H. DOBROSKI, JR. ET AL.

TABLE I
Tabular summary of K and R? values for various types and sizes of data sets examined
K and (R?) values
Coal-rock By flow rate
dust mixture within, a mixture within a family
- By mixture By group
% Coal % Rock’ K, (R”) K2 (R’) K3(R?) —_ withina family _ By family Kg (R’)
Dust family dust dust 1 Limin 2L/min 3 L/min Ku (R?) Kp (R*) and Kg (R?)
Black Creek 100 0 1,00 (0.99) 1.49.(0.99) 2,19 (0.99) 1,57 (0.83)
. 75 25 0.94 (0.99) 1,320.99) 1.90 (0.99) 1.42 0.85)" 1.41 (0.85)
50 50° 0,99(0.98) 1.34.(0.99) 1.89 (0.96) 1.26 (0.90).
Illinois No. 6 100 QO — 0,700.99) 0.90.(0.94) 1.46 (0.99) 1.03 (0.82)
75 25 0.77 (0.96) 1.11 (0.98) _ 1.62 (0.98) 1.16 (0.82) 1.14 (0.81) Kg (R’)
50 50 0,810.98) 1.17 (0.98) 1.70 (0.99) 1.23 (0.84) 1.35 (0.84)
Pittsburgh 100 0 0,85 (0.95) 1.11 (0.98) 1.54 (0.98) 1.20 (0.87)
75 25 0.72 (0.96) 1.08 (0.97) 1.53 (0.99) 1.14 (0.82) 1.19 (0.85) and
50 50 0,93 (0.99) 1.19 (0.99) 1.53 (0.99) 1.27 (0.92)
Pocahontas No.4. 100 0 1.11(1.00) 1.61 (1.00): 1.97 (0.97) 1.67 (0.92) Kg (R*)
75 25 1,210.98) 1.49. (0.94) 1.99 (0.90) 1.62 (0.86) 1.64 (0.88) 1.30 (0.94)
50 50 1.23 (0.96) 1.39 (0.92) . 2.10 (0.97) 1.66 (0.87),
Upper Freeport 100 0 0.95 0.99) 1.30 (0.99) » 1.51 (0.98) 1.27 (0.93)
15 25 1,05 0.99) 1.46.(0.99) 1.78 (0.99) 1.45 (0.92) 1.40 (0.91)
50 50 1,07 0.96) 1.45.(0.99) 1.84 (0.99) 1.53 (0.92)
Limestone 0 100 1.01 (0.97) 1.35 (0.98) 1.51 (0.91) 1,35 (0.91) 1.35 (0.91)
14.0
50% error lines
12.0 35% error lines
coo 25% error lines
10.0

bad
°

2
>

Fad
°

2.0

Differential Pressure Increase (AP), cm H,O

Regression for 2 L/min flow rate data, AP = 1.30 M, bracketed by +25%, +35%, and +50% lines.

20

3.0

4.0 5.0

6.0

FIGURE 4

70

8.0

9.0

10.0


10.

. Weingartner, E.; Haller,

DIFFERENTIAL PRESSURE RESPONSE OF GLASS FIBER FILTERS

Novick, V.J.; Klassen, J.F.: Predicting Pressure Response Charac-
teristics Across Particle-Loaded Filters. In: Advances in Aerosol
Filtration, K.R. Spurny, Ed., pp. 337-347. Lewis Publishers, Boca
Raton, FL (1998).

|. Novick, V.J.; Monson, PR.; Ellison, P.E.: The Effect of Solid Parti-

cle Mass Loading on the Pressure Drop of HEPA Filters. J Aerosol
Sci 23(6):657-665 (1992).

jurtscher, H.; et al.: Pressure Drop
Across Fiber Filters. J Aerosol Sci 27(Suppl 1):$639-S640
(1996).

13.

15.

103

Dobroski, Jr, H.; Tuchman, D.P,; Vinson, R.P.: Differential
Pressure as a Means of Estimating Respirable Dust Mass on
Collection Filters. App! Occup Environ Hyg 12(12):1047-1051
(1997).

. Bartley, D.L.; Chen, C.C.; Song, R.; et al.: Respirable Aerosol Sam-

pler Performance Testing. Am Indus Hyg Assoc J 55(11):1036—-
1046 (1994).

Perry, R.H.; Green, D.W.; Maloney, J.O., Eds.: Perry's Chemical
Engineers’ Handbook, 7th ed., pp. 2-119-2-120. McGraw-Hill,
New York (1997).


Differential Pressure Increase (AP), om H,0

Differential Pressure Increase (AP), cm H,0

DIFFERENTIAL PRESSURE RESPONSE OF GLASS FIBER FILTERS

20.0
‘Pittsburgh coal (PI) a
18.0 Pocahontas No. 4 coal (PO)
AUpper Freeport coal ( UF ) a (PO)
16.0 Black Creek coal (BC) ‘
+ illinois No. 6 coal (IL)
14.0

© Rock dust (RD)

iS
°

3
&

@
°

6.0

4.0

2.0

0.0
0.0 1.0 2.0 3.0 40 5.0 6.0 7.0 8.0 9.0 10.0 11.0
Mass (M),mg
FIGURE 2
A comparison of the different dust family responses.

18.0

ame 50% error lines AP = 1.35M

16.0 ,
“+ 35% error lines

14.0

cwoceee 25% error lines

12.0

10.0

8.0

6.0

40

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
Mass (M), mg

FIGURE 3
Regression for all data, AP = 1.35 M, bracketed by +25%, +35%, and +50% lines.

99


DIFFERENTIAL PRESSURE RESPONSE OF GLASS FIBER FILTERS

set of data from this research fitted with a single summary regres-
sion equation. In this composite case, Kg = 1.35 and R? = 0.84,
Figure 4 shows the 2 L/min subset of the data, for which
Kg2 = 1.30 and R? = 0.94. This portion of the data was exam-
ined because cyclones are normally operated at 2 L/min. Table I
lists K and R? values and is the primary tabular summary for all
data gathered in this research.

Being the principal descriptor of differential pressure re-
sponse, K values were calculated for many subsets of the data
collected and recorded in Table I. When an integer, the subscript
of K indicates sampling flow rate in L/min. The subscript “M”
indicates “mixture.” For purposes of this study, a pure dust is
also considered a compositional “mix.” The subscripts “F” and
“G” indicate “family” and “group,” respectively, while “G2”
indicates “group 2L/min.”

PREDICTING MASS BASED ON AP

The data points shown in Figure 3 represent the AP versus
mass behavior for all of the filter samples generated and tested.
If the three boundary conditions of +25 percent, +35 percent,
and +50 percent are used to bracket the line representing the
regression equation AP = 1.35 M, the percentage of data points
within these three boundaries can be used as a measure of how
accurately the equation can predict collected mass on the basis
of measured AP. Figure 4, depicting the 2 L/min subset of the
data, can be used in a similar manner, when its own regression
equation is utilized.

Table II summarizes prediction accuracy based on this ap-
proach, derived from visual data point counts between error
boundaries, performed on Figures 3 and 4 for masses above
2.00 mg. For the set containing all the data collected from highly
variable dusts, error limits of +50 percent are required to achieve
a data point count with 93 percent inside the stated boundaries.
Meanwhile, for the 2 L/min data subset, containing samples with
somewhat less variability, narrower error limits of +35 percent
are required to achieve a similar data point count with 95 percent
inside the stated boundaries.

TABLE II
The percentage of data points that fall within +25%, +35%,
and +50% of the values predicted by two linear regression
equations representing all of the data, and the 2 L/min subset
of the data*

Percentage of data points bounded by
the error limits applied to each equation

Error AP = 135M AP= 130M
Limits (All data) (2 Limin subset)
£25% 58 86
£35% B 95
+£50% 93 100

AFor data with masses above 2.00 mg.

101
TABLE II
Summary of three-composition Kz statistics
Dust Family Mean Ky Kz Std. Dev. CV (%)
Black Creek 1,38 0.093 67
Illinois No. 6 1.06 0.142 13.4
Pittsburgh 1.13 0.057 5.0
Pocahontas No. 4 1.50 ~ 0.110 14
Upper Freeport 1.40 0.090. 64

Table II concentrates on the results for 2 L/min samples,
broken down by dust family, rather than the full data set. It
records the mean K, value for the three dust mixes in each dust
family, as well as standard deviation and coefficient of variation
(CV). Itis noteworthy that the largest CV in the table is a modest
13.4 percent, derived from the Illinois No. 6 data.

GENERAL DISCUSSION

Despite its greater density, it can be seen in the Ky column
of Table I that rock dust still has a AP response comparable to
the other materials tested. Ky = 1.35 for 100-percent limestone
dust, higher than Ky for pure Illinois No. 6, Pittsburgh, and
Upper Freeport coals, but lower than Ky for pure Black Creek
and Pocahontas No. 4 coals. This suggests that density is not
the major factor that determines the magnitude of a dust's AP
response. Table I records that the range of Ky for pure coals
is 1.03 to 1.67. When the mixtures are 50-percent rock dust,
substantially increasing the density of the test dusts, the range for
Ky is 1.23 to 1.66, only moderately changed. This also suggests
that the presence of rock dust, with the attendant effect on dust
density, is not the predominant factor influencing AP response.

Further examining Table I for the influence of rock dust on
AP, we find that adding the denser material to coal dust can
change the coal’s K value, but the trend is not always reliable.
Kw for Black Creek coal is lowered as rock dust is added from
0 percent to 50 percent, while Ky for Illinois No. 6 and Upper
Freeport coals rise as rock dust is added. Ky for Pittsburgh and
Pocahontas No. 4 coals do not show clear trends as rock dust
is added to their mixes. While the presence of rock dust is not
without some consequence, the nature of the coal dust seems to
have greater influence on AP response.

Table I shows that K, < K2 < K3 is a highly consistent trend,
with the same influence of particle size distribution for every dust
mixture tested. Smaller particles produce greater AP responses
perunit mass than larger particles. The average K, value reported
in Table I is 0.96. The average K, value is 1.30, 35 percent
greater, and the average K; value is 1.75, 82 percent greater. For .
several of the dust mixtures tested, K; is more than twice the
value of Kj . Particle size strongly and consistently influences AP
response, regardless of the specific dust mix involved, The strong
impact of particle size distribution and the weaker influence of
density are generally recognized in the literature.468-!2)

‘The R? values recorded in Table I provide evidence as to what
is required for a dust to be accurately measured by a AP-based


Differential Pressure Response of 25-mm-Diameter
Glass Fiber Filters Challenged with Coal

and Limestone Dust Mixtures

Harry Dobroski, Jr., Donald P. Tuchman, Robert P. Vinson, and Robert J. Timko
National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory,

Pittsburgh, Pennsylvania

This article summarizes results of research conducted by
the National Institute for Occupational Safety and Health
(NIOSH) at its Pittsburgh Research Laboratory. The ob-
jective of this work was to determine the correlation be-
tween the mass (M) of respirable coal and limestone dusts
collected on 25-mm-diameter glass fiber filters mounted in
cassettes and the increase in differential pressure (AP) that
develops across the filters when drawing at constant air
flow. Test aerosols were generated inside a laboratory dust
chamber using various coal dusts, limestone dust, and mixes
of the two. Dusts with different particle size distributions
were deposited on the filters by sampling from the chamber
through cyclone preclassifiers at different flow rates. Re-
sults show that the relationship between differential pressure
increase (cm water) and dust mass (mg) is linear and can
be approximated by the equation AP = KM. The K values
(slopes) range from 1.14 to 1.64, depending on the parent
coal of the samples. The influence of particle size distribu-
tion was also found. The overall K value for all the data
summarized in this article is 1.35, with R? = 0.84 for the
summary equation. When calibrated for individual work
sites, or other circumstances where great variability in dust
characteristics is avoided, the relationship between collected
dust mass and increase in differential pressure may provide
an exploitable principle for measurement of respirable dust
concentrations.

Keywords Dust Measurement, Dust Monitoring, Differential Pres-
sure, Pressure Drop, Filters, Coal Dust

As the nation’s mining industry moves into the twenty-first
century, prevalence of occupationally related respiratory dis-
ease remains significant.‘ This long-term health hazard seri-

This article is not subject to U.S. copyright laws.

96

ously compromises the well-being of mine workers and esca-
lates healthcare costs. Preventing lung disease through reduction
of worker exposure to injurious mine dusts is a shared priority
for industry, labor, the Mine Safety and Health Administration
(MSHA), and the National Institute for Occupational Safety and
Health (NIOSH). Development of instruments that can rapidly
measure the level of respirable dust in mining environments is a
critical element for reduction of worker exposure. Such instru-
ments could promptly reveal out-of-compliance dust conditions
and expedite optimization of dust control strategies.

Presently, federal regulations require mine operators to reg-
ularly collect respirable dust samples using approved equip-
ment and send them to an MSHA laboratory for gravimetric
analysis.) There is a substantial time delay between collection
of samples and their analysis, which is performed under con-
trolled laboratory conditions at a location distant to the mine.
Such delays both hamper the timely realization that excessive
dust concentrations exist and impede adjustment of dust control
systems to prevent high concentrations. The availability of a de-
vice for quicker assessment of respirable dust concentrations,
simpler, more convenient, and less expensive than existing real-
time monitors, would greatly help with these difficulties.

In 1992, an MSHA Respirable Dust Task Group recommen-
dation provided new stimulus to investigate practical means of
accomplishing on-site dust monitoring. This task group recom-
mended that research be conducted to develop dust monitors
“that will provide continuous information to the miner and mine
operator on the status of dust resulting from the mining pro-
cess as well as information on the status of compliance with
respect to the applicable respirable dust standard.”®) The work
described in this article addresses this recommendation to find a
rapid on-site means of measuring airborne dust concentrations.

The focus of this research is the increase in differential pres-
sure (AP) that occurs when a mass (M) of dust is collected
on a sampling filter under constant air flow conditions, with
greater dust masses causing greater differential pressure in-
creases. This concept is straightforward and well recorded in the


102

instrument. Generally, R? entries trend lower as we progress
from the left side of the table to the right. R? values are very
high for a specific dust mix sampled at a specific flow rate.
Most of the R? values that are both mixture- and flow-specific
are 0.97 and above. The behavior of R? is not surprising. The
variability in the dusts examined directly affects the variability in
the data collected and their associated statistics. Dusts with less
variability in particle size distribution, composition, and density
will have more consistent filtration properties. Thus, the more
consistent dusts are in these characteristics, the more predictable
their AP response will be and the more accurately they will
be measured by a AP-based instrument. Table II shows some of
the limitations of the AP approach when highly variable dusts
are involved. Applying only one regression equation to such
dusts necessitates error boundaries of 35 percent or 50 percent.

Table III gives evidence for what is likely the best application
of the AP method. Even though there is still a significant degree
of variability in dust composition, limiting the data set to 2 L/min
samples and breaking it down further by individual coals greatly
improves the prospects for accuracy. Table III, whose highest CV
entry is 13.4 percent, suggests that there are individual work sites
where a AP-based instrument might be implemented usefully. It
also suggests that a site-by-site calibration is necessary, unless it
has previously been established that sites have very similar dusts.
The AP approach appears robust for minor dust variations that
may occur at the same work site, but is less reliable for major
variations, or for variations occurring from one work site to
another.

SUMMARY AND CONCLUSIONS

Experiments were conducted to measure the correlation be-
tween the mass of respirable dust collected on 25-mm-diameter
glass fiber filters and the increase in differential pressure that
develops across the filters when drawing at a constant air flow.
‘Test atmospheres were generated inside a laboratory dust cham-
ber using various coal dusts, rock dust, and mixtures of coal
and rock dust. Deposits with different particle size distributions
were collected on the filters by sampling the dust in the chamber
through cyclone preclassifiers at different flow rates.

Results show that the correlation between differential pres-
sure increase (AP) and dust mass (M) is linear and can be ap-
proximated by the equation AP = KM, confirming work of other
researchers. Observations on strong particle size influence and
weaker dust density influence on AP also confirm the literature.

This study’s K values range from 1.14 (R? = 0.81) for the Illi-

nois No. 6 family of dust mixtures to 1.64 (R? = 0.88) for the
Pocahontas No. 4 family of dusts. The overall K value for all the
data summarized in this article is 1.35 (R? = 0.84). These val-
ues describe trends for coal dusts derived from different seams,
mixed with differing amounts of rock dust, and having different
particle size distributions.

Correlations between AP and M for specific dust mixes sam-
Pled at specific flow rates are very high, with R? typically 0.97
and above. While K and R? are influenced by variability in

H, DOBROSKI, JR. ET AL.

dust composition and particle size, these factors will be more
limited for any one work site than for a collection of many work
sites. Therefore, prospects for dust measurement accuracy will
be much improved when calibration is performed for an individ-
ual work site. A simple device for end-of-shift measurements can
currently be constructed from common, commercially available
hardware. A device for automated periodic or continuous dust
concentration readings requires further hardware development.
When a suitable portable instrument is developed, the device
would also likely have its most accurate application when cal-
ibrated for specific dusts or individual work sites. Research to-
ward such a AP-based continuous monitoring device is ongoing
in our laboratory.

DISCLAIMER

Reference to any company name or product does not con-
stitute endorsement by the National Institute for Occupational
Safety and Health.

REFERENCES

1, National Institute for Occupational Safety and Health: Criteria for
a Recommended Standard—Occupational Exposure to Respirable
Coal Mine Dust, pp. 42-47. DHHS (NIOSH) Pub. No. 95-106.
NIOSH, Cincinnati, OH (1995).

2. U.S. Code of Federal Regulations. Title 30, Chapter I, Subchapter
O, Parts 70-74, pp. 404-436. U.S. Government Printing Office,
Washington, DC (1999).

}. Mine Safety and Health Administration: Review of the Program to
Control Respirable Coal Mine Dust in the United States—Report
of the Coal Mine Respirable Dust Task Group. MSHA, Arlington,
VA (1992).

4, Gupta, A.; Novick, V.J.; Biswas, P; et al.: Effect of Humidity and
Particle Hygroscopicity on the Mass Loading Capacity of High
Efficiency Particulate Air (HEPA) Filters. Aerosol Sci Technol
19:94-107 (1993).

. Hunt, C.M.: An Analysis of Roll Filter Operation Based on Panel
Filter Measurements. ASHRAE Trans 78:227-234 (1972).

6. Letourneau, P;; Mulcey, P;; Vendel, J.: Prediction of HEPA Filter
Pressure Drop and Removal Efficiency During Dust Loading. In:
Proceedings of the 20th DOE/NRC Nuclear Air Cleaning Con-
ference, M.W. First, Ed., Boston, MA, vol. 2, pp. 984-993. The
Harvard Air Cleaning Laboratory, Boston, MA (1989).

. Letourneau, P;; Renaudin, V.; Vendel, J.: Effects of the Particle

Penetration Inside the Filter Medium on the HEPA Filter Pressure

Drop. In: Proceedings of the 22nd DOE/NRC Nuclear Air Cleaning

Conference, Denver, CO, M.W. First, Ed., vol. 1, pp. 128143. The

Harvard Air Cleaning Laboratory, Boston, MA (1993).

Novick, V.J.; Higgins, PJ.; Dierkschiede, B.; et al.: Efficiency and

Mass Loading Characteristics of a Typical HEPA Filter Media Ma-

terial. In: Proceedings of the 21st DOE/NRC Nuclear Air Cleaning

Conference, M.W. First, Ed., San Diego, CA, vol. 2, pp. 782-798.

The Harvard Air Cleaning Laboratory, Boston, MA (1991).

Novick, VJ.; Klassen, J; Monson, PR. et al.: Predicting

Mass Loading as a Function of Pressure Difference Across

Prefilter/HEPA Filter Systems. In: Proceedings of the 22nd

DOE/NRC Nuclear Air Cleaning Conference, M.W. First, Ed.,

Denver, CO, vol. 2, pp. 554-573. The Harvard Air Cleaning Lab-

oratory, Boston, MA (1993).

2

a

2

cd

2


DIFFERENTIAL PRESSURE RESPONSE OF GLASS FIBER FILTERS 97

literature,“~TM) but interest has been in areas other than the ef-
fects of respirable mine dusts. The data collected in this study are
derived from laboratory experiments using prepared coal dusts,
limestone dust (commonly called rock dust), and coal-rock dust
mixtures. The results provide a fundamental understanding of
the performance of the AP method for estimating coal and rock
dust masses on collection filters, clarifying whether the method
could be used currently in some applications, and clarifying the
method’s potential with further instrument development.

This current research refines and expands substantially on an
earlier study“ of filter differential pressure, maintaining a focus
on mine-related dusts (but relevant to all dusts). The earlier work
by the authors used a limited variety of dusts over a limited range
of dust deposit masses, investing substantial effort investigating
alternative filter compositions. The primary conclusions of the
earlier work were that glass fiber filters were the most useful of
the compositions tried and that good correlations between dust
mass and differential pressure increase were achievable. This
article presents a new and more extensive study with a more
advantageous filter material.

METHOD AND MATERIALS

Experimental Setup and Procedures
All experiments were conducted under laboratory conditions
using the following general procedures:

1. 25-mm-diameter glass fiber filters were weighed, mounted
on cellulose support pads, and then sealed in cassettes.

2. The initial differential pressure across each cassette was
measured on a laboratory differential pressure apparatus
at a 2 L/min flow rate prior to collecting any dust.

3. Each cassette was inserted in a sampling train between
a 10-mm-diameter nylon cyclone and a constant air flow
pump. Completed assemblies were then installed inside a
0.1-m? aerosol test chamber.

4, A dust mixture was selected and injected into the test
chamber using a mechanical dust mill and dry pressur-
ized air from a high-volume laboratory-scale compres-
sor. The resulting dust-laden atmosphere inside the cham-
ber was < 10 percent RH, necessarily dry to prevent the
dust mill from clogging. To verify that the dust mill was
functioning properly, a RAM-1 real-time aerosol moni-
tor (manufactured by MIE, Inc., Billerica, MA) was used
as a.rough indicator of the dust concentration being
generated,

5. Dust from the test chamber was sampled through the cy-
clones onto the filters at flow rates of 1, 2, and 3 L/min for
various lengths of time.

6. The dust-laden cassettes were removed from the test cham-
ber, then the differential pressure was again measured
on the laboratory differential pressure apparatus at a 2
Limin fiow rate. Subtracting initial from final differen-

tial pressure yielded the differential pressure increase
(AP).

. The filters were removed from the cassettes and reweighed.
The filters were not desiccated, but were equilibrated to
ambient laboratory conditions.

. Data tables were generated correlating differential pres-
sure increase with dust mass for each filter, A commer-
cially available software program was used to plot each
regression equation and calculate the coefficient of deter-
mination (R*) and other statistics.

~

~

Filters and Cassettes

The filters used (manufactured by Pallflex Products Co.,
Putnam, CT) were of glass fiber composition with polytetrafluo-
roethylene (PTFE) binder. These. high-efficiency 25-mm-
diameter filters were capable of retaining high dust mass load-
ings and were selected because they performed well in small
pilot tests of a variety of filters. Each filter, with cellulose sup-
port pad, was installed in a three-piece clear polystyrene cas-
sette with a 2-in extension (manufactured by Envirometrics,
Charleston, SC).

Glass fiber filters with PTFE binder produced the most accu-
rate gravimetric measurements among various glass fiber com-
positions. The 100-percent glass fiber filters used in the earlier
study) were prone to minor weight loss, possibly from fiber
breakage. Glass fiber filters with non-PTFE binder tended to
adhere to plastic cassette surfaces and tear, also causing minor
weight loss.

Laboratory Differential Pressure Apparatus

Figure 1 shows the laboratory apparatus, constructed of
polyvinyl chloride tubing, used to measure the differential pres-
sure across filters. Each filter being tested was connected in
parallel with two instruments. The first was a Magnehelic direct-
reading mechanical pressure gauge (manufactured by Dwyer In-
struments, Inc., Michigan City, IN) having a magnetic linkage to
apointer on a scale. It had a resolution of approximately 0.25 cm
water. The second device was a variable-capacitance pressure
transducer (manufactured by Setra Systems, Inc., Boxborough,
MA) with steel diaphragm and insulated electrode. This instru-
ment had a voltage output that required a conversion to differen-
tial pressure with a resulting resolution of approximately 0.025
cm water. The two gauges were used for redundancy to reduce
the chances of operator error when making measurements. Only
the data from the second instrument was used in the tables and
figures of this article.

A shunt valve (V1) was opened whenever the vacuum pump
was turned on or off to eliminate pneumatic shock to the instru
ments and to avoid dust dislodgement from the filters. Otherwise,
the valve was kept closed. A small bench-top dry-air generator
provided air at < 3 percent RH, while the vacuum pump and
critical orifice maintained the air flow at 2 L/min. Flow rates
were confirmed with a Gilibrator soap-film air flow calibrator
(manufactured by Sensidyne, Inc., Clearwater, FL).


98 H. DOBROSKI, JR. ET AL.

4

AP
instruments
Vi
Critical
orifice
>> f 2
Dry 2 L/min Filter ] Vacuum pump
air source flow under test
Relative humidity
monitor
FIGURE 1

The laboratory constant air flow apparatus for measuring the differential pressure of filters.

Cyclone Performance at Different Air Flow Rates

The cyclones used in sampling from the test chamber were
the Dorr-Oliver 10-mm-diameter nylon type used by the mining
industry for collecting gravimetric dust samples to assess
compliance with dust regulations. In these tests, the cyclones
were used at 1, 2, and 3 L/min. Multiple samples were collected
simultaneously at these three flow rates from each dust mix dis-
persed in the test chamber. Sampling flow rate and duration were
the only differences in collection procedure between samples of
a given dust mix.

The primary effect of different flow rates on cyclone perfor-
mance isa shift in the 50-percent aerodynamic diameter cut size
of the sampling efficiency curve.“ If the flow rate is increased,
the 50-percent cut size will decrease. For any specific particle
size, the percentage penetrating through the cyclone will de-
crease as flow rate increases, reflecting the general increase in
collection efficiency across all particle sizes. Conversely, de-
creasing flow rate will reverse these effects.

Thus, the cyclones at the different flow rates produced three
different particle size distributions from the atmosphere gen-
erated within the dust chamber and deposited samples of the
different distributions on different filters. Note that it cannot be
assumed that the proportion of coal to rock dust collected on
the filters was precisely the same as that in the atmosphere of
the dust chamber. This is because the initial particle size distri-
butions of the rock dust and the individual coal dusts are likely
to be dissimilar, and a cyclone would act on the two dust frac-
tions differently. However, because coal dust is extremely dark
in color and limestone is extremely light, the shading of the fil-
ter samples served to confirm that their compositions roughly
represented those of the bulk dust mixes used.

Dust Types

Dusts prepared from Black Creek, Illinois No. 6, Pittsburgh,
Pocahontas No. 4, and Upper Freeport bituminous coal seams
were used in this research. These dusts were ground and screened
to —325 x 0 mesh. For tests not using pure unmixed dusts, coal-
rock dust mixtures were created to achieve either 25-percent or
50-percent rock dust, by weight. Bituminous coals are record-
ed) as having a density of 1.35 + 0.15 g/cm’, and limestone
as having a density of 2.50 + 0.40 g/cm’, almost double that of
the coals.

TEST RESULTS

In agreement with the literature, research results indicate that
the relationship between the increase in filter differential pres-
sure and collected dust mass is linear and can be approximated
by the equation:

AP =KM (]
where:

AP = differential pressure increase (cm water),
M = dust mass (mg), and
K = slope of the regression line.

Each regression line in this research may be regarded as an in-
dication of continuous AP versus M filter response in that it is
derived from a series of discrete measurements over continuous
ranges for both AP and M. Each data point in the figures rep-
resents the AP for a single filter, corresponding to the collected
dust mass M of that filter.

Figure 2, presenting a series of regression lines, compares the
different dust family AP responses. Figure 3 shows the complete