INP,IO,IS1,IS2,QSI,OS2,N,IAERF,CCOSTCS3,COSTOC5),
. ACOST(5),DHR,PCT,WPI,CCI,RI,AF,RATIO,CKWH,
, CF,EER,EEP,ALAND
DO 10 132,20
10 SMATXCI,OS1)3SMATXCI,IS1)
VSHTaSMATX(2,ISl)*OMATX(l,N)*1000./7.48»OMATXC16,N)
XaALOGCVgHT)
CCaSTtl)sEXP(2,625751 +.484180*X-I'.000613*X**2-t-.002252*X#*33»
. 1000.
VlsSMATX(2,ISl J-»DMATX(l,N)»1000,/7.48
XsAIiOGCVl)
OHRS3EXP(5."727345*.000762*X'I-»098701*X»»2".006786*X*»3}
XMHRS3EXPC4.506628-t..214662*X+.071402*X»»2-.004681»X»*3)
THSUaEXPCS.479939+.299282*X+,106008*X»*2-.008658»X**3)
COSTO(l}3((OHRS-t-XMHRSJ»DHR*(l,+PCT)-l-TMSU*WPI)/SMATX( 2, 13/3650.
OMATXC1,N)=VSHT
EERsO.
RETURN
END
140
-------
SUBROUTINE CENT
CENTP.IFUGATION
INTEGER 031,052
COMMON SMATXC20,45),DMATX(20,50),OMATXC20,50),IPC50),
. INP, 10, 131, 132,031, 052, N,IAERF.CCOSTC5),COSTOC5),
, ACOST<5).DHP,PCT,WPI,CCI,P.I»AF, RATIO, CK"H,
. CF,EER,EEP»ALAND
DIMENSION CPOATA(4,2)
DATA CPDATA/.73..8,,43,.41,1.6,1.48,.81,,74/
HPWK=DMATX(3,N)
XCENsl.
POI,¥=DMATX(S,N)
CPOLY=DMATX(6,N)
GPMNaDMATX(7,N)
CNMIN=DMATX(8,N)
SAVEl3gMATX(7,ISl)
SAVE2=SMATXC10,IS1)
SMATXC7,ISl)=SMATX(7,ISl)+POL1f»SMATXUO,lSl)/2000.
SHATX(10.IS15=SMATXC10,ISn*POLX*5MATX(10,ISl)/2000.
DSOLaSMATXC 10, IS1)*SMATXC2,IS1)*8, 33*365. X2000.
SMATX(10,OS2)a((l.-DMATXCl,Nn/(l.-DMATX(l,N)*SHATX(10,Isn/
. DMATXC2,N)))*SMATXUO,IS1)
TEMPlaDMATXt 2, Ni/SMATXC 10,131)
TEHP2aSMATXC10,OS2JXSMATX(lO,ISl)
5MATXC10,OS1>=DMATXC2,N)
SMATXC2,OSl)aCSMATX(lO,ISn-SMATX(10,OS2n*SMATX(2,ISl)/
. CSMATXC10.0Sn-SMATX(10,OS2)}
SMATX(2,OS2)3SMATXt2»ISl)-SMATXC2,OSl)
DO 11 1=3,9
SMATXCI,OSl)aTEMPl»SMATX(I,ISl)
11 SHATXtI,OS2)=TEMP2»SMATXCI,ISl>
SMATXC20,OS1)=TEMP1»SMATX(20,IS1)
SM ATX C 20, 032 )=TEMP2*SMATXC 20,131)
DO 21 Iatlrl9
SMATXtI,OSl)sSHATX(I,ISl)
21 SMATXU,OS2)*5MATXCI,IS1)
CN=CNMIN
CGPM»SMATX(2,IS1)»U6666.7/HPWK*DMATXC16,N)/CN
GPMM»SHATX(2,IS1)*1000000./1440.
CSIZE=.27S*«PMN
IFCCGPM-CSIZE) 8,8,2
2 CSIZE3.3SO*GPMN
IFCCCPM-CSIZE) 12,12^4
4 CSIZE=.S90*GPMN
IFCCGPM-CSIZE) 16.16,6
6- CSIZEsGPMH
CN*NCN+1
GO TO 20
8 IF(GPMM-CSIZE»
-------
GO TU 20
24 CCOST (1)378500. *(!.-. 044* (CN-2.) }*CN
GO TO 32
26- CCOS-T CD *9&000. +{1. -.044* (CN-2.))*CN
GO TO 32
28 CCOSTClJsl 40000, »(l.-.044»CCN-2.n*CN
GO TO 32
30 CCOST Cl )sl 60000, *C1.-. 044* (CH-2.) 3 »CN
32 AFC3Rl*(l.+RI)»*10./t(l.-l-RI)»»iO,-.l,)
ACOST(l)3CCOSTa)*AFC/SMATX(2,n/'36SO.
XsALOGCDSOL)
IFCXCENJ 40,34,40
34 OHRS3EXP(7,6215t7-.476977*X + 1,07l516»X**2}
GO TO SO
40 OHRS3EXPc7.264153-.466246»X*.0695S2*X*-»2)
50 XMHRS3EXPC5.997U5-.493809*X*.070892*X**2)
SUPPsEXP(-2.822519+.700948»X)»1000.
COSTOCUsC COHRS-(-'X.MHRS)»DHR»(l.+PCT}-».SUPP»WPI-fCHEM)/
. SMATXC2, 13/3650,
OMATX(l,N)sCGPH
O.MATXC2,N)=DSOL
OMATX(3,N)sAFC
OHATXC4,N)3CSIZE
OMATX(5,N)3CN
SHATX(10,IS1}3SAVE2
EERsGPMM*. 7457*24,
RETURN
END
C
C
C
C
C
C
C
C
SUBROUTINE AEROB
AEROBIC DIGESTION
INTEGER 051,052
COMMON SMATXC20,45),DMATXC20,50),OMATX(20,50),IPCSO),
2 INP,IO,IS1,IS2,OS1,OS2,N,IAERF,CCOST(5),COSTO(5),
3 ACOST(53,DHR,PCT,WPI,CCI,RI,AF,HATIO,CKWH,
4 CF,EER,EEP,ALAND
SRT3DMATX(3,M)
OEGC3DMATX(4,N)
DTA»DMATXC5,N)
CB1 aOMATXC6,N)
CY *DMATXC7»N)
TSSl3DMATXC9,NJ
TSS23DMATXC9,N]
CRN 3DMATXC10,NJ
AEFF»DHATX(11,N)
COMPUTE INFLUENT BIOMASS, REFRACTORIES, AND BOD
XASSINsSMATX(20,ISl)
XRSSINsSMATXC4,ISl)/SMATXC3,ISl)»SMATXC9,ISl)-,2»XASSIN
SIN3SMATX(8,Isn+SMATX(17,ISl)-.97*,8*XASSIN
FIND EFFLUENT SOLIDS CONC.
CB3CB1»CSRT+OTA)»*-,415»(1,05)»»CDEGC-20.)
XAS53CXASSIN+CY»5IN)/(!,+,8»CB*DTA)
XRSS=XRSSIN+,2*ffB*XA5S»DTA
TSS3SMATXC7, IS1) *XASS4-XPSS
142
-------
60
C
C
70
80
C
C
90
C
C
100
110
FIND FLOW PATES IN OUTPUT STPEAMS
IF»SMATX(t4,ISl)+SMATX(6,ISl>-SMATX(6,OSl)»SHATXC2,OSl}/
2 SMATX(2»IS1)-SMATX{6,OS2)»SMATXC2,OS2)/'SMATXC2,IS1}
SMATX(IS,OS1)*SMATX(15»IS1)
CHECK FOP NITRIFICATION
IFtSRT+DTA.LT.5.) GO TO 90
IFtCKN.EQ.O.) GO TO 90.
CKN*CKN*1.0S*ȣDEGC"20.)
SMATXd8,031331./CCSRT+OTA)».05*CKN-1.)
SMATX(19,031>»SMATXC13»OS1)-SHATXC18,031)
GO TO 100
SHATXC19,OS1)=SMATXC19,IS1)
SMATXCl8,OSl)aSMATX(l3,OSl)-SMATX(19,OSl)
ADJUST ALKALINITY AND SOLUBLE BOD
SMATX(16,OSl)»SMATXC16,ISi)-7.14»11,19
SMATXtl.OS2)aO.
120
130
C
C SIZE DIGESTOR
140 VAER3DTA*SMATXC2,IS1)*DMATXC16,N')»1000.X7,48
X3ALOGCVAER)
CCaSTCl)3EXPC2,414380*.173682*X+.084742*X*»2-.002
-------
150
160
C
C
C
C
COMPUTE AIR REQUIREMENTS
ACFM3C1.5-1.42*CY)»SMATX(2,1S1)»SJ:N»8.33
CB3CB1*U.05*»CDEGC-20.))»CSRT+DTA)**-,415
ACFMaACFM+l.42#CB*.8-»XASS*VAER»l,E3*7.48»8,33Xt.E6
ACFM3ACFM+4.6*CSMATXU9,OSl)-SMATXU9,ISin»SMATXC2,ISn*8.33
ACFM»ACFMXAEFF/.232X.07SX1440.»DMATXC15,N)
IF(20,»VAER*DHATXa5,N).GT.ACFM)ACFM320.»VAER*DMATXCl5,N)
XaALOG(ACFM/1000.)
CCOSTC2)=EXP£4.145454*.633339*X+.031939*X*»2-.002419»X»»3)»lrE3
XX3ACFMX1000.XDMATXC15.N)
X*ALOG{XX3
IFCXX.GE.DGO TO ISO
OHRS*850,
XMHRSS350.
GO TO 160
OHHSsExP (6.900586-1..323725»X+.059093*X»*2-.004926*X»»3)
XMHR53EXP(6.169937+.294853*X+.17599»X»*2-.040947»X**3+.0033»X**4J
HP«ACFMXDMATXC15,N}»8,1*144,/(33000,*,8)
XKWa.8*HP
XKWPY3XKW*24.*365.
ECOSTsXKWPY«CKHH
8COST31000,*EXPC.62138+.482047»X)
TMSUsECOST*SCOST*WPI
COSTO(2)»aOHRS+XMHRS)*DHR*(l.+PCT3't'TMSU)/SMATXC2,l)X3650.
ASSIGN VALUES TO OUTPUT MATRIX
OHATX(l,N)aVAER
OHATX(2»N}3ACFM
OHATXC3»NJ=0.
OHATX(4fN>»0.
COMPUTE ENERGY REQUIREMENTS
EER»XKWPXX365.
RETURN
END
C
C
C
LAND DISPOSAL SUBROUTINE
SUBROUTINE LANDD
INTEGER 051,032
COMMON SMATX(20,45),DMATXC20,50)rOMATX(20,50),IP(50),
INP,IO,ISl,IS2,OS1.0S2»N,IAERF,CCOSTC5),CaSTOC5},
ACOST(5),DHR,PCT,WPI,CCI,RI»AF,RATIO,CKWH,
CF,EER,EEP,ALAND
DIMENSION CAPACC6),TRUCKffiJ,OPER(6J,XMPG(6)
DATA CAPAC/1200.,2500.,5500.,10.,1!».,30./
DATA TRUCK/25000.,42000.,55000.,25000.,42000.,50000.X
DATA OPER/.2,.25,.3,.2,.2S,.3X
DATA TMPGX4.5,4.5,3.5,4.5,4.5,3,5X
HHPY3DMATXC1.N)
DIST»DMATX(2,N)
YRSI,«DMATX(3,NJ
FCOST3DMATXC4.N)
SP3DMATX(7,N)
TNMAX»DMATX(8,N)
ECFTsDHATXtt5,N)
AFCTR»RI»C1,+RI)*»YPSLX C(1.+RIJ *«YRSL-1.)
144
-------
c
c
10
20
30
C
C
25
26
27
28
COMPUTE HAULING COSTS
SLV»0.
JSTXPEsO
WS3(SMATXUO»Isn+5MATXCt5.1Sm*SMATXC2,ISl)*8,33
WWsSMATXC2»ISl)*8,33E6-WS
IFCHS/CWS+W«}.CE.0.15) JSTYPE»1
IFtJSTYPE.EQ.nGO TO 10
ASV*SMATXC2,IS1)*365.E6
JlsO
GO TO 20
ASV*CWS+WW}»365./55,/27,
CTsl,E20
IF{DIST.GT.20.}ARSs(25I>»20./DISTm35.*CDIST-20.)/DIST5
AHPT*(2.*OIST/ARS)+.75
DO 30 KTYPE*1,3
TPYaASVVCAP AC C J 1 +KTYPE )
MT*TPY/'rPTPY-»ECri + , 9999999
ATM»TPY*2.*01ST
TMHPY»AHPT»TPY*1.1
CCsNT»TRUCKCJl*KTUPEJ*WPI*H8.2/150,2
CAsCC».35*AFCTR+.15»CC»Rr
CO»CO'>TMHP1f»DHR*f 1 . +PCT5
IF(CA+CO.GE.CT)GO TO 30
CTsCA+CO
CCOST(2)aCC
ACOSTC2)aCA
COSTQC2)»CO
*3/170.3
OMATX{l,N)aTPY/NT
OHATX(3>N)»NT
CONTINUE
EER3EEJ*#14000a-.*i,000293*CF/36S,
ADD ON FACILITY COSTS
IFCJSTYPE.EQ.13GQ TO 25
QzASV/l.ES
Cl»200lS.»a»*.32
C3* 936»*Q»*.22
C4* 900.»Q»»,3
GO TO 28
Q3ASV/1.E3
IFtQ.LT.lS.KO TO 26
Cl»13849.»0*».32
GO TO 27
Cls32387,
C2sl7700,»Q**.40
C3s 936.*Q»»»22
C4» 900.»Q**.3
CCOST(23»CCOST(23*C1
ACOST(2)'(ACOSTt2)+Cl»AF)/SMATX(2,l)/3650.
TMSUaC2»CKWH+C3*WPI ,uc,,,,
COSTO(2)s(COSTO(2)+(C4*DHR»(l.+PCT)-ctMSU)}
COSTOC2)»COSTO(2)/SMATXC2»U/3650.
EERsEER>C2/365,
145
-------
c
c
40
C
C
C
50
55
C
C
C
60
C
C
C
70
COMPUTE STORAGE COSTS
TONS»WS»365./2000.
IFCDMATXC6,N).GT.0.3GO TO 60
ALAND»SMATXC2,IS13»CSMATXC5,IS13.t.SMATXU3,IS133»3040./TNMAX
IF(JSTYPE,EQ.13GO TO 50
SLV»SP»365.»S.MATXC2,I513»t,E6/7.48/l.E3*DMATXC16,N3
IF(SLVrLE.O,)GO TO 40
XaALOG(SLV)
CCOSTCl)»EXP(.375449+r394996#X+.O.i4726*X**2j»1000.
IFtTONS.LE.O.JGO TO 50
X»ALOG(TONS3
OHRS»EXPC&.567594-,971759»X*,09S689*X»*2)*SP
XMHRSsEXPC-2.087393+2.395831*X-.340388*X#*2+
,017499*X*»33»SP
COSTO(l)»C(OHRS+XMHRS)»DHR*(l,+PCm/S«ATXC2»l)X3650.
COHPUTE LAND AND APPLICATION- COSTS
CCOST(3J»AI.AND»DMATX(9,N)
COSTOC3)»tALAND*DMATXCS,N)*RI+TONS»DMATX(10,M))/SMATXC2,l)/3650.
IFCJST1fPE.EQ.l3GO TO 55
EER»EER-H80.»SHATXC2,IS13»293.«CF
GO TO 70
EER»EER+71.43*ASV/1000.»293.*CF/365.
GO TO 70
COHPUTE LANDFILL COSTS
WTPD»TONS/365,+HW/2000,
ALAHD»3.75E-3»HTPD*365.
CCOST£3)3ALAND»OMATX(S,N)+l.E4»C«TPD**,743*1.506/2.4
COSTOC3)a9480.*(WTPD*».625)»1.192/1,7/SMATX(2,13/3650.
EERaEER+18.*ASV/1000.*293r»CF/365,
FILL IN OUTPUT DATA
OMATXC4,N)3SLV
OMATX(5»N)*TONS
OMATX<6,N)»ALAND
OMATX(7»KJsALAND»DMATX(5.N3»RI
IP(DHATX£fr,N-).GT.O.>OHATX£7,H>BO.
OMATXC8,M)aALAND#DMATXC5,N3
OHATXC9,N3»AFCTR
RETURN
END
SUBROUTINE LIME
LIME ADDITION TO SLUDGE
INTEGER OS1,QS2
COMMON SMATXC20,4S),DMATX(20,50),OMATXC20,503,IPC50),
. INPfIO.IS1,IS2;OS1,OS2,N,IAERF,CCOSTC53»COSTOC53,
, ACOSTCS3,DHR,FCT,WPI,CCI,R!,4F,RATIO,CKrtH,
. CF,EER,EEP,ALAND
DLIMEaDMATXCUN)
CIiIMEaDHATXC2,N}
DTOMa(SMATXC10,IStJ+SMATX(l5,ISl))»SMATXC2,IS 13*8.33/2000.
PPDL«DLIME*DTON»DMATX{16,N3
DO 10 1*2,6
10 SMATX(I,OS1)3SMATX(I,IS1)
5MATX(7,OS13»SMATX(7,IS13+PPDL/8.33/SMATX(2,IS13
SHATXC8,OSl)aSHATXC9,IS1)
SMATX(9,QS13aSMATXC9,IS13
SMATXC10,OSl)3SMATX(lO,IS13+PPDl-'8.33/SMATXC2,IS13
SHATXC20,031)30.
146
-------
DC 20 I3ll»19
20 SMATXCI»OS1)«SMATXCI.IS1)
OHPSoO
XMHRS»EXPC6,0600S4+.t97073*XJ
BETUFN
END
BIOLOGICAL CON'tACTOH^- FINAL SETTLES
SUBROUTINE R8C
POTA1ING
COMHONPS°ATXC20,45),DMATXC20,50),OMATX120,50),IF<50),
INP,IO,IS1,IS2,OS1,OS2,N,IAEHF,CC05TC5),COSTO<5),
. ACOSTC5), DHR,PCt,v,PI,CCI.RI,AF,PATIO ,CK»iH,
. CF.EER,EEP,ALAND
80D=DMATXU»N>
XNSTG=DMATX(2,N)
DEGCsDMATX(3,N)
QPA8I3DHATX(4,N)
QPANI=DMATX(5,N)
BODN=DMATX(7,N)
TSS=DMATX(8,N)
CPDisDMATXt9iN)
QPA8sOHATX(4,N)*t.04»»CDHATX(3,N)-20.)
QPANSDMATX(5.N)*1.04»*(OMATXC3.N)-20.)
PBOOaDMATX(lfN)/(SMATXCl7,ISX)*SMATX{Bf J&l
TEMP1=ALOGCPBOD)/DHATX(2,N)
TEHP2al ,/EXP(TEI*Pl)-le '
APSTGaSMATX (2, IS 1)» 1000000. »TEHP2/QPAB
NTRN=APSTG»DMATXC16,N)/1.E5
NTPN=NTRN*1
NSHFT=NTRN*XN5TG
XNTRNaNTRN
XSHFTsNSHFT
TEMP3=1 /(l.+QPAB»APSTG/SMATXt2.ISl)/1000000.)
P80D=DMATXC7,N}/(SMATXtl7.I51)+SMATXC8.ISn)
FNSTG3ALOG(P80D)/ALOGCTEMP3)
pNH3S(t!/(U+aPAN»APSTG/SMATXC2,ISl)/1000000.))*«RNSTG
SMATXU8,OSU=SHATX(18fISl)»PNH3
SMATX (2,052 )=PD5D/DHATX(8,N)/10000./8. 33
*
SMATX(10,OS1)=4.5*.51»DMAIXC1,N)
SHATX(10.0S2)=DMATXC8,N)»10000.
SMATX(8,OSl)=CSHATXC10,OSl)-4,5)».897
SMATXC17,OSn=OMATXCl.N)-SMATXC8,OSn
SWATX{l9,OSl)5SHATX(l8,ISl)-SMArX(l»fOSl)
UPSS=5HATX(2.Isn/(5MATXC2.asn*SMATXl2,aS2)*5MATXC10,
, SHATX(10,aSl) )
SMATX{4,OS1)=UHSS»SMATX(4,IS1)
SMATX(3,OSl)=S«ATX(8,aSt)«1.6/2t7+SMATXl4,OSl)
147
-------
70
80
90
100
"SMATXC6,OS1)=,01»SMATXC3,OS1)
5MATXC7,OS1)=URSS»SMATX(7,IS1)
SMAIXC9,OS1)3SMATX(10,OS1)-SMATXC7,OS1)
SMATX(ll,OSl)=SMArxC12,ISl)+SMATXC17,USn«1.6/2.7
SMATX(12,OSl)sSMATXC12,ISl)
TEMPsSMATX(2,031)/SMATX(2»ISlJ
TEMP=TEMP+SMATXC2, 032 )/SMATXC 2, IS1)«SMATX CIO, 032 )/SMATX (10,051)
SMATXC13,OS1)=SMATXC5,IS1)+SMATX(13,IS1)-TEMP*SMATXC5,051)
SMATXC14,OSl)sSMATX(6,ISl ) +SHATX ( 14. 1S1 ) -TEMP*SMATX C6 . OS 1 )
SMATXC15,OsnsSfAtX(15. JS1)
SMATXCl6.0Sn=SMArXC16,ISl)-lO,*(SMATX(18,XSl)-SMATX(18,051))
5HATXC20,OSUsO,
S«ATXC20fOS2)=0.
TEMP43SHAIX ( 1 0, Ooi 3 /SM ATX t 10,031)
DO 60 J=3»9
60 SM'ATX (U,052~) STEM? 4«SM ATX 1 0,031)
00 70 Jsll,19
SMATX(J,OS2)sSHATX(J,OSl)
AFS3SMATXC 2,031 )« 1000000, /DMATXC6 ,N ) »UM AIX ( 1 5, N)
PREHs(SMATX( 18, IS1)-SMATX(18,OSI))»100./SMATX( 18,151)
OPATsSMATX(2,Isn«1000000./APSTG/XNSTCi
IF(NSHFT-20) 80,30,90
CC05T(l) = (28500,+45.*DMAT.X(9,N))«N5riFT*1.50b/2.12l5
GO TO 100
CCOSTC1 ) = t 23000, +45, *DHATXC9,N) )»NSHFT«1 , 506/2 . 1 215
XSALOGCAPEA/IOOO ,/DMArxd6,N) )
OHPSxEXPC1.323670+,5242l5«X+.023076»X*«2)
XMHRSsEXP(-,124185+,840104*X*,007757#X*»i)
COSTH3(CCOSTC1)-45,*DMATX(9,N)*NSHFT»1 ,506/2.1215)*. 02
COSTEsNSHFT»5,».746*24.«365,«CK«H
THSUsCOSTM+COSIb
COSTO(l)a(CUHRStXMHRS)«DHP»(l,+PCT)+'rMSUJ/SMATX(2,l)/3650.
XsALOGfAFS/1000.)
1000.
X3ALOG£AFS/1000./DMATX(15,N) )
OHRSsEXP(5,846565t,254813«X+,ll J703*X#»2- . 0 10942»X«»3 )
XMHRSsEXPCS. 27341^+. 228 329«X+.122646»X«*^.,011672#X#»3)
TMSU3EXPC5, 66988 1+, 750799* X)
COSTO(2)s((aHRS+XMHRS)«DHR*(l.+PCT)t'rMSU«WPI)/SMATXC2, 1)/3650,
OHATX(l,N)sQPAB
OHATX(2,N)sQPAN
OCATX(3,N)=APSTG
OMATX(4,N)sAREA
OHATXC5,N)sFNSTG
OMATX(6,N)sRNSTG
OHATXC7,N)=RATIO
OMATX(8,N)=PREM
QMATX(9,N)sQPAT
OMATX(10,N)=AFS
OHATXC11,N)=PD3U
OMATXC12,N)sURS5
OMATXC13,N) = XNTflN
OHATXC14,N)=XSHFT
OHATX(1S,N)=COSIM
OHATXC16,N)=COSTE
OHATX(17,N)=COSTL
EERsCOSTE/36b,/CKWH
EER3EEP+EXP(2.3248+.30093*X+.022308»X»*2+
2 ,OOJ5144«X««3)
HETURN
END
148
-------
c
c
c
c
c
c
c
10
C
C
C
20
30
C
C
C
40
43
SUBROUTINE PSASFS
PRIMARY SEDIMENTATION, ACTIVATED SLUDGE, *AS RETURNED TO PRIMARY
CLARIFIES.
INTEGER OS1.0S2.0S11, 0312,0821, 0522, QSlSAV,OS2SAV
COMMON SMATX(20,45),DMATX(20,50),OMATXt20,50),IP(50),
INP,IO,IS1.IS2,OS1»OS2,N,IAERF,CCOSTCSJ,COSTO(5),
ACOSTC53rDHR,PCT,WPI,CCI,RI,AF, RATIO, CKWH,
CF,EER,E£P, ALAND
DIMENSION TPEC¥CC20),CTEMP(5)»TEMPO(5),ATEMPC5)
SAVE STREAM AND PROCESS ID NUMBERS
NITERsl
ISISAValSl
OSlSAVsOSl
QS2SAVaOS2
NSAVEsN
Nl3DMATX(l,N5
N2aDMATX(2.N)
EPSs.OQl
N1TMAX320
IS113QS2+1
OS11SQS2-I.2
OS123QS2
OS213QS1
OS22=OS2*3
DO 10 1=2,20
SMATX(IrOS22JsO.
TRECYC(IJ=0.
MIX STREAM IS1SAV WITH QS22
TEMPl»SMATXC2,OS22)+SMATXC2,ISiSAV3
DO 30 133,20
TEMP2sSMATXC2,OS22)*SMATXCI»OS22)
TEMP2*TEMP2+SMATX(2,IS1SAVJ»SMATX{I,IS1SAV)
SMATXtI»IS115sTEMP2/TEMPl
SMATX(2»IsmaTEMPi
EVALUATE PROCESS PERFORMANCES
N»N1
ISlsISll
OSlaOSll
OS2»OS12
DO 40 131 ,5
CCOSTCDaO.
COSTOClJaO.
ACQSTCIJaO.
CALL PRSET
DO 45 131,5
CTEMP ( I JaCCOST ( I )
TEMPO(I)=CaSTO(I)
ATEMP(I)»ACQST(I)
EEPTMPaEER
5.0
ISl^QSll
- OS13QS21
OS230S22
DO 50 isl, 5
CCOSTtDsO.
ACOST{I)»0.
cosToen=o.
CALL AERFS
IF(IAERF.GT.O)GO TO 1000
149
-------
55
C
c
C
60
C
C
C
C
70
80
C
C
C
90
100
C
C
C
1000
DO 55 131,5
CCOSTCIJsCCOSTm+CTEMPm
COSTO CI) sCOSTO C I) -(-TEMPO (I )
ACOST(I)3ACOSTCIJ+ATEMPU)
EERaEER+EERTMP
COMPARE RECYCLE STREAM QS22 WITH TRECYC
DO 60 132,20
IF(ABSCSMATXU,OS22)-TRECYCCIJ)-SMATXU,OS22)*EPS)60,60,70
CONTINUE
GO TO 90
CONVERGENCE NOT ATTAINED. INCREMENT ITERATION COUNT.
SAVE STREAM QS22 IN TRECYC. REPEAT ANOTHER ITERATION,
NITER3NITEP-H
IFCNITER.GT.NITMAXJGO TO 100.0
DO 80 132,20
TRECXCCI)3SMATXCI,OS22)
GO TO 20
CONVERGENCE ATTAINED,
NsNSAVE
DO 100 1=1,20
OMATXCI,N)3dMATX(I,N2)
RETURN
ITERATION LIMIT EXCEEDED OR HtASS CANNOT BE ATTAINED,
IAEP.F31
GO TO 90
END
C
C
C
c
c
10
20
C
C
30
SUBROUTINE HEAT
HEAT TREATMENT SUBROUTINE
INTEGER 031,052
COMMON SMATXC20.45),DMATXC20,50),QMATXt20,50J,IP(50),
2 1NP.IO,IS1,IS2,OS1,OS2,N,IAERF,CCOST(5),COSTO£5),
3 *COSTC5),DHR,PCT.WPI,CCI,RI,AF,BATIO,CKWH,
4 CF,EER,EEP,ALAND
IlsDMATXCl.N)
I2»DMATXC2,N)
IDIGsDMATXC3,N)
TEMPsDMATXC4,N)
FIND FRACTION OF SLUDGE FROM PRIMARY TREATMENT
IF(IDIG.GT.O)GO TO 30
IFCI1.EQ.OJ GO TO 10
IF(I2,EQ.O) GO TO 20
FPpl3l.-SMATXC2,I2)*SMATX.aO,I2)/SMATXC2,Il)/SMATXUO,Il)
GO TO 30
FPRIsO.
GO TO 30
FPPIsl.
COMPUTE EFFLUENT STREAM CHARACTERISTICS
AS3l.32S5-.00457»TEHP
APsl.8112-.00596*TEMP
AD»1.9698-.00709»TEMP
BS3t,5855-.00657»TEMP
BPal.8455-.00657»TEMP
BDsl.9855-.00757»TEMP
GNDs.00163*TEMP-|..1755
CDs.00163»TEMP+,0755
150
-------
c
c
ALPHA3(l-IOIG)*CFPRI»AP.t.+IDIG»BD
GAMMA3(1-IDIG)*GNO+IDIG»GD
5MATX(2,OSn=SMATXC2.ISl)
SMATXC3,QSn=SMATXC3,ISn*8ETA
SMATX<4,OSn3SMATXC3,OSn»SMATX(4,ISl)/SMATXC3,ISl)
SMATXC5»OSn3SMATX(5»ISn»BETA
SMATXC6,QSt)3SHATXC6,ISn»BETA
SMATXC7,051}3SMATX£7rISl)
SMATXC9,OS1)3SMATX(8.IS1)»ALPHA
SMATXC9»C)SnsSMATXC9,ISn»BETA
SMA.TXCtO,OSn=SMATXC7,CJSl).+SMATXC9,CISU
SHATX(12,QSl)*SMATXai,OSl)*(l.-<3AMMA]
SMATXC13^0Sl)=SMATX(13,ISl)-t-SMATXC5,ISl}»tl-."BETA)
SMATXCl4,QSl)=SHATXC14,Isn+SMATX(6,ISl)*.073395*X#*2 3 »1000.». 60873
OHRS3EXP(8.428l-.084636*X*.0596H»X»*23
OHPSsQHRS*BMA7X{5rN)*S2i/8000.-
XMHRS=,25»OHRS
TMSU=EXPC8.8497-.13093»X+.073644*X*»23*.6643»WPI
XKWPY3.007»GPM/DMATX£16,N)#60,«DMATX(5.»N)*52.
ECOST=XKWPY*CKWH ,
OTQTs(TEHP-20.)*,25*lSry.75*GPM/DMATXC16»N)»60.»DHATX(5,N}*52.
HPDsDMATXC5,N)/DMATX(6,N)
IF(HPD.LT.8.3FKs2000.
IF(HPD.LT.16,)FK=1500.
IF(HPO.LT,24.)FK3lOOO.
IFCHPD.GE.24.)FK=Q.
SPY»DHATX(6,NJ»52.
QTOT3QTOT*C12.»2000.+FK»(SPV-12.))»GPM/DMATXC16,N)»60.
>COST3FUEL*DMATX(7,N)/1.E6
TMSUsTMSU+ECOST+FCOST
CaSTOCl)3(COHPS>XMHRS)»DHP»(l.+PCT)-)-THSU)/SMATX(2,l)/3650.
EER3XKHPY/365.+QTOT/ 365. », 00029 3»CF
FILL IN VALUES OF OUTPUT MATRIX
OMATXC1,N)=GPM
OMATXC2*N)3ALPHA
OMATX(3,N)=BETA
OMATX(4»N)=QTOT/1.E6
RETURN
END
151
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/2-79-158
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
COMPUTER-AIDED SYNTHESIS OF WASTEWATER TREATMENT
AND SLUDGE DISPOSAL SYSTEMS
5. REPORT DATE :
December 1979 (Issuing Date)
6. PERFORMING ORGANIZATION CODE
7, AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Lewis A. Rossman
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Municipal Environmental Research Laboratory—Gin., OH
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
10. PROGRAM ELEMENT NO.
1BC821, SOS 1
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
Same as above
13. TYPE OF REPORT AND PERIOD COVERED
Inhouse (Feb. - Oct. 1978)
14. SPONSORING AGENCY CODE
EPA/600/14
15. SUPPLEMENTARY NOTES
Project Officer: Lewis A. Rossman 513/684-7636
16. ABSTRACT
A computer-aided design procedure for the preliminary synthesis of wastewater
treatment and sludge disposal systems is developed. It selects the components in
the wastewater treatment and sludge disposal trains from a list of candidate process
units with fixed design characteristics so that criteria on effluent quality, cost,
energy, land utilization, and subjective undesireability are best satisfied. The
computational procedure uses implicit enumeration coupled with a heuristic penalty
method that accounts for the impact of return sidestreams from sludge processing.
The programmed version of the design procedure, called EXEC/OP, has been interfaced
with the unit process subroutines contained in a previously EPA developed system
evaluation program known as EXECUTIVE. A number of case study design problems are
presented to demonstrate the versatility of EXEC/OP. Included among these is a
preliminary cost/energy-effectiveness analysis for a hypothetical design problem
containing over 15,000 alternative system configurations.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Waste treatment
Sludge disposal
Facilities
Systems analysis
Optimization
Alaste water treatment
Alaste treatment facilities.
Sludge treatment
Treatment facilities
System synthesis
Multi-objective planning
13B
18. DISTRIBUTION STATEMENT
Release to public
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
160
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
152
U.S. GOVERNMENT PRINTING OFFICE: 1980-657-146/5556
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