United States
Environmental Protection
Agency
Municipal Environmental Research
Laboratory
Cincinnati OH 45268
Research and Development
EPA-600/S2-81-221 Dec. 1981
Project Summary
Comparative Study of
Air Classifiers
G. M. Savage, L. F. Diaz, G. J. Trezek, V. Hopkins, B. Slmister, D. Fiscus, S. C.
James, and D. Brunner
This paper presents the results of an
extensive air classifier field test
program conducted for the U.S.
Environmental Protection Agency
(EPA). Methods of testing, criteria for
evaluation, operating conditions, and
assessment of air classifier perform-
ance are described. Topics germane to
the design and operation of air classi-
fiers are also covered. Test results
show that air classifiers performances
can be compared under equitable
conditions. The comparisons presented
here permit judgements about the
relative performances of air classifiers
and the tradeoffs that must be con-
sidered when describing air classifier
performance.
This Project Summary was devel-
oped by EPA's Municipal Environmen-
tal Research Laboratory, Cincinnati,
OH, to announce key findings of the
research project that is fully docu-
mented in a separate report of the
same title (see Project Report ordering
information at back).
Introduction
Seven air classification systems with
nominal throughputs ranging from 4 to
91 Mg/hr have been field tested and
evaluated under a 2-year program
sponsored by the U.S. Environmental
Protection Agency (EPA).1 The purpose
of the testing program was to charac-
terize and compare the operation and
performance of air classifiers located in
the field. During the course of the work,
characterization criteria were developed
that enabled the comparison of all air
classifiers on an equivalent basis.
Because the best air classifier perform-
ance existed only for sufficiently dilute
air/solids mixtures within the air
classifier column, the air/solids ratio
was chosen as the means for establish-
ing the performance parameters. For
each set of operating conditions, the
performance parameters varied rela-
tively little when the air/solids ratio was
greater than a critical value, that value
being defined by the point where the
constant light fraction split fell off by 1
percent. (Constant light fraction split is
defined as that value of the light fraction
split that does not change at a given air
flow and is independent of the air/solids
ratio.) The unvarying nature of material
characteristics above the critical air/
solids ratio has been documented
previously.2
The seven air classifiers tested in this
study, their locations, and their general
descriptions are given in Table 1.
(Further details concerning the geomet-
rical configuration of the air classifiers
are available in the final report to EPA.)
An examination of some of the key
characteristics of the solid waste
encountered during the air classifier
testing program shows the importance
of normalizing the performance param-
eters in terms of the air classifier infeed
composition. Wide variations exist in
the waste characteristics from site to
site. For example, the paper and plastic
content of the infeed to the Los Angeles
air classifier average 30.2 percent,
whereas it exceeded 50 percent at the
other six air classifiers.
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Table 1. Summary of Air Classifiers Tested
Site
Tacoma, WA
Baltimore Co., MD
Richmond, CA
Ames, IA
Los Angeles, CA
Akron, OH
Pompano Beach, FL
Type of
Air Classifier
Horizontal
Vertical
Vertical
Vertical
Vertical
Vibratory,
inclined
Vibratory,
inclined
Design
Throughput
(Mg/hrJ
73
91
4
45
4
64
6
Cross
Sectional Shape
of Column
Rectangular
Circular
Rectangular
Rectangular
Rectangular
Rectangular
Rectangular
Two of the air classifiers were fed
shredded and screened solid waste
(Ames and Pompano Beach), and the
other five air classifiers received
processed waste that had been shredded
only. Consequently, normalizing the
performance parameters in terms of the
infeed composition was essential to
allow comparisons among the air
classifiers. Note that the ash content of
the screened air classifier feed material
at Ames and Pompano Beach ranged
from 13 to 14 percent, whereas it was
31 to 40 percent at the five sites not
employing screening before air classifi-
cation.
Abbreviations for terms specific to air
classifier performance and operation
are listed in Table 2.
Table 2.
Air Classification
Abbreviations
Abbreviation
A/S ratio
LF
HF
Fe
NonFe
PP
Term
Air /sol ids ratio
Light fraction
Heavy fraction
Ferrous metals
Nonferrous metals
Paper and plastic
Test Procedures and Methods
of Analysis
For each air classifier, the test
program consisted of collecting samples
of heavy and light fractions under a
specified set of operating conditions.
Samples were collected at three discrete
air flow settings (high, medium, and
low) over as wide a range of material
flowrates as was possible within the
physical constraints imposed by the
facility or the air classifier system. One
of the three air flow settings was the
operating mode used by the plant under
normal processing conditions. The
reason for selecting three air flows and
a full range of throughputs is that no
generally accepted definition exists for
an optimum air classifier operating
condition or setpoint in the industry. In
addition, manufacturers' data or recom-
mendations as to an air classifier
setpoint are not typically available.
Five samples of heavy and light
fractions were collected simultaneously
for each air flow setting. Before each
actual test run, a dry run was used to
determine the time intervals needed to
collect simultaneous samples of heavies
and lights. Particle size of the heavy and
light fractions was used to determine
the quantity of material to be used for
the size distribution and composition
analyses. Size distribution and composi-
tion analyses were performed with
subsamples from the heavy and light
samples collected for mass flowrate
measurements. The latter samples
generally ranged from 10 to 50 kg.
Samples for the size distribution and
composition analyses ranged from 2 to
10kg.
The general procedure involved
setting a particular airflow through the
classifier, by varying the rpm of the air
classifier fans or adjusting a damper.
Subsequently, samples of heavy and
light fractions were collected simul-
taneously for a number of different
material feed rates to the air classifier.
Samples of heavy and light fractions
were collected from conveyors down-
stream of the classifier. These samples
served the dual purpose of allowing
calculation of the flowrate of the heavy
and light fractions and providing the
material from which representative
samples were chosen for later labora-
tory analyses. All material was com-
pletely removed from a given length ol
conveyor belting, thus alleviating the
problem of stratification of components
in the flow stream (which might have
skewed the results if only grab samples
had been collected).
Laboratory analysis consisted of air
drying, screening, and manually sorting
the light and heavy fractions—except
for the Ames air classifier testing, in
which it was only possible to collect
infeed and heavy samples. In addition,
heating value determinations and ash
analyses were carried out on light and
heavy fractions collected at each site
following the procedures proposed by
the ASTM for refuse derived fuel No. 3
(RDF-3). Both manual and mechanical
screening were used to determine the
size distribution of the samples. Size
distribution analyses were carried out to
determine the amount of fines (minus
14 mesh) in the heavy and light
fractions and also to provide a quanti-
tative means of describing the particle
size of the shredded air classifier feed
material. Manual sorting of the samples
consisted of separating ferrous, papen
and plastic, and nonferrous components
from the heavy and light fractions.
Composition and size data were used
subsequently to develop the character-
ization parameters.
Also as part of the test program, both
air flow and system power requirements
were measured for each operating
setpoint (i.e. air flow setting).
Results and Discussion
Operating parameters determined for
each air classifier are summarized in
Table 3. Data are shown for each of the
three air flow settings used during the
test program at each site. The volumetric
air flows corresponding to the high,
medium, and low air-flow settings are
reported in the final report.
Critical throughputs are reported in
Table 3 instead of the maximum tested
feedrates because the air classifiers
were operating in a choked condition for
feedrates greater than the critical
throughput value. Consequently, they
were not exhibiting their best perform-
ance for each air flow setting.
The air classifier performance param-
eters were calculated from data for
which the air/solids ratios exceeded the
critical value. For reasons previously
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Table 3. Summary of Operating Parameters
Tacoma
Baltimore
Richmond
Ames
Los Angeles
Akron
Pompano Beach
Air Flow
Setting
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
Critical
Specific Energy
KWh/mg (KWh/t)
460
372
36.2
98
5.9
3.9
8.8
4.9
2.0
>186
>176
>27.4
8.8
78
49
167
21 6
20.6
118
88
88
2/4.7;
(3.8)
(37)
(1.0)
(06)
104)
10.9)
(0.5)
(02)
T31.9)
S/1.8)
2J28I
(0.9)
(0.8)
(0.5)
(1.7)
(2.2)
(21)
(12)
(09)
(0.9)
Critical
A/S Ratio
<16
1 7
1.7
3.5
55
75
57
6.8
as
£2.6
£2.5
120
99
95
36
21
13
33
1 7
0.9
S210
3205
237.8
3.2
2.8
2.0
19.9
26.7
249
44
3.3
3.4
2732
109
104
40
23
14
36
19
10
323 /
3225
335.0
35
3.1
20
21.9
294
27.4
48
36
3.7
' ( = ton, tph = ton per hour
b Air velocity in the vicinity of the heavy fraction discharge point of the Triple S air classifiers
" NA- not applicable.
a Re-adfustment of the air bleed of the air classifier caused an increase in the column velocity despite a decrease in the air flow control setting
discussed, the normalized values are
the significant parameters. Because of
the differing characteristics of the air
classifier infeed material among the
/arious sites, the absolute values for the
performance parameters are only of
value when they are used to judge
individual air classifier performance
and not to compare air classifiers
among different sites. In addition, the
use of the absolute values for evaluating
air classifier performance at a particular
site presupposes that the waste compo-
sition does not vary for the duration of
the tests—a questionable assumption.
Many comparisons and conclusions
can be drawn from the data. For
example, the data show that approxi-
mately 70 to 99 percent of the input
energy content can be recovered in the
light fraction. In addition, for most air
classifiers near their normal operating
points, recovered energy ranges from
90 to 99 percent. As a second example,
the percentage of paper and plastic in
the heavy fraction ranged from 0.8 to
42.8 percent, excluding the Pompano
Beach data, which was skewed because
of the significant amount of preprocess-
ing of the waste before air classification.
The light fractions obtained in Ames
and Pompano Beach have relatively
high heating values. This result maybe
due to the composition of the raw waste
and/or the effect of screening before air
ssif ication. The light fraction obtained
in Los Angeles also has a relatively high
heating value, but it cannot be attributed
to screening, since that unit process
was not employed at the site. Instead it
is partly a consequence of the air
classifier feed at Los Angeles which
also has a relatively high heating value.
To establish criteria for air classifier
evaluation, two key ratios can be used:
recovered paper and plastic/retained
fines, and recovered energy/retained
ash. The first ratio (recovered PP/re-
tained fines) could be used to determine
optimum air classifier performance for
selectively separating paper and plastic
components from fine inorganic con-
taminants (for instance, when fiber
recovery is the intent of a resource
recovery process). On the other hand,
for recovery of a refuse derived fuel, the
ratio of recovered energy (RE) to
retained ash (RA) is significant and has
been chosen for use in this study as the
key criterion for establishing the per-
formance characterization of the seven
air classifiers that were tested.
The selection of the RE/RA ratio as
the key criterion is based on the fact that
both recovered energy and retained ash
in the light fraction are normalized on an
infeed basis, thus eliminating the
effects of variation in refuse composi-
tion. Large values of this ratio imply that
an air classifier is recovering in the light
fraction a significant percentage of the
energy available in the infeed material
and simultaneously dropping out into
the heavy fraction a significant per-
centage of the ash-carrying components.
If the maximum values of RE/RA for
each air classifier are chosen as the
criterion (or premise) for determining
the optimum operating point for recovery
of a high-quality RDF, then other
performance parameters can also be
chosen for comparison based on the air
flow setting that results in the maximum
value of RE/RA for each air classifier.
A comprehensive assessment can be
made of the various air classifiers under
their respective optimum air setting for
RDF recovery (Table 4). Included are the
judging criteria and the key parameters
used to quantify each judging criteria.
The first three parameters (specific
energy, column loading, and RE/RA)
were determined from field measure-
ments. The last two parameters (system
complexity and 1980 costs) are of a
qualitative nature. The judgment of
system complexity was based on visual
observation of the total air classifier
system (including number of conveyors,
blowers, cyclones, mechanical equip-
ment required) the engineering and
structural complexity, and an engineer-
ing judgement of the degree and type of
control necessary to maintain the air
classifier in operating order. Costs were
determined by escalating reported
capital costs (10 percent per annum) to
1980 levels and dividing them by the
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Table 4. Operating and Performance Parameters of Seven Air Classifiers for RF Recovery
Judging criteria and key parameter
Energy Requirement Unit Size
RDF Quality
Avg. Recovered Energy
System Complexity
Cost*
Air Classifier
1. Tacoma
2. Baltimore
3. Richmond
4. Ames
5. LA.
6. Akron
7. Pompano Beach
Specific Energy
(KWh/Mg)
(0,91
15.9)
(5.1 >
(7.6)
(11.4)
(11.4)
(11.2)
Column Loading
(Mg/hr) m
(36.2)
(9.8)
(8.8)
(18.6)
(4.9)
(20.6)
(1 1.8)
Avg. Retained Ash
(Ratio)
(1.5)
(1.8)
(2.3)
(2.0)
(1.9)
(1.3)
(1.5)
Design Simplicity
(Ranking)
6h
3
1
4
2
6
5
1980 Cost Basis
($Mg/hr)
$ 5,800
12,600
4,300
14.OOO
8,200
6,600
6,200
a Estimated capital cost (equipment and engineering) for air classifier system; costs exclude operating and maintenance expenses.
Mg/hr - throughput at the critical A/S ratio.
b / = simplest system; 6 = most complex system.
critical throughput for each air classifier
at its optimum air flow setting.
Conclusions
The testing and performance charac-
terization of seven air classifiers has
shown the ranges of operating condi-
tions and performance that can be
expected for each air classifier. In
addition, methods have been presented
for comparing different types of air
classifiers operating under different air
and material flows and handling
shredded refuse of differing composi-
tion. No absolute means exists for
comparing air classifier performance.
As the data in Table 4 show, positive and
negative points exist for all air classifiers.
But it is now impossible to judge air
classifier performance on a relative
basis if the judging parameters are
judiciously chosen so as to allow an
equitable comparison (i.e., normaliza-
tion of the system outputs to eliminate
the effect of varying infeed composition).
The methods and data presented here
should prove useful in the evaluation of
other air classifiers.
Since the field tests covered a number
of different air classifiers, analysis of
the test data can establish quantities of
waste constituents that should be of
interest to the resource recovery
industry— for example, the percentages
of ferrous metals, nonferrous metals,
retained fines, and paper and plastic in
the light fraction. Results of all seven
field tests taken collectively enable an
overall (or average) value of certain
parameters to be calculated. Such
average values subsequently can be
used for calculation of mass and energy
balances on an air classifier system. For
example, the mass fraction of non-
ferrous metals in the light fraction can
be computed from the mass fraction of
nonferrous metals in the air classifier
feed and the percentage of nonferrous
metals retained in the light fraction.
The testing of these air classifiers
makes apparent the need for a standard
method of testing and evaluation for the
resource recovery industry. Such stan-
dards would not only allow performance
comparisons among different air classi-
fication systems, but they would also
establish the correct operating settings
for producing a specified output.
Presently little control is exercised over
the air classification process. Conse-
quently, the quality of the RDF output
often suffers. As with most resource
recovery processes, air classification
has yet to progress from an art to a
controlled process.
The full report was submitted in
fulfillment of Contract No. 68-03-2730
by Midwest Research Institute under
sponsorship of the U.S. Environmental
Protection Agency.
References
1. Hopkins, V., Simister, B., and Savage,
G., Comparative Study of Air Classi-
fiers, EPA-600/2-81-221, NTIS
PB82 106121, U.S. Environmental
Protection Agency, Cincinnati, Ohio,
September 1981.
2. Savage, G., Diaz, L and Trezek, G.,
"Performance Characterization of
Air Classifiers in Resource Recovery
Processing," Proceedings of the
1980 National Waste Processing
Conference: Resource Recovery
Today and Tomorrow, American
Society of Mechanical Engineers,
New York, May 1980.
-------
G. M. Savage, L F. Diaz, and G. J. Trezek are with Cat Recovery Systems, Inc.,
Richmond, CA 948O4; V. Hopkins, B. Simister, andD. Fiscus are with Midwest
Research Institute. Kansas City, MO 64110; S. C. James (also the EPA
Project Officer, see below) is with the Municipal Environmental Research
Laboratory, Cincinnati, OH 45268; (Don Brunner, with the Naval Civil Engi-
neering Laboratory, Port Hueneme, CA 93043, directed two small-scale sys-
tem studies.).
The complete report, entitled "Comparative Study of Air Classifiers," (Order
No. PB 82-106 121; Cost: $22.50, subject to change) will be available only
from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Municipal Environmental Research Laboratory
U.S. Environmental Protection Agency
Cincinnati, OH 45268
5
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