Computer simulation of coal preparation plants

Chemical

Compuen ami Enginm’ng, Vol. 2, pp. 994l @ Pergamon Press Ltd.. 1078. Printed in Great Britain

COMPUTER SIMULATION OF COAL PREPARATION PLANTS BY~KINS. Go’rrPRl~ and h3PHTHAHABARA Department of IndustrialEngineering,System ManagementEngineeringand OperationsResearch,University of Pittsburgh,Pittsburgh,PA 15261,U.S.A. (Received21 February198) Ah&act-Coal preparationis a procedurefor removingphysical impurities,such as ash and pyritic sulfur, from freshly mined coal. This paper describes the development of a computer simulationmodel for predictingthe performanceof coal preparationplants.The model allows the user to interconnectindividualplant componentsinto whatever configurationmay be of interest.A provisionfor recycle streamsis also included.The paperpresents a discussion of the computationalmethods used for each of the unit operations,and a descriptionof the executive routine. Several examples are presentedwhich illustratethe use of the simulatorto verify theoreticalpredictions and to predictthe performanceof a realisticplant conIiguration. Scnpe-This paperdiscusses the underlyingstructureof a computerprogramthat was writtenfor the U.S. Bureau of Mines(Departmentof Energy)to simulatethe performanceof coal preparationplants.The paperbeginswith an introductorydiscussion of the role of computer simulation in coal preparation.The principal unit operations (washing, crushing,screening, etc.) are then described followed by a descriptionof the executive routine. The emphasis is placed upon general concepts rather than computationaldetails. Finally, some applicationsof the programare described,includingboth simple circuitsand realisticplant configurations. The Bureauof Mines/DOEprogramis writtenin standardFORTRANIV and is availableto the generalpublic.A listingof the program,andinstructionsfor its use, can be obtainedfromthe U.S.Departmentoe EnergyLaboratories, Pittsburgh(Bruceton)PA. simulationmodel describedhereinis a flexibleand convenienttool for analyzing concIMimM and -The the behaviorof coal preparationplants. It can be used for a varietyof purposes,such as comparingdiierent modes of operationof a given plant, comparingone plant configurationwith another,assessing the washing potentialof one coal versus another,and assessing the impactof variousenvironmentalrestrictionson the recovery (yield) of clean coal. Policy makers as well as plant managersshould Iindthe simulatoruseful in assessing various types of alternatives.

Coal preparation, commonly known as coal washing,

offers a practical and economical approach to the removal of ash and pyritic sulfur from raw coal. For example, the ash in a typical U.S. Northern Appalachian bituminous coal can be reduced from 14 to 6% and the total sulfur from 3 to l-1/2% by coal preparation [Il. Such beneficiation can be obtained at a yield ranging from 60 to 90% and at a cost of about $2.!JO-$4.83per ton of clean coal 121. The process is becoming increasingly important as the environmental restrictions associated with coal utilization become more stringent. A typical coal preparation plant consists primarily of various interconnected washers, crushers and screens. Several types of washers are normally present since each piece of washing equipment is best suited for a particular size fraction of coal. The crushers break the coal into the proper sizes, and the screens separate the crushed coal into appropriate size fractions. These various units are interconnected in a number of ways, the choice being dependent upon the characteristics of the coal feed and the desired product purity. A typical coal preparation plant configuration is shown in Fig. 1. The principal consideration in operating a coal preparation plant is to maximize the yield of clean coal while reducing the impurities to an acceptably low level.

*Authorto whom correspondenceshould be addressed.

In addition, in assessing the possible intercomection of units in an existing plant, or when a new plant is being considered, it is essential that the optimum configuration be determined for a given degree of cleaning. Usually these matters are handled by some prudent combination of experience and experimentation. A computerized process simulator can, however, be an effective aid in comparing alternatives, provided the program is sticiently comprehensive to simulate conditions found in industrial practice. This paper describes the principal features of one such simulator, which was developed under the sponsorship of the U.S. Bureau of Mines (Department of Em+). General concepts are presented rather than computational details, in order to provide guidance to others who might wish to pursue a similar undertaking. Moreover, some of the basic ideas used in this simulator appear to have potential applicability to the simulation of other solid-solid separation processes. Some applications of the Bureau of Mines simulator are also discussed. UNIT~TIONS To analyze realistic plant cord&rations, a computer program must be able to simulate the performance of all principal plant components under specified operating conditions. The manner in which these calculations are carried out is summarized below.

B. S.

100

&X-WtUED

and J. AnMu

Feed Clean

Refuse 1: Rotary

breaker,2: Wet screen(Upper),3: Wet screenCower), 4: Blender,5: Baumjig. 6: Multipleroll

crusher,

7:

Blender,8: Wet screen,9: CooceotratiqrTable, IO:Froth Botatioocell. Fig. 1. Washers Coal washing equipment makes use of a float-and-sink principle, based upon the specific gravity differences between the coal and its associated impurities. (An exception is the froth flotation process, in which the separation is dependent upon the differences in surface characteristics of the coal and its associated impurities.) The performance of these float-and-sink units is characterized by a distribution curve (also called a partition or separation curve). Of the material in the feed having a given speci6c gravity, fi this curve indicates the percentage reporting to the clean coal product [31. A typical distriiution curve is shown in Fii. 2. Notice the value of specific gravity corresponding to an ordinate value of 50%. This value is defined as the specific gravity of separation, p,; that is, the specific gravity of material in the feed that is divided equally between clean coal and refuse. By making appropriate physical adjustments on a coal washing unit, the value of the specific gravity of separation can be increased or decreased, shifting the distribution curve accordiiy. The specific gravity of separation can therefore be

considered a control variable. An increase in the specific gravity of separation results in an increase in the yield of clean coal but a decrease in its quality. Recently a method has been developed for representing the distribution curve in a manner that is independent of the specific gravity of separation 14.51. To do so, the percent of feed reporting to clean coal is plotted against reduced specific gravity, x. The reduced specitic gravity is defined as the ratio of specific gravity to specific gravity of separation; that is, x = p/p,. Such a curve is called a generalized distribution curve. A typical generalized distribution curve is shown in Fig. 3. The use of generalii distribution data greatly simplifies the task of representingwashability data within a simulator. Float-and-&k calculations depend not only on the specific gravity of material in the feed, but also on its size composition (called the size consist). Consequently, a diRerent generalized distribution curve is obtained for each of several size increments of the feed material. To simulate the performance of a washer, then, it is necessary to consider the feed as divided into seve!ral size increments, and each size increment as subdivided into

2

z

_----

E

2 ”

50

s ._ 2 3 i! 2 iz !

1.4

16

1.8

gravity,

Fig. 2.

I

L

p

f

0 0.8

PI

Specific

2

---

09

Reduced

1.0

specific

Fii. 3.

I.1

gravity.

I2

I.3

x

Computersimulation of coal preparaton plants

101

tion function, S,(B) (the fraction of feed particles in the itb size increment that will be crushed at a given crusher Setting, 8). and a breakage function for crushed particles, B&3) (the fraction of crushed particles or&ally in the itb size increment that reaches the kth size increment). In this model it is assumed that both S@) and B&3) are dependent upon initial particle size but independent of specific gravity. Furthermore, the breakage of a particle is assumed to create fragments that are unchanged in their specific gravity analysis. The flowrate of crushed product in the kth size fracwhere Fc represents the overall clean coal flowrate, FR tion, K(p), is expressed as represents the refuse flowrate, Fiirepresents the flowrate of feed in the ith size fraction and the jth specific gravity fraction, and f&) is the distribution factor for the ith size fraction and jth specific gravity fraction. Note that the values for the distribution factor, and hence the product flowrates, depend upon the value where Fij represents the flowrate of feed in the itb size speci&z.dfor the control parameter, p, (that is the overall fraction and jth specific gravity fraction. Note that the specific gravity of separation). product flowrates depend upon the value specified for The overall ash, sulfur and energy content of the the control parameter, /3 (i.e. the crusher setting). product streams are easily determined once the weight The explicit forms of S&3) and &k(B) depend upon distributions have been established. These calculations the type of crusher to be simulated and the magnitude of are based upon the assumption that within each size and the control parameter. In general, however, these specific gravity fraction, the concentration of ash and functions are represented by exponential-type equations sulfur and the energy content of the coal are unchanged that have been fitted to actual plant performance data. by cleaning. The simulator contains specific equations for each of The simulator contains generalized distribution data, in the following types of crushers: 1. Single roll crusher; 2. tabular form, for each of the following commonly used Multiple roll crusher; 3. Gyratoryljaw crusher; 4. Cage coal washing units: 1. Baum jig; 2. Dense-medium vessel; mill crusher. For each device a distinction is made be3. Dense-medium cyclone; 4. Hydrocyclone; 5. Concen- tween a primary and a secondary crusher, the caltrating table. In each case the basic distribution data culations being carried out somewhat differently in each were obtained from U.S. Bureau of Mines reports [6-101. case. In addition, there is a provision for simulating a Within the computer model a four-point Lagrangian in- rotary breaker, as discussed separately after the disterpolation routine is used in conjunction with the tabu- cussion of screens. lar generalized distribution data. The actual distribution data (in generalized form) are tabulated in a recent U.S. Screens Bureau of Mines report 141. Gottfried[S] has recently investigated the use statistiThe computation of screen performance is similar to cal distribution functions (viz., Weibull functions) to the computation of washer performance. A selection represent the generalized distribution data. These function, $(a), is again required; the selection function functions were found to be slightly less accurate (though now represents the fraction of feed particles in the itb size increment that passes over the screen into the much more convenient) than the tabular distribution data included in the simulator. Reference [S] also includes a overflow (coarse) product, given a screen opening size u. description of the method used to divide the coal feed The flowrate of overflow material, Focanthen be written into various size increments, and later reconstitute the as overall clean coal and refuse streams from these individual size increments. The simulator also contains a provision for the froth flotation process, even though the method used to carry out the computation is less precise than that for the coal and the flowrate of underflow (fine) material, F., canbe washing units listed above. In this case the overall yield expressed as of clean coal and the overall ash concentration are determined from the properties of the feed, using an approximate rule of thumb. A detailed analysis of the clean coal and refuse products are then obtained by fitting a float-and-sink-type distribution curve to the where Fuagain represents the flowrate of feed in the ith overall yield and ash concentration. The sulfur and size fraction and the jth specific gravity fraction. Note energy content of the products are then obtained from that the overtlow and undertlow rates depend upon the the distribution curve calculations. value specilkd for the control parameter, u (i.e. the size of the screen opening). Crushers The explicit form of $(u) depends upon the type of crushers are somewhat more dillicult to model than screen sekcted (both primary and secondary screens can washers, because it is necessary to account for both the be simulated, in either a dry or a wet operating mode). In size distribution of the original (parent) particks and the general, the selection functions are determine-d by size distribution of the CNshed (daughter) particles. To exponeritial type equations that represent actual screen carry out the calculations the simulator employs a selec- performance data.

specific gravity fractions. The washer’s effect on each size and specific gravity fraction is determined, and then the overall clean coal and refuse products are reconstituted. Mathematically, this can be expressed as

CACE Vd. 2, No. 2/3-D

102

B. S. Gorrmrso and J. ABNU

Rotary breaker The methods used to simulate crusher and screen performance are combined in the rotary breaker model. . Essentially, the rotary breaker is considered to be a sequence of consecutive events where breakage and screening alternate, as feed material undergoes successive falls in a rotating, perforated drum. The model includes its own breakage distribution function, selection-for-breakage function, and screen selection function. A ‘work-hardening’ factor is also included for the case where initial particle flaws are activated primarily in the first few falls. Thus the selection-forbreakage function decreases with subsequent falls because of its depencence on the work-hardening factor. The rotary breaker routine requires three input parameters-drum length, drum diameter, and size of openings in the drum (screen size). The height of each fall and the number of falls (required control parameters) are then determined from the breder’s physical dimensions. Blender

A ‘blender’ is simply a point within the plant where two different flow streams are combined. The flowrate of blended product can be expressed as

ExRcuTlvEBouTINE

An important aspect of any process simulation program is the ability to interconnect the various units into whatever plant conliguration may be of interest. This can be accomplished by specifying an origin and a destination for each tlowstream, thus permitting the output from any unit to be directed to any other unit within the plant. The bookkeeping required by this feature is logically contained in the executive routine (which also reads input data, calls appropriate subroutines for the various unit operations, controls the generation of output data, etc.). Recycle streams A provision for recycle streams can easily be included in the executive routine, using an iterative procedure such as the method of successive substitutions. The simulator discussed herein allows for as many as three recycle streams. The value of this feature is questionable, however, since coal preparation pfants rarely, if ever, utilize recycle streams. Moreover, the recycle calculations converge rather slowly, resulting in a substantial increase in total running time when recycle is present.

Storage requirements One undesirable characteristic of coal preparation analysis is the need to store a large amount of information for every flowstream. Specifically, for each where P represents the Aowrate of the product stream, size fraction and each specitic gravity increment, a value and I$,, and fijl represent the flowrates of the first and must be stored for the weight distribution, ash content, second feed streams in the ith size fraction and the jth pyritic sulfur content and total sulfur content. Thus, if specific gravity fraction. Similarly, any attribute in the every flowstream is characterized by 16 size fractions and 10 specific gravity increments, a total of 640 data blended product can be determined as values must be stored within the computer. This translates into 16K of storage for a plant having 25 flowstreams (which is not unrealistic). Moreover, the storage problem is further aggravated if other attributes (e.g. where A represents the attribute (e.g. ash, sulfur or energy content, moisture content, etc.) are considered. energy content) in the blended stream, and Aill and A,j2 (The simulator described herein calculates energy content represent the attributes of the first and second feed as a function of per cent ash, and it does not consider streams in the itb size fraction and the jth specilic moisture content.) Therefore the available amount of gravity fraction. Notice that the blender calculations do computer memory can limit the use of the simulator. However, many problems do not require 16 siie fracnot require the specification of a control parameter. tions and 10 specific gravity fractions. Thus, the use of a programming language having a dynamic storage Splitter capability (e.g. PLIl) would be a decided advantage. A ‘splitter’ is a point within the plant where a portion of a flow stream is diverted. The net effect is to create two UsEOFTRRSJMULATOR flow streams from one flow stream, where the combined flowrates of the two new streams equal the flowrate of the A number of very practical questions can be answered original stream. The composition of the new streams will through the use of a coal preparation plant simulator. be the same as the original stream. Thus Spec&ally, the simulator allows one to compare the performance of diierent plant configurations, to deter(8) mine the effectiveness of various modes of operation with a given con@uration, to determine the degree of cleaning obtained with different coal feeds, and to assess the impact of various environmental restrictions. The following information must be provided as input data: type of each unit and associated control where Ej represents the flowrate of the feed stream in parameters, origin and destination of each flowstream, the ith size fraction and the jth specific gravity fraction, and detailed analysis of the feed stream (diitributions of PI and K represent the product streams, and Q is a ’ weight, ash and sulfur with respect to size fraction and control parameter, 0 < a C 1. specific gravity). The minimum required output consists Additional details concerning all of the unit operations of a summary of the input data, the yield and energy can be obtained from the FORTRAN listing of the U.S. recovery of each unit, and the gowrate, composition and Bureau of Mines @epartment of Energy) simuIator[ll]. energy content of each flowstream. More detailed in-

103

Computersimulationof coal preparationplants

formation for each unit and each flowstreamcan also be generated as an option. The U.S. Bureau of Mines simulatorhas been used to’ analyze a wide variety of coal preparation circuits, ranging from single units to realistic plant conligurationsas shown in Fii. 1. Some representative situations,and the results obtained therefrom, are described below.

2. If two or more ideal washers are operatingin series, there is no benefit in passing the product from the first washer through other washers. The applicability of the theory to real washers has been investigatedthrough the use of the simulator. For example, three sets of calculationswere carried out for two dense~mediumcyclones in parallel,as shown in Fii. 5. The feed materialhad an overall ash content of 13.2%. Simple circuits Half of the feed was treated in each vessel. The separaSimulationsof individualunits (i.e. washers, crushers tions were computed at various specific gravities of and screens) were very useful when debuggingand vali- separation. The overall cumulative ash of the blended dating the simulator. From an applications viewpoint clean coal product was the same for each set of calthese simulationsare of little interest, however, and will culations(5.67%)but the yields were different,depending therefore not be discussedherein. of greater interest are on the values chosen for the specilicgravitiesof separasimple circuits involvingtwo or three units in series or tion. The resulting yields are summarked in Table 1. parallel. Several such simulationsare discussed below. Notice that the maximum yield (87.6%)was obtained Cierpisz & Gottfried[l21 have recently presented a when the specilk gravities of separation were equal theoretical study of the optimum performance of ideal (P, I = pa2 = 1.50),as the theory of perfect washers would washers.(An ideal washer is a washer whose distribution suggest.Similarresults were obtained with other vessels curve is a step function, as illustrated in Fii. 4.) The and other coal feeds. In another study, 3 sets of calculationswere carried follo*g two results, taken from that paper, are partiout for two dense-mediumcyclones in series, with the cularly signiticant. 1. If two or more ideal washers are operating in clean coal from the first vessel fed to the second vessel, parallel,with each coal feed havingthe same distribution as illustratedin Fii. 6. The feed materialenteringthe fkst of attributes (i.e. ash, sulfur, etc.), then the yield of clean vessel again had an overall ash content of 13.2%.Table the results that were obtained with three coal will be maximizedby washingeach feed stream at 2 SUIIIIIW&S different combinations of separation gravities. The the same specificgravity of separation. Unit I - DMC

Feed 13.2% ash

Unit 2 DMC

4 Specific

gravity.

I Refuse

p

Fia.5.

Fii. 4.

Table 1. Performance summaryfor two dense-mediumcyclones ia parallel specilic gravity

Run

of separation

a 3

Clean coal yield (bknded product)

Cumulativeash (bknded product)

unit 1

unit 2

%

%

1.50 1.425 1.39

1.50 1.60 1.70

ix 85:7

5.61 5.67

Table2 Performance summary for two dense-mediumcyclones in series Run

1 2 3

spedic &zavity of separation unit1

unit2

1.50 1.55 1.60

1.60 1.55 1.50

overau ckan coal ykld % unit 1 Units182 87.8 88.3 87.8

Cumuiiveasb

46 unit 1 5.7 5.8 6.0

Units1&2 5.1 5.8 5.7

104

B. S. GOTTFRIED and J. AEIARA

results indicate that there is very little benefit in utilizing the second washer, at least for relatively efficientvessels such as dense-medium cyclones. Thus the simulated results are consistentwith the predictionsbased upon the theory of perfect washers. In a similar study, 3 sets of calculationswere carried

out for washers in series, as illustrated in Fig. 6. Now, however, the first washer was a hydrocyclone and the second a concentrating table-both relatively inefficient vessels. The results obtained with a feed whose ,overall ash content was 16.M are summarizedin Table 3. In this case the benefit of a second vessel in loweringthe

Table3. Performance summary for a hydrocyclone and a concentrating table in series Specific gravity of separation Run

unit 1

Unit 2

1 2 3

1.50 1.55 1.60

1.60 1.55 1.50

Overall clean coal yield % Unit 1 Units 1 & 2 12.2 14.7 16.9

:*; 68:2

Cumulative ash % Unit 1 units 1 & 2 6.4 6.8 7.4

4.5 4.5 4.5

Table 4. Coal preparation plant simulator input data

UN,,

TYPE

DECISION 2O.OOO

VARIABLES 6.000

,R”TAVV

P3

IWEI

UPPFR

SCREEN,

1.500

24

IrlET

LOWER

SCREEN,

.500

4,

ISTREA~

6

HRFAKFQ)

12.000

I,

RLENOER,

(Z-STAGE

RA”M

16

ISECONDAUV

4,

ISTREAW

23

Itit1

1.620

.,I‘)

MULTIPLE

CRUSHERI

ROLL

.275

FILENOFR)

UPPEQ

SCREEN)

7

IFHOW

1

~CONCENTPATIYG

.023

FLOTATION) TABLE)

1.580

- - __FLOWSTREAM

ORIGIN

NUMBEQ (FEED1

2

UNIT

DESTINATION

NWRER

(WEFUSE

-

1

R

0

1

c

2

4

2

L

3

5

2

U

4

6

3

L

7

7

3

U

4

PRODUCT)

4

8 ICLEAN

COAL

PROOUCTl

c

0

5

e

0

11

5

M

6

12

6

7

13

7

8

,PEFKF

P(rODUCTI

14

6

L

9

15

8

”

10

9

R

0

9

c

0

IO

R

0

IO

c

0

16

(REFUSE

17

(CLEAN

16

,9LFUSF

19

(CLEAN

10

PkODUCT, COAL

PUODUCT,

PYODUCT) COAL

YFLO”S=

PQM)UCTl

19

NSIZL=

8

NUMAER

5

5

10

UN,7

1

3

9

NUNITS=

-

0

I

NGRA”=

8

lOlIT=

2

ICYAX=

50

NCOMP=

0

Computersimulationof coal preparation plants

10s

ash is readily apparent. This is not surprising, since the theory of perfect washers is not applicable to

cumulative

Fig. 1, can easily be studied with the simulator. For example, the configuration shown in Fig. 1 has been simulated for the conditions shown in Table 4 and the

Refuse

Refuse

Fig.6.

Table5. Speci6cgravityanalysisof feed SPEClf

FRPCTIOhl AND WEIGHT

SIZE

IC

GRAVITY

VEIGHT ASti

DIRECT. PTRITIC

18 RI

12 RI

12

7.1

6

27.1

PERCENT

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60 l.bO-1.70 1.70-l.RO si~u 1.80

PERCENT

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60 1.60-1.70

1.70-1.80 SlNU 1.80

6 RI

2

32.2

2 LIY 112

13.1

PERCENT

PERCENT

AI

s

13.0

PERCENT

aV 28

5.6

3.2

100

FLOPT 1.30 1.30-1.3s 1.35-1.40 1.40-1.50 1.50-1.60

PERCENT

aI

colrPosl

325

2.6

TE

100.0

FLOWSTREAM

fLO,,RATE

PERCENT

PEQCFNT

SUMMARY

=

100.0

RT”

CONTENT

=

SO2

CONTENT

=

PFOCENT RW,,. ?0.03

OF

FEED

Las

SO~/MILLION

UTU

.I

48.1

4.5 2.2 1.6 35.3 33.5 23.1 7.5 6.9 3.2

48.0 19.5 5.H 4.9 2.3 ::: 17.0

.lB

14307. 14001. 13202. 11961. 10261. 8833. 7031. 2000.

16.7 31.0 36.3 41.6 45.7 47.0 49.0 100.0

.80

14324. 14052. 13474. 12199. 10567. 9445. 7830. 2000.

.77 -99 1;40 1.95 2.49 3.97

.RO

1.22 1.65 2.08 2.47 3.78

.21 .39 .43 .60 .64 .73 .80 9.81

.I9 1.07 1.10 1.23 1.26 1.35 1.42 10.52

14324. 14227. l4l7a. 13667. 13525. 13364. 13773. 7513.

8.0 9.1 10.0 47.2

.la .38 -48 ;61 .73 .84 .97 9.14

.80 .99 1.09 1.22 1.33 1.44 1.56 10.01

14307. 14166. 14025. 13762. 13448. 13245. 13093. 7441.

21.4 33.2 35.4 47.6 49.5 51.2 51.9 100.0

2.8 :*:, 616 7.3 8.1 a.6 45.1

::: .37 .‘I6 .a3 .94 1.00 10.20

.80 .99 1.04 1.41 1.4A 1.59 1.65 10.95

14324. 14227. 141RO. 13674. 13555. 13418. 13342. 78.97,

14392. 14001. 1316B. 12063. 10397. 9003. 7609. 2000.

22.0 42.0 4R.9 56.4 60.9 63.1 64.7 100.0

::: 4.4 5.9 7.4 8.3 9.2 35.1

.14 .31 .40 .53 .b5 .75 .86 6.75

.77 .a7 .95 1.08 1.19 1.2R 1.40 7.97

14392. 14206. 14059. 13794. 13542. 13384. 13241. 9276.

.64 .69 .74 .79 .84 .90 5.62

14392. 14226. 14099. 13908. 13757. 13646. 13531. 10941.

.68 .77 .02 .90 .95 1 .OO 1.08 4.58

14494. 14371. 14287. 14154. 14054. 13969. 13886. 11869.

.73 .82 .89 .98 .1.04 1.09 1.13 3.60

14494. 14357. 14254. 14150. 14015. 14OPB. 13994. 12~8~.

6;i

1.32 1.85 2.49 3.25 4.66 6.30 20.98

2.4 4.7 9.6 16.1 2S.Y 34.1 42.3 H2.b

.14 .49 .95 1.41 2.06 3.51 5.56 17.54

20.02

.08 .11 .45 .65 1.48 2.79 3.87 21.09

.69 .92 1.17 1.86 3.00 4.04 21.09

14392. 13984. 13151. 12131. 10431. 9054. 7762. 2000.

33.5 56.6 b4.1 71.0 74.2 7b.0 77.5 100.0

2.4 3.4 4.1 5.2 6.1 6.8 7.5 24.7

.0.3 .09 .13 .16 .24 .30 .37 5.03

14494. 14069. 13304. 12182. 10652. Yl39. 7711. 2000.

48.0 67.5 73.3 7A.2

1.8 2.5

.20 .28

81.9 83.0 100.0

::: 4.4

.41 .34 .46 .51 4.06 .59

2.4 4.a

9.7 15.7 25.7 33.6 41.4 84.2

6.04

.b4

1.8 4.3 R.8 15.4 24.4 33.3 41.7 R2.8

2.20 3.45 6.20 21.02

.60 .9a 1.50 2.02 2.67 4.06 6.70 21.72

.lb .73 1.02 2.05 3.27 4.51 6.44 20.43

.73 1.24 1.49 2.63 3.73 5.17 7.22 21.41

14494. 13712. 13389. 12165. 10924. 9972. 8969. 3291.

59.0 71.5 80.0 84.2 ‘06.2 87.2 07.8 100.0 36.4

.20 .4R 1.00 1.50

.lO .2a .41 1.14 2.38 4.*09 7.44 25.60

.64 .84 1.01 1.66 2.98 4.32 8.06 26.50

14596. 14341. 14086. 12930. 11808. 10703. 9326. 3359.

FLOAT 1.30 1.30-1.3s 1.35-1.40 1.40-1.50 1.50-1.60 1.60-1.70 1.70-1.80 SlNr( 1.80

25.0 15.7 5;o 0.4 3.0

2.5 4.6 R.7 15.6 25.6 33.1 42.4 R3.5

.I4 .49 .a8 1.54 2.09 3.47 5.46 18.91

.74 1.09 1.42 2.11 2.56 4.00 5.95 20.02

143.93. 14024. 13323. 12151. 10452. 9177. 7597. 2026.

PERCENT

2.9 3.7 4.6

.13 .64 1.25 1.88 2.68 4.01 5.55 20.13

1.2 2.7 4.2 11.0 17.6 24.1 32.2 67.3

3R.4

:*: 6:7 7.5 8.4 9.0 47.6

::: 15.3 24.9 31.5 41.0 84.6

2.8

12::

::: 40.2

2.8

6.56 18.14

36.4 24.0 15.6 5.6 2.3 1.0 .s 14.7

=

59.0 12.5 6.5 4.2 2.0 1.0

19.7 30.7 32.8 44.1 46.2 48.1 48.9 100.0

6.07 17.00

:*z

?2:5

14324. 14052. 13474. 12182. 10550. 9445. ltl30. 2000.

r79 1.57 1.59 1.60 1.84 3.50 6.13 19.23

.62 1.05 1.52 1.97 3.23

9.4 16.7 26.7 35.1 45.7 w.9

,::i

.21 .7l .9R 1.10 I.44 2.90 5.46 18.43

2.9 4.7

RTU/LB

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60 1.60-1.70 1.70-1.80 SINK 1.80

ASH

RTWL”

2.8 4.4 7.0 15.4 25.0 31.5 41.0 84.6

CuHULATlVEI PERCENT WEIGHT mi PYPlTIf : TOTAL SIJLFUQ SULFUR

SULFUR

1.8 6.4 R.3 15.5 22.8 2R.4 34.3 67.7

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60

1.60-1.70 1.70-1.80 SIW 1.60 100

4.1 2.1

22.0 19.9 6.9 7.5

1.60-1.70 1.70-1.80 SINK 1.80

.re av

5;3

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60

PERCENT

16.7 14.3 5.3

21.4 11.8 2.2 12.1 1.9 1.7

1.60-1.70 1.70-1.80 SINK 1.80

a

1.9 .a 51.1

FLOAT 1.30 1.30-1.35 1.35-1.40 1.40-1.50 1.50-1.60 1.60-1.70 1.70-1.00 SINK I.80

1.50-1.60 1.60-1.70 1.70-1.80 SINK 1.80 l/2

19.7 11.0 2.1 11.3 2.1

awca

PERCENT TOTAL

SULFUR

PTRlTtC

S&FUR

*

8.06

PERCENT

li0;5

la:5 x 1.8 :*z 3:e 4.3 4.5 4.7 12.4

.bb

.lb .26 .3* .43 .49. .54 .5a 3.00

1.2

.10

:*i 219

.17 .22 .28

:*: 3:7 13.0

.34 .39 .43 4.12

2.5 3.3

.14 .28 .34

a:7 x

.53 .61 .70 8.06 .78

b0.3

75.9 111.5 83.8 84.8 es;3 100.0 25.0 40.6 45.6

54.0 57.0 58.d 59.8 100.0

3e:4 :*:

TOTAL

SULFUR

.

=

.64 .72 .78 .a4 b90 .94 .98 4.73

14596. 14495. 14411. 14309. 14241. 14199. 14170. 12583.

.74 .88 .94 1.12 1.19 1.28 1.36 8.86

14383. 14244. 14144. 13R34. 13657. 1351.3. 13413. w40.

8.86

PERCENT

B. S. GOITFRIED and J. ASARA

106

Table 6. Summarydata for units UNIT NUMREI? __-__----_-

UNIT

DECISION

TYPE

_--______

VARIAHLES

1

11 IROTARY

2

23

,YET

UPPER

SCREEN)

1.500

3

24

IYET

LOWER

SCREEN)

.500

4

*1

(sTREAt4

BLENDER)

6

l2-STAGE

BAUM

5

BREAKER1

6

16 fSECONOAPV

7

*1

(STREAM

tl

23

(YET

9

2n.000

12.000

JIG1 ROLL

CRUSHED)

PTU l?ECOVEPV IPERCENT)

___

6.000

1.620

MULTIPLE

57.3

86.3

69.2

e-r.2

62.3

86.6

.275

BLENDER1

UPPER

7 (FROTH

10

VIELO (PERCENT1

__________________ _________

SCREEN)

.023

FLOTATION,

1 ICONCENTRATING

TABLE)

1.580

Table 7. Summarydata for flowstream OUIGIY !lNlT NO. _-_-w--w

FLOVSTREA~ NUWEU __________ 1

IFFEDl

2

,REFIKE

F,ESTINATION UNIT NO. ___________

1

0

FLOWATE (PEuCtNT DF FEW)

AS” IPERfEYTl

PVRITIC SULFUR (PERCENT)

TOTAL SULFUR IPERCENT)

BTU/LB.

LRS so21 MILLION RTU

em____-

___________

______-_ -_______-_____-__- _________ 38.4

100.0

B.Oh

B.lh

88340.

20.03

1

R

0

1Z.H

98.4

.CY

.31

2168.

2.8.9

3

1

c

2

67.2

29.6

Y.20

10.11

9991.

PO.24

4

2

L

3

45.9

20.0

8.06

8.99

11401.

15.77

5

2

u

4

41.3

40.2

10.47

11.36

8426.

26.96

3

L

7

32.2

17.5

6.92

7.73

11831.

13.07

3

u

L

13.6

26.0

10.77

11.96

10383.

23.04 25.R3

PUODUCTI

6

8

5

55.0

36.7

10.54

11.51

8911.

5

c

0

31.s

8.1

.Yb

1.58

13428.

5

R

0

15.1

76.7

23.73

25.16

2631.

l

11

5

H

6

a.3

72.2

22.70

24.21

3252.

*.e*.

12

6

7

8.4

72.2

22.78

24.21

2901.

****”

13

7

R

40.6

20.7

10.18

11.12

9916.

22.43

9 10

4 (CLEAN (REFUSE

COAL

PUOOUCTI

Pwmucll

2.36 ***.

14

R

L

9

3.9

21.5

7.76

8.56

11141.

15.36

I5

4

u

10

36.6

29.5

10.44

11.40

9780.

23.30

23.86

25.20

4634.

l

1.16

14033.

26.17

27.75

3480.

.93

1.69

13599.

16

,REFUSt

17

(CLEAN

18

(REFUSE

19

(CLEAN

PPODUCTI COIL

PHOOUCTl

PQODUCT) COAL

PUOOUCT~

9

R

0

1.2

59.8

9

c

0

2.7

6.5

10

R

0

13.8

10

c

0

2?.L)

coal feed whose detailed analysis is presented Table 5. The results of the simulation are summarixed in Tables 6 and 7. It has not been possible to assess the accuracy of this simulation since neither theoretical predictions nor actual plant performance data are available. The desirability of obtaining actual plant data, in order to validate the simulator, is obvious. It is not clear that such data can be obtained, however, because of both the time and expense involved in gathering detailed plant data and the possible disclosure of private, proprietary information. F%oGgAMAvArLABILJTr The coal preparation plant simulator developed for the U.S. Bureau of Mines has been written in standard FORTRAN IV, and should in on any large computer having a FORTRAN compiler and at least 56K of memory.

tMr. Jacobsen is now with the Colorado School of Mines ResearchInstitute,Golden, CO.

66.6

7.1

.bO

...* 1.65

. ..*. 2.20

A program listing and instructions for its use are presented in a recent University of Pittsburgh report (11). The program can also be obtained from the Coal Preparation and Analysis Laboratory, U.S. Department of Energy, 4800 Forbes Avenue, Pittsburgh, PA 15213. Ac~nowledgemenrs-Thisstudy was funded jointly by the U.S. Bureau of Mines (Departmentof Energy) and the U.S. EnvironmentalProtection Agency. The active participationof Mr. P. S. Jacobsen, formerly of the Coal Preparationand Analysis Laboratory,U.S. Bureau of Mines, Pittsburgh,PA is sincerely appreciated.tAlso, the crusher, screen and rotary breakerroutines were developed by Dr. A. VaiBant, Automated Process Surveys, Inc., New York, NY. Dr. Vaillant’s contributionsare grateftdlyacknowledged.

1. J. A. Cavallaro,M. T. Johnson& A. W. Deurbrouck,Sulfur reduction potential of the coals of the United States, Bur. MfnesRI 8118(1976). 2. Report on Sulfur Oxide Control Technology, U.S. Department of Commerce(1975).

Computer simulation of coal preparation plants 3. M. R. Geer 8 H. F. Yaucey, Plant performance and forecasting cleaning results, iu Coal Pfepamtion, (Eds J. W. Leonard & D. R. MitchelI) Chapter 18. AIME, New York (1968). 4. B. S. Got&xl & P. S. Jacobsen, A general&d distribution curve for characterizing the performance of coal cleaning equipment, Eur. Mines ZU 8238(1977). 5. B. S. Gottfried, A generalization of distribution data for characterizing the performance of float-sink coal cleaning devices, Znt. J. Min. Pnx. 5, l-20 (1978). 6. A. W. Deurbrouck, Performance characteristics of coalwashina eouioment: Hvdrocvclones, Eur. Mines RZ 7891 (1974)._ - 7. A. W. Deurbrouck & J. Hudy Jr., Performance characteristics of coal washing equipment: deuse-medium cyclones, Eur. Mines RI 7673 (1972).

107

8. A. W. Deurbrouck & E. R. Palowitch, Performance characteristics of coal-washing equipment: concentrating tables, Eur. h&es RZ 6239(1963). 9. J. Hudy JR., Performance characteristics of coal-washing’ equipment: dense-medium coarse-coal vessels, Bur. Mbtes RI 7154(1968). 10. M. Sokaski, P. S. Jacobsen & M. R. Geer, Performance of baum jii in treatiug rocky mountain coals, Bur. Mines 6306 (1%3). 11. B. S. Gottfried, Computer simulation of coal preparation plants-Final report, University of Pittsburgh Department of Industrial Engineering, (August, 1977). 12. S. Cierpisz & B. S. Gottfried, Theoretical aspect of coal washer performance, Znf.Z. Min. Proc. 4.261-278 (1977).