use of artificial intelligence for developing a ...coal preparation plant simulators are computing...

FINAL TECHNICAL REPORT February 1, 2005, through July 31, 2006

Project Title: USE OF ARTIFICIAL INTELLIGENCE FOR DEVELOPING A

NOVEL COAL PREPARATION PLANT SIMULATOR ICCI Project Number: 04-1/2.1B-1 Principal Investigator: Dr. Manoj K. Mohanty, Southern Illinois University Other Investigators: Dr. Hasan Sevim, Dr. A.M. Mahajan and Dr. Zhanyou

Huang, Southern Illinois University Project Manager: Mr. Joseph Hirschi, ICCI

ABSTRACT Coal preparation plant simulators are computer programs used to evaluate the performance of a preparation plant for given feed coal characteristics and the type of process flowsheet selected. However, none of the commercially available plant simulators attempts to maximize the overall plant yield for alternative flowsheets and also select the most suitable circuit configuration based on maximized plant profitability. In addition, none of these simulators has the ability to determine the required operating conditions for satisfying multiple product quality constraints while maintaining plant yield at its maximum level. This study developed a comprehensive plant simulator that not only maximizes overall plant yield while satisfying multiple product quality constraints for various circuit configurations, but also recommends the best circuit configuration based on maximized plant profitability. This new simulator, named as SIU-Sim, utilizes genetic algorithms, an artificial intelligence based optimization technique, to maximize overall plant yield for each alternative plant flowsheet. Furthermore, it utilizes the net present value (NPV) analysis technique to maximize plant profitability considering the total life of a new plant and the remaining life of an existing coal preparation plant. The new simulator has been tested for its utility with size-by-size feed characteristics obtained from a preparation plant processing Illinois coal. For the coal washability characteristics of that plant, a water-only plant was shown to be more profitable than a heavy media-based plant, although the latter provides better separation efficiency. The differential cost of a water-only plant versus a heavy media plant outweighs the incremental benefit obtained from the latter in the form of higher plant yield. It is believed that this simulator will be extremely useful for Illinois coal companies in selecting the most suitable cleaning alternative for new preparation plants as well as in upgrading existing plants to ensure maximum plant profitability.

EXECUTIVE SUMMARY

Past studies indicate that the higher cost of Illinois coal is a more significant parameter than its high sulfur content in causing the declining trend of Illinois coal sales. Hence, the reversal of this decreasing trend will require stringent cost cutting measures in all relevant cost components including coal preparation cost. One of the ways to reduce coal preparation cost is to investigate all possible plant flowsheets for a given coal and select the one that gives the maximum plant yield. Although, maximum plant yield translates to minimum cost per clean ton for an existing operation, it may not necessarily guarantee the maximum profitability for a coal preparation plant where there is an option of plant modification. For example, a heavy media circuit plant may provide more plant yield than a water-only circuit plant; however the required additional capital and operating cost for the heavy media plant alternative may outweigh the incremental gain in the plant yield in some cases. Therefore, it is quite likely that a water-only circuit plant may be more profitable than a heavy media plant in specific cases, especially with easier to clean coal and less stringent product quality constraints. Thus, a cost-benefit analysis is also needed for all technically feasible flowsheets and should be done for the entire life of a new plant or the remaining life of an existing operation. In some cases, examining the projected revenue and cost over the remaining life of a plant may show sufficient profitability to justify a major plant modification.

Coal preparation plant simulators are computing tools commonly used to predict the performance of a plant for the type of process flowsheet in use and for a given feed coal characteristics. However, none of the commercially available plant simulators attempts to simultaneously maximize the overall plant yield for alternative flowsheets and select the most suitable circuit configuration based on maximized plant profitability. In addition, none of these simulators has the ability to optimize the plant performance for simultaneously satisfying multiple product quality constraints, i.e., targeted values for not just ash content, or heating value, but simultaneously for ash content, sulfur content, heating value, moisture content, etc. Thus, the main goal of this study was to develop a comprehensive plant simulator:

which maximizes the overall plant yield while satisfying multiple product quality constraints for various alternative circuit configurations, and

which helps select the best circuit configuration on the basis of maximum plant profitability based on a variety of cost parameters.

In a recently completed ICCI project, Mahajan and Mohanty (2004) developed a genetic algorithm based plant optimization model that maximizes plant yield, while dealing with multiple product quality constraints. However, the objective function in the GA-based model includes multiple weight factors, thereby requiring an additional iterative process to first select the correct values for these weight factors prior to running the GA model. Inappropriate selection of weight factors may lead to erroneous results. In the present study, a new GA-based model has been incorporated in the simulator, named SIU-Sim, to address the aforementioned problem. SIU-Sim identifies the required operating conditions for individual circuits of a plant to obtain the maximum plant yield. The

simulator utilizes a net present value (NPV) analysis technique instead of lowest cost per clean coal product approach to ensure maximum plant profitability. The plant optimization process built into SIU-Sim utilizes the characteristic performance models generated for each cleaning unit operation, including heavy media vessel, heavy media cyclone, jig, water-only cyclone, spiral and flotation cell. An extensive plant testing exercise was conducted in a coal preparation plant and a nearby slurry pond recovery system to develop characteristic performance curves for jigs and water-only cyclones. The performance data developed in a past ICCI project (Mahajan and Mohanty, 2004) for heavy media unit operations, spirals and flotation cells were utilized for the above optimization process to avoid duplication of plant test work. A detailed literature survey was conducted to investigate the suitability of a variety of partition models developed in the past (Terra, 1954; Lynch and Rao, 1975 and Trawinski, 1976; Meloy, 1982; Kelly and Spottishwood, 1982; Rong and Lyman, 1985; Klima and Luckie, 1989; Peng and Luckie, 1991). Some available models were found more suitable for heavy media based processes, whereas others were more suitable for the water-only gravity based processes. Therefore, a unified model was developed by modifying the existing log-logistic model to best fit all density based separation processes. This modified log-logistic model is used to predict the performance from a specific category of density based separators, unless the user inputs any other models of his/her choice. To estimate the net present value for various plant flowsheet alternatives, it was required to develop model equations to estimate the capital and operating cost for not only the cleaning unit operations, but also the necessary screening, classification and dewatering unit operations. An updated cost data bank maintained at the Western Mining Inc. (2005) for various mining and milling equipment was utilized along with some of the cost models developed in the past by the US Bureau of Mines (1987) to develop new cost estimating equations for the above mentioned unit operations. These equations were utilized to estimate capital/installation and operating costs for individual cleaning classification and dewatering circuits on a basis of tons per hour of coal fed to these unit operations. The cash inflow (revenue) and outflow (cost) for each year during the lifespan of a plant was considered to determine the net present worth of each plant flowsheet alternative. A positive net present worth indicates the economical viability of a plant flowsheet and the flowsheet alternative providing the maximum net present worth is considered as the most profitable plant configuration. The conventional wisdom may suggest that a heavy media based plant flowsheet, being technically more efficient, may provide more profitability than a water-only based flowsheet. However, a case study has been conducted to show that it is quite possible that a water-only based coal cleaning plant is more profitable than a heavy media based plant although the latter is more efficient in cleaning coal being produced at a minesite in Illinois. Therefore, it would be of significant benefit for coal companies to consider various flowsheet alternatives by utilizing SIU-Sim and select the most profitable one for the specific coal they mine.

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OBJECTIVES The main goal of this study was to develop a comprehensive coal preparation plant simulator which recommends the most profitable plant flowsheet alternative with the optimum operating conditions of individual circuits for any given coal. To accomplish this goal, the specific research objectives are listed as follows:

• To generate the characteristic cleaning performance curves for a water-only plant (coarse coal jig and water-only cyclone) and a heavy media plant.

• To develop a robust optimization model to maximize plant yield for each alternative cleaning flowsheet to be evaluated during this investigation for a set of given product quality constraints.

• To develop a cost model to calculate coal cleaning cost for individual flowsheet alternatives and conduct a net present value analysis for various flowsheet alternatives.

• To incorporate the aforementioned plant yield optimization model and cost model in a plant simulator having an easy to use graphical user interface.

• To test and demonstrate the utility of the plant simulator with the actual feed washability data obtained from a plant cleaning Illinois basin coal.

INTRODUCTION AND BACKGROUND

Plant simulators are used to study various flowsheet alternatives and determine the product quality and quantity that can be produced under various operating conditions for individual circuits. Some of these simulators also provide an estimate of cost per ton of clean coal by determining the number and size of different processing units required for a specific raw coal tonnage and by computing both capital and operating costs for individual units. Plant simulators have been available since the 1970s. Some of these simulators were developed for mineral processing operations, whereas some were dedicated exclusively for coal processing operations. Some of the prominent work in the first category include JKSimMet (Morrison and Richardson, 2002), MODSIM (Ford and King, 1984; King, 2001) and its derivatives MicroSim (Cilliers and King, 1987; Stange, Cilliers and King, 1988; Stange, 1989), Utah-MODSIM (Herbst et al., 1989), USIM PAC (Brochot et al., 2002), MINDRES (McKee and Napier-Munn, 1990) and UCMINPRO (Adel and Sastry, 1982). The prominent work in the second category includes Limn (Wiseman, 2000; Leroux and Hardie, 2003), CoalPrep+ (Arnold, 2004), COPS (Salama et al., 1997), CPO (Ni and Lu, 1991), CCS (Arnold, 1994) and simulators developed by Waters et al. (1976), Gottfried (1975, 1978, 1982), and Rong and Lyman (1985). These simulators have a wide range of capabilities. They can be used to develop partition curves, material balances, and models for size classification, cleaning, dewatering, and thickening utilizing the size-by-size feed washability data. Some of the simulators also have the capability of conducting cost analysis. Most of the existing coal processing plant simulators, however, do not have optimization capabilities. Those having optimization capability predict the optimum plant performance based on the underlying principle of constant overall product quality approach or constant

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incremental product quality approach. The constant overall product quality approach maximizes the overall clean coal yield by producing identical product quality (such as ash content) in each cleaning circuit. On the other hand, the constant incremental product quality approach maximizes the clean coal yield by equalizing the ash content (or sulfur content) of the dirtiest particle(s) recovered from each cleaning circuit. Past studies indicated that the constant product quality approach can not guarantee maximum plant yield. The constant incremental product quality approach works very well for a single product quality constraint, such as to meet either the ash or the sulfur specification. However, it may become increasingly complex when multiple product quality constraints such as ash, moisture, sulfur and heating values (some or all of which are commonly specified in the sales contracts) are to be satisfied simultaneously. A recent study (Gupta and Mohanty, 2006) reported the limitations of the constant incremental product quality approach in greater detail. In recent years, a powerful optimization tool, known as genetic algorithms (GA), has been utilized for solving optimization problems in many different industries. GA utilizes an evolutionary algorithm based on Darwin’s theory of evolution which states that an offspring inherits many characteristics from its parents, but also possesses some unique characteristics of its own, and based on the inherited characteristics, only a small percentage of offspring survives (Haupt and Haupt, 1998). It is a random search technique that operates on grouped pieces of information called chromosomes. Rajesh et al. (2001) conducted an optimization study for a hydrogen plant using an adaptation of the GA technique. In another study, Svdensten et al. (2005) developed an optimization model for a crushing plant utilizing an evolutionary genetic algorithm. A more recent study (Mahajan and Mohanty, 2004) utilized GA to maximize coal preparation plant yields for satisfying multiple product quality constraints. However, the objective function in the GA-based model utilized multiple weight factors and an additional iterative process to first select the correct values for these weight factors before attempting to run the GA model. Inappropriate selection of weight factors may lead to erroneous results. In the present study, a new GA-based model has been incorporated in the simulator, named as SIU-Sim, to address the aforementioned problem with the previously developed GA-based model (Mahajan and Mohanty, 2004). Most commercially available simulators select the optimum flowsheet based on maximized yield, which minimizes the preparation cost on the basis of clean coal quantity (tonnage). However, a plant flowsheet generating maximum yield and thus maximum revenue, may not guarantee the maximum profitability because of the differences among flowsheet alternatives in capital and operating costs. SIU-Sim not only maximizes the overall plant yield using a new GA-based optimization model, but also helps determine the most profitable flowsheet based on maximum net present worth.

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EXPERIMENTAL PROCEDURES

A plant testing exercise was conducted in a coal preparation plant (shown in Figure 1) operating with a jig as the major cleaning unit. The plant treats 200 tph of coal with three piston jigs operating in series to clean the plus 3/16th inch size fraction and a bank of spiral concentrators to clean the finer fraction. Five sets of samples were collected from the jig feed, product and tailings streams by setting the float at different height on the jig bed. The test samples were screened at 3/16th inch before any sample analysis to evaluate separation performance achieved by the plant jig. Washability analysis was conducted on the feed and tailings samples obtained from each test to generate the partition data characterizing the separation performance achieved from the plant jig.

Figure 1: Flowsheet for a Jig and Spiral Preparation Plant in Illinois

Stoaker Coal

Pond

ROM coal 200 tph

Rotary Breaker

Jig Screen Screen

Classifying Cyclone Basket

Centrifuge

Spirals

Sieve bend

2”

Basket Centrifuge

Major Clean Coal

Coarse Reject

EB 36

1”

3/16”

1½”

3/8”

VC 48

Fine Reject

Stoaker Coal

Pond

ROM coal 200 tph

Rotary Breaker

Jig Screen Screen


Centrifuge

Spirals

Sieve bend

2”

Basket Centrifuge

Major Clean Coal

Coarse Reject

EB 36

1”

3/16”

1½”

3/8”

VC 48

Fine RejectPond

ROM coal 200 tph

Rotary Breaker

Jig Screen Screen


Centrifuge

Spirals

Sieve bend

2”

Basket Centrifuge

Major Clean Coal

Coarse Reject

EB 36EB 36

1”

3/16”

1½”

3/8”

VC 48VC 48

Fine Reject

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A similar testing exercise was conducted to evaluate the performance from water-only cyclones operating at a pond recovery site (shown in Figure 2). The operating pressure and the spigot diameter of the 15-inch diameter cyclones were varied to collect four sets of samples from the feed, overflow and underflow streams. As the flowsheet indicates, the water-only cyclones were evaluated to determine their cleaning performance on the ½-inch x 3/16th-inch size fraction of the slurry pond feed. Accordingly each water-only cyclone test sample was screened at 3/16th-inch before undergoing any analysis. Washability analysis was conducted on the overflow and underflow samples obtained from each test to generate the partition data to characterize the separation performance achieved from the water-only cyclone process.

Figure 2: Flowsheet for a Water-only Cyclone and Spiral Preparation Plant in Illinois

Classifying Cyclone

Coal Slurry Pond

DredgeDredge

Feed

½”Rock

15”dia

Water OnlyCyclone

Clean CoalProduct

3/8”

Spirals

Sieve bend

Screen

Screen

3/16”

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RESULTS AND DISCUSSIONS

Tasks 1-2: Plant Testing and Data Analysis: As described previously, the characteristic performance data for coarse coal jig and water-only cyclones were obtained from two extensive plant testing exercises conducted in a coal preparation plant and coal slurry refuse pond recovery site, respectively. Similar data for heavy media units, spirals and flotation cells were obtained from a past ICCI project (Mahajan and Mohanty, 2004). Coarse Coal Jig Testing: Five tests were conducted by varying the float level (height) on the material bed in the jig operating in a coal preparation plant at the RedHawk mine site. The overall samples obtained from feed, product and tailings streams for each test were analyzed for their ash content to determine the mass yield to the product. In addition, feed and tailings samples from each test were subjected to washability analysis to generate the characteristic partition data for the jigging process. A list of the specific gravity of separation (D50), probable error (Ep), Imperfection (I) and tailings bypass obtained from each test is presented in Table 1. As indicated, D50c and Ep varied over the range of 1.73 to 2.13 and 0.08 to 0.22, respectively. The detailed washability analysis data for each test is provided in Table A-1 in Appendix A. Water-only Cyclone (WOC) Testing: Four tests were conducted by varying spigot diameter and pressure drop across a 15-inch diameter WOC in use at a slurry pond recovery operation at a former Old Ben mine site. Ash analysis was conducted on the samples collected from the feed, overflow and underflow streams to determine the mass yield from the WOC. Washability analysis was conducted on the overflow and underflow samples obtained from each test to generate the characteristic partition data for the WOC operation. A list of the specific gravity of separation (D50c), probable error (Ep), Imperfection (I) and overflow (tailings) bypass obtained from each test is presented in Table 2. As indicated, D50c and Ep varied over the range of 1.39 to 1.54 and 0.07 to 0.15, respectively. The detailed washability analysis data for each test is provided in Table A-2 in Appendix A.

Table 1: Coarse Coal Jig Performance Parameters

Test Id D50c Ep I Bypass (%)1 1.79 0.18 0.1 16.972 2.02 0.2 0.099 11.253 2.13 0.15 0.07 2.524 1.73 0.08 0.046 12.55 1.86 0.22 0.12 12.16

Table 2: Water-only Cyclone Performance Parameters

Test Id D50c Ep I Bypass to u/f (%)

1 1.54 0.15 0.1 17.82 1.45 0.13 0.09 11.033 1.51 0.09 0.06 3.74 1.39 0.07 0.05 28.06

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Data Analysis and Development of a Unified Partition Model: Partition data for the jig and the water-only cyclone along with the data previously generated for heavy media vessel, heavy media cyclone and spiral circuits were normalized by dividing the X-axis data (relative density data) by the corresponding specific gravity of separation (D50c). A variety of models describing the normalized partition curve for density based separators as a function of D/D50c were evaluated including those developed by Terra (1954), Lynch and Rao (1975), Trawinski (1976), Meloy (1982), Kelly and Spottishwood (1982), Rong and Lyman (1985), Klima and Luckie (1989), and Peng and Luckie (1991). Some of these models fit better for heavy media separators, whereas others were better suited to water-only systems. Finally, the log-logistic model originally developed by Klima and Luckie (1989) was slightly modified to best fit the normalized partition data generated from all of the aforementioned density based separators. The modified log-logistic model may be described as follows:

)1Fexp(+1100

=Y

where )bX

ln(*a=1F

Y= Corrected partition coefficient to the reject stream; X= Average density/D50c

and ‘a’ and ‘b’ are fitting constants. The fitting coefficients, ‘a’ and ‘b’, determined for the above mentioned density-based separators by conducting a non-linear regression analysis using more than 30 partition data for each separator along with the respective coefficient determination (R2) values, are listed in Table 3. The fitting constant “a” is indicative of the efficiency of separation, whereas constant “b” aids in obtaining a better fit for the equation. Introduction of this latter constant “b”, which was missing from the original log-logistic model, helped make the new model more versatile.

Table 3: Non-linear Regression Fitting Coefficients for Water-based and Heavy Media-based Density Separators

Separator Type a b R^2

Jig -20.82 1.002 98.7WOC -18.34 1.006 98.6Spiral -14.68 1.008 98.4HMC -32.25 1.001 99.6HMV -22.32 1.006 99.4

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Task 3.1: Development of a Novel Plant Optimization Model: The GA is the underlying algorithm in the SIU-Sim to determine the maximum clean coal yield while satisfying multiple product quality constraints. In the GA model, combinations of yields of individual cleaning units are defined as chromosomes, and each yield value is called a gene of the chromosome. The length of a chromosome is equal to the number of parallel cleaning units in the flowsheet. For example, for a four circuit flowsheet consisting of heavy media vessel, heavy media cyclone, spiral and froth flotation, a chromosome may be defined as:

]YYYY[=Chromosome 4321 (1) where Y1, Y2, Y3 and Y4 are yield values for heavy media vessel, heavy media cyclone, spiral and froth flotation circuit, respectively. The objective or cost function is defined as a weighted average of the yield values for individual circuits, i.e.:

]dY+cY+bY+aY[=FunctionCost 4321 (2) where a, b, c and d are the mass fraction of the run-of-mine feed that reports to the above four circuits. A negative sign is placed at the beginning of the cost function since GA is traditionally applied to minimization problems. The individual steps utilized for this GA-based optimization model are discussed as follows:

Generate Initial Population: The algorithm starts by generating a large pool of chromosomes known as initial population. The size of initial population is 200 by default in this simulator. However, it can be changed by the user. A majority of chromosomes in the initial population must satisfy the constraints on product quality. Evaluate Cost Function: All chromosomes in the initial population are evaluated by calculating the cost function (plant yield in this case) using Equation (2). Then, chromosomes are ranked in ascending order with respect to the magnitude of the cost function. Select Mating Couples: Only the best half (top 100 chromosomes by default) are kept in each generation, while others are discarded. Of the 100 good chromosomes, the first 50 chromosomes are selected as candidate parents for mating. Cross over: 50 parent chromosomes are paired to generate 50 new chromosomes called offsprings. Offsprings that do not satisfy the constraints are discarded. Those remaining are added to the chromosome population described in the above step. The chromosomes are ranked again and the top 100 chromosomes are preserved for the next step. Mutation: Mutation is accomplished by deliberately changing values of some genes (in this case the yield values of some circuits) to generate new chromosomes. Mutation is able to increase the likelihood of jumping out of local minima. New chromosomes with lower cost functions are added to the population to replace chromosomes having higher cost functions. The population size still remains the same, i.e., 100 after mutation. The above steps are repeated until convergence conditions are satisfied. A Matlab-based computer program was written to develop the above plant optimization model.

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Task 3.2 Development of an Economic Analysis Model: It may be noted that the GA- based model discussed in the previous section optimizes only the coal cleaning part of the plant flowsheet. However, the economic analysis model considers the entire plant by also including conventional size separation and dewatering equipment. Vibrating screens have been considered for coarse screening, whereas classifying cyclones were used for fine size classification. Vibrating screen, basket centrifuge, screen bowl centrifuge and disk filter have been considered for dewatering of coarse, intermediate, fine and ultrafine coal, respectively. The cost calculation for the plant also includes the cost to maintain a plant thickener. The capital and operating cost data obtained from the latest version of the Western Mine and Milling Cost Manual (Western Mine Engineering, 2005) have been utilized to develop equations to separately calculate the capital and operating cost for each processing circuit, which includes a major unit operation and its ancillary equipment. A few equations were also obtained from the old Bureau of Mines Cost Estimating Handbook (1987) with appropriate inflation factors. Equations to estimate operating costs and combined capital and installation costs per tons-per-hour (TPH) of raw coal feed capacity for a variety of process equipment are listed in Tables 4 and 5. The detailed dataset and assumptions utilized to develop these estimating equations are listed in Appendix B.

Table 4: Estimating Equations for Combined Capital and Installation Cost of Various Coal Processing Circuits

Equipment Capital Cost Equation

(X: tph; y: capital cost($))

HMV Circuit 1,502 238,411y X= +

HMC Circuit 0.82358, 285.2y X=

Spiral Circuit 2,760y X=

Flotation Circuit 5, 471.4 46,954y X= +

JIG Circuit 0.523828,169y X=

WOC Circuit 1,018.2 67,879y X= +

Basket Centrifuge 0.293343,122y X=

Vacuum Filter 0.3537118,037y X=

Plate & Frame Pressure Filter 139,500y X=

Dewatering Screen 1,452.2y X=

Screenbowl Centrifuge 10,371y X=

Thickener 4,790.7y X=

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Both inflow and outflow of cash are calculated for each year during the projected life of a plant based on the plant yield and corresponding revenue as well as the capital/operating cost for the entire plant. SIU-Sim utilizes a spreadsheet based model to conduct a discounted cash flow analysis and determine the net present value (NPV) for the plant with each cleaning flowsheet option. To use this model, the user is required to input the economic and engineering data such as mining cost, product selling price, tax rate, minimum required rate or return, depreciation method, depreciation life, plant life, operating schedules, etc.

Table 5: Estimating Equations for Operating Cost of Various Coal Processing Circuits

Equipment Operating Cost Equation

(X: tph; y: operating cost($/ton))

HMV Circuit 0.0001 0.2794y X= − +

HMC Circuit 0.0001 0.2487y X= − +

Spiral Circuit 0.44191.2354y X −=

Conventional Flotation Circuit 0.0541.2922y X −=

JIG Circuit 0.63934.6806y X −=

WOC Circuit 0.0627 ( ) 0.4914y LN X= − +

Basket Centrifuge 0.74942.1641y X −=

Vacuum Disc Filter 0.64091.7572y X −=

Plate & Frame Pressure Filter 0.36060.8384y X −=

Dewatering Screen 0.0915y X= (y: $/hour)

Screenbowl Centrifuge 0.4889y X= (y: $/hour)

Thickener 0.3108y X= (y: $/hour)

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Task 4: Development of Plant Simulator: The new simulator, SIU-Sim, has been developed using the Matlab program and its graphic user interface (GUI) features as well as Excel spreadsheets. Figure 3 shows the graphical interface of the SIU-Sim, which consists of several modules that are described as follows: The first module allows the user to enter the basic plant data, such as raw coal feed rate (TPH) and target product qualities (ash content, sulfur content, heating value). The size distribution of feed coal, size-by-size washability data, and the flotation kinetic data for the ultrafine size fraction is input in an additional Excel spreadsheet. The washability data for a circuit shows up automatically in the next module when particle size fraction(s) are assigned to different cleaning circuits, marked as Circuit #1, #2, #3 and #4 in Figure 3. As shown, a maximum of 5 circuit configurations (including the bypass stream) can be considered for simulating coal preparation plant performance using SIU-Sim. A unified partition model has been developed by modifying the log-logistic model (Klima and Luckie, 1989; Meloy, 1982; and Peng & Luckie, 1991) suitable for developing the characteristic partition curve for both gravity separators and heavy media separators. This modified log-logistic model is used as the default equation for each density-based separator selected for various circuits shown in the display. However, users have the option of also selecting their own equations for the cleaning circuits.

Figure 3: Graphic Interface of Newly Developed Coal Preparation Plant Simulator SIU-Sim

SIU-Sim: Coal Preparation Plant Simulator

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Upon selecting the individual separator (heavy media vessel, jig, etc.) to clean one or multiple size fractions, the “generate data (G)” button can be used to plot the yield-ash curve or yield-sulfur curve for the separator for ready reference. After selecting the desired size fractions for all circuits (some circuits may be left unused), the GA model can be run by clicking the “Run GA” button in the lower left hand corner of the display. The simulator may take, at most, a few minutes, in a standard PC for a complete run. The clean coal yield and the product quality for each individual circuit that produces the maximum plant yield achievable while satisfying all quality constraints shows up in the “Output” box on the display. Corresponding operating conditions, i.e., effective specific gravity of separation (D50) for density-based circuits and retention time (in seconds) for flotation circuits, that need to be maintained in various cleaning units are recommended in the same “Output” box.

Task 5: Plant Simulator Testing: The developed simulator was tested using coal washability data obtained from a preparation plant cleaning Illinois No. 5 coal. The size-by-size feed washability data are provided in Table 6. The mass fractions of the coarse, intermediate, fine and ultrafine sizes of the run-of-mine coal are 60.88%, 28.26%, 4.56% and 6.30%, respectively. The assumed feed rate to the plant is 1,000 tons per hour (tph), with ash content of 42.4%, total sulfur of 0.61% and heating value of 8,069 Btu/lb. To meet the contract specifications, the plant is required to produce a maximum product ash content of 10%, sulfur content of 0.8% and a minimum heating value of 13,000 Btu/lb. Two four circuit flowsheets, one being heavy media-based and the other water-only- based, were studied to test and explain the utility of the simulator. The heavy media- based flowsheet used heavy media vessels, heavy media cyclones, spirals and flotation cells to clean coarse (3-inch x ½-inch), intermediate (½-inch x 16-mesh), fine (16-mesh x 100-mesh) and ultrafine (-100mesh) size fractions of the feed coal, whereas the water-only-based flowsheet used jigs, water-only cyclones, spirals and flotation cells to clean the coarse, intermediate, fine and ultrafine size fractions of the feed material. Simulation results shown in Figure 4 indicate a maximum plant yield of 56.75%, achievable using the heavy media-based flowsheet. As shown in the output box of the simulator, the actual product ash (10%) and sulfur (0.79%) content are equal or slightly less than targeted values (10% and 0.8%), whereas the heating value (13,103 Btu/lb) is slightly higher than the targeted value (13,000 Btu/lb). The simulator recommends utilizing a 50D of 1.78, 1.86 and 1.84 for the heavy media vessel, heavy media cyclone and spiral circuit and 189 seconds of retention time for the flotation cells to achieve the maximum plant yield while satisfying the product quality constraints. Simulation results for the water-only-based flowsheet are shown in Figure 5. It indicates that to satisfy the specified product ash, sulfur and heating value requirements, the water-only-based flowsheet could produce clean coal of 56.16% yield, 10% ash, 0.79% sulfur and 13,094 Btu/lb heating value, all of which satisfy the contract specifications. To meet these specification, the simulator recommends utilizing a 50D of 1.82, 1.76 and 1.75 for the jig, water-only cyclone and spiral circuit, and 81 seconds of retention time for the flotation cells.

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Table 6: Size-by-size Feed Washability Data Used for Simulator Verification 3'' x 1/2''

%Wt %Ash %Sul. But/lb %Wt %Ash %Sul. But/lb1.30 20.47 5.27 0.64 13897 20.47 5.27 0.64 13897

1.30 1.35 17.42 8.10 0.72 13399 37.89 6.57 0.68 136681.35 1.40 6.20 11.76 0.95 12869 44.10 7.30 0.72 136051.40 1.45 1.97 13.95 1.01 12472 46.07 7.59 0.73 135611.45 1.50 1.38 21.30 1.12 11195 47.45 7.99 0.74 134951.50 1.55 1.47 25.29 1.40 10590 48.93 8.51 0.76 134131.55 1.60 0.86 31.47 1.34 9651 49.79 8.91 0.77 133531.60 1.70 0.89 36.92 1.75 8742 50.69 9.40 0.79 132741.70 1.80 0.70 42.79 2.03 7581 51.39 9.86 0.80 131991.80 1.90 0.49 51.41 1.72 5985 51.88 10.25 0.81 131361.90 48.12 91.31 0.24 350 100.00 49.26 0.54 6986

1/2'' x 16M%Wt %Ash %Sul. But/lb %Wt %Ash %Sul. But/lb

1.30 37.13 4.75 0.66 13897 37.13 4.75 0.66 138971.30 1.35 13.82 7.49 0.71 13401 50.94 5.50 0.67 137631.35 1.40 7.62 12.62 0.95 12699 58.57 6.42 0.71 136751.40 1.45 1.88 16.47 1.11 12048 60.45 6.74 0.72 136251.45 1.50 1.17 21.37 1.26 10980 61.62 7.01 0.73 135771.50 1.55 1.14 26.84 1.38 10245 62.75 7.37 0.74 135241.55 1.60 1.02 32.98 1.48 9355 63.78 7.78 0.76 134631.60 1.70 0.94 38.84 1.60 8462 64.72 8.24 0.77 133931.70 1.80 1.00 41.94 1.71 7523 65.72 8.75 0.78 133071.80 1.90 1.07 57.72 1.46 4787 66.79 9.53 0.79 131801.90 33.21 89.83 0.40 549 100.00 36.20 0.66 8996

16M x 100M%Wt %Ash %Sul. But/lb %Wt %Ash %Sul. But/lb

1.30 26.67 2.06 0.65 14164 26.67 2.06 0.65 142061.30 1.35 15.99 4.33 0.68 13836 42.66 2.91 0.66 140671.35 1.40 14.16 6.13 0.69 13602 56.83 3.71 0.67 139791.40 1.45 6.81 9.84 0.74 13025 63.63 4.37 0.67 138991.45 1.50 3.51 15.20 0.88 12184 67.14 4.93 0.69 138281.50 1.55 2.04 21.71 1.09 11219 69.18 5.43 0.70 137671.55 1.60 0.97 27.84 1.28 10278 70.15 5.74 0.71 137301.60 1.70 1.33 34.24 1.40 9244 71.48 6.27 0.72 136541.70 1.80 1.26 41.66 1.51 7903 72.74 6.88 0.73 135681.80 1.90 1.30 55.71 1.54 5517 74.04 7.74 0.75 134441.90 25.96 86.86 0.83 683 100.00 28.28 0.77 10152

100M x 0%Wt %Ash %Sul. But/lb %Wt %Ash %Sul. But/lb

0 20 23.20 5.35 0.89 13938 23.20 5.35 0.89 1393820 40 12.08 15.45 0.92 12734 35.27 8.81 0.90 1352540 60 4.95 10.25 0.91 13354 40.22 8.99 0.90 1350460 80 5.69 13.95 0.90 12912 45.91 9.60 0.90 1343180 100 4.57 26.07 0.85 11467 50.48 11.09 0.90 13253

100 120 1.54 38.37 0.78 10000 52.02 11.90 0.89 13157120 140 1.93 47.68 0.72 8891 53.95 13.18 0.89 13004140 180 1.09 57.05 0.66 7772 55.04 14.05 0.88 12900180 240 0.33 61.92 0.62 7192 55.37 14.33 0.88 12867

individual float cumulative float




Density Class

Density Class

Density Class

Flotation time (sec)

13

Figure 4: Plant Optimization Results Generated with SIU-Sim for a Heavy Media-based Cleaning Circuit

14

Figure 5: Plant Optimization Results Generated with SIU-Sim for a Water-only-based Cleaning Circuit

Some of the input data utilized for the economic analysis, which is accomplished by utilizing the “cost analysis” button in the simulator, are listed in Table 7. Economic analysis results for the two flowsheet alternatives are shown in Tables 8 and 9, respectively. Results shown in Table 8 reveal that, for the heavy media-based flowsheet, a total capital investment of $17.5 million will be required to build the 1,000-TPH plant considered in this case study. The additional annual operating cost approximates to $12.5 million. Expected total clean coal production per year is 3.06 million ton, which would result in annual revenue of over $107 million. The estimated NPV of this plant with a heavy media-based flowsheet is $25.2 million, based on a plant life of 25 years.

15

Table 7: Input Data Utilized for NPV Analysis

Plant life: 25 yearsMining cost: $15/tonClean coal selling price: $35/tonEffective operating hours: 5,400 hours/yearEffective tax rate: 40%Discounted cash flow rate of return: 20%Hourly wage: $15Depreciation Method: MARSDepreciation life: 7 years

Table 8: Economic Analysis Data Generated using SIU-Sim for a Heavy Media Plant

Items Feed Rate Capital Cost Operating Cost tph $ (installed) $/yearHEAVY MEDIA VESSEL 609 1,153,194 686,450HEAVY MEDIA CYCLONE 283 864,656 330,311SPIRALS 46 56,470 43,818FLOTATION 63 408,124 351,479DEWATERING SCREEN 312 841,844 171,215BASKET CENTRIFUGE 687 200,079 43,365SCREENBOWL CENTRIFUGE 68 1,307,522 126,530THICKENER 3 30,199 6,327ANIONIC FLOCCULANTS 71 764,374CATIONIC FLOCCULANTS 71 84,930LABOR COST (60) 6,318,000SUPERVISION COST 947,700PAYROLL OVERHEAD 2,542,995ENGINEERING & CONSTRUCTION 4,862,086BUILDING AND SITES 4,862,086PROCESS PIPING 2,431,043MISCELLANEOUS 486,209TOTAL 17,503,511 12,417,494NPV $25,215,666

On the other hand, for the water-only-based flowsheet, as shown in Table 9, a total capital investment of $14.0 million will be required to build the 1,000-TPH plant. The annual operating cost is estimated to be about $11.8 million. Total clean coal production per year is expected to be 3.03 million tons. Annual revenue approximates to $106 million. The expected NPV of this plant with a water-only-based flowsheet is $26.9 million, based on a similar plant life of 25 years.

16

Table 9: Economic Analysis Data Generated using SIU-Sim for a Water-only Plant Items Feed Rate Capital Cost Operating Cost tph $ (installed) $/yearJIG 608.8 809,616 255,303WATER-ONLY CYCLONE 282.6 355,622 225,274SPIRALS 45.6 56,470 43,818FLOTATION 63 408,124 351,479DEWATERING SCREEN 315.29752 849,885 172,851BASKET CENTRIFUGE 184.73562 199,287 43,218SCREENBOWL CENTRIFUGE 61.52712 1,184,251 114,601THICKENER 3.076356 27,352 5,730ANIONIC FLOCCULANTS 65 692,310CATIONIC FLOCCULANTS 65 76,923LABOR COST (60) 6,318,000SUPERVISION COST 947,700PAYROLL OVERHEAD 2,542,995ENGINEERING & CONSTRUCTION 3,890,607BUILDING AND SITES 3,890,607PROCESS PIPING 1,945,303MISCELLANEOUS 389,061TOTAL 14,006,184 11,790,202NPV $26,938,344

Based on the above techno-economic analysis of two types of four circuit flowsheets, it is determined that the heavy media based flowsheet requires higher capital investment ($17.5M versus $14.0M) and slightly higher annual operating cost ($12.5M versus $11.8M). Although the heavy media flowsheet generates higher yield (56.75% versus 56.16%) because of the higher separation efficiency provided by heavy media-based separators, profitability assessed by the NPV is lower for this flowsheet. As indicated, NPV for water-only and heavy media flowsheets are $26.9M and $25.2M, respectively. Thus, the water-only based flowsheet should be the system of choice for the given plant.

CONCLUSIONS AND RECOMMENDATIONS

A new coal preparation plant simulator, named SIU-Sim has been successfully developed and tested with size-by-size feed washability data obtained from a coal preparation plant. The unique features and utility of this simulator include:

The new simulator provides an easy to use graphical user interface (GUI) developed to integrate a GA-based plant optimization model and an NPV-based economic analysis model into the simulator. The simulator allows the user to input washability data, flotation kinetic analysis data, plant feed rate, target product qualities and process partition models. It also allows the user to set up the plant flowsheet, display the washability data and kinetic analysis data and generate yield-product quality curves for each cleaning unit.

17

The new simulator maximizes the plant clean coal yield while simultaneously satisfying multiple product quality constraints utilizing a novel genetic algorithm (GA)-based approach, which is more reliable than the GA model previously developed by Mahajan and Mohanty (2004). In the present form, the simulator can satisfy three product quality constraints, such as ash, sulfur and heating value. The target moisture content is also satisfied by taking into consideration assays on an as received basis instead of a dry basis, which is an optional feature of the simulator.

For a new plant, the developed simulator helps select the most profitable flowsheet

by conducting net present value (NPV) analysis considering the entire life of the plant. The spreadsheet based cost analysis includes not only the cleaning circuits but also the entire plant, including dewatering and size classification units. New cost estimating equations were developed to separately determine the capital and operating costs for various screening, classification, cleaning and dewatering units utilized in a coal preparation plant utilizing the latest mine and mill equipment cost data obtained from Western Mine Engineering Inc.

The new simulator is also useful in analyzing the performance of an existing plant

flowsheet and determining the best flowsheet alternative for the remaining life of the plant. In the case of an operating plant having a considerable portion of its life still remaining, the additional cost of replacing the existing flowsheet with a more profitable new flowsheet may be fully offset by the additional revenue to be generated during the remaining life of the plant. This feature will be particularly useful for plants that have been operating with relatively inefficient cleaning processes but having to treat relatively difficult to clean coal.

For an operating plant which has the optimum combination of efficient cleaning

circuits, the simulator helps identify the most appropriate operating conditions for individual cleaning circuits that would allow the plant to achieve maximum clean coal yield and thus, maximum revenue. The new simulator provides a better way for selecting a specific flowsheet alternative for a plant by basing such decision on an NPV analysis instead of using the clean coal cost/ton approach, which does not take into consideration the differential cost between various flowsheet alternatives.

The new simulator utilizes a unified model (a modified log-logistic model) suitable

for all density-based separators. The model helps predict the partition data as a function of normalized mean density (ratio of mean density of particles and the effective density of separation) for all types of density-based separators, such as heavy media vessel, heavy media cyclone, water-only cyclone, jig, spiral, crossflow separator, etc. without the need of any probable error (Ep) values.

Finally, the following recommendations are made for further developing the new SIU-Sim coal preparation plant simulator:

The simulator now has the provision for utilizing a single partition model for each unit process. However, it is realized that it may be necessary to use multiple model

18

equations to predict the partition data for multiple size fractions treated by each unit process. This will require suitable modification of the Matlab code utilized to develop the simulator.

Currently, the simulator has the ability to maximize plant yield based on four

product quality constraints, such as ash, sulfur, heating value and moisture content. In the future, plants may be required to comply with certain restrictions on Hg, As, Se and other trace elements. This may require suitable modification of the Matlab code.

ACKNOWLEDGEMENTS

The Principal Investigator greatly appreciates the dedicated effort of his former graduate student, Dr. Zhanyou Huang, and his plant testing research team, which included Mr. Richard Geilhausen and Mr. Pramod Sahoo, for the successful completion of this research project. Special thanks go to Mr. Josh Carter and Mr. Steve Carter of Knight Hawk Coal Company for their special assistance during this project. The research funds from the ICCI/DCEO and the cooperation of the Coal Research Center at SIU are sincerely appreciated. Without their support, this project would not have been feasible.

DISCLAIMER STATEMENT

This report was prepared by Dr. M.K. Mohanty of Southern Illinois University at Carbondale with support, in part by grants made possible by the Illinois Department of Commerce and Economic Opportunity through the Illinois Clean Coal Institute. Neither Dr. Mohanty of Southern Illinois University at Carbondale nor any of its subcontractors nor the Illinois Department of Commerce and Economic Opportunity, Illinois Clean Coal Institute, nor any person acting on behalf of either: (A) Makes any warranty of representation, express or implied, with respect to the

accuracy, completeness, or usefulness of the information contained in this report, or that the use of any information, apparatus, method, or process disclosed in this report may not infringe privately-owned rights; or

(B) Assumes any liabilities with respect to the use of, or for damages resulting from the

use of, any information, apparatus, method or process disclosed in this report. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring; nor do the views and opinions of authors expressed herein necessarily state or reflect those of the Illinois Department of Commerce and Economic Opportunity or the Illinois Clean Coal Institute. Notice to Journalists and Publishers: If you borrow information from any part of this report; you must include a statement about State of Illinois’ support of the project.

19

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Simulation Package,” 17th APCOM Symposium, pp. 681-692. Arnold, B.J., (2004), Personal Communication. Arnold, B.J. and Gallier, P.W., (1994), “Using Coal Cleaning Simulators to Optimize

Clean Coal Production,” Coal Prep ’94, 11th International Coal Preparation Exhibition and Conference, pp. 145-157.

Brochot, S., Durance, M.V., Guillaneau, J.C., and Villeneuve, J., (2002), “USIM PAC 3.0: New Features of a Global Approach in Mineral Processing Design,” APCOM2002, pp. 465-477.

Cilliers, J.J. and King, R.P., (1987), “Microcomputers and Simulation: Meeting the Challenge,” Proceedings of APCOM 87, Johannesburg.

Ford, M.A. and King, R.P., (1984), “The Simulation of Ore-dressing Plants,” International Journal of Mineral Processing, 12, pp. 285-304.

Gottfried, B.S., (1975), “Computer Simulation of Coal Preparation Plants,” U.S. Bureau of Mines, Grant No. GO-155030, Final Report, Department of Industrial Engineering, University of Pittsburgh, November 1975 and August 1977.

Gottfried, B.S., (1978), “Computer Simulation of Coal Preparation Plants,” University of Pittsburgh, FE-9000-1(EPA-600/7-78-211), November.

Gottfried, B.S., Luckie, P.T. and Tierney, J.W., (1982), “Computer Simulation of Coal Preparation Plants,” Final Report, Washington D.C., United States Department of Energy, December.

Gupta V. (2005), “Performance Optimization of a Coal Preparation Plant using Genetic Algorithms,” MS Thesis, Southern Illinois University Carbondale.

Gupta, V. and Mohanty, M.K., (2005), “Coal Preparation Plant Optimization – A Critical Review of the Existing Methods,” International Journal of Mineral Processing, 79, pp: 9-17.

Haupt, R.L. and Haupt, S.E., (1999), “Practical Genetic Algorithms,” John Wiley & Sons, Inc., New York.

Herbst, J.A, Schena, G.D. and Fu, L.S., (1989), “Incorporating State of the Art Models into a Mineral Processing Plant Simulator,” Trans. Instn. Min. Metall., 98, January-April, pp. C1-C11.

Kelly, E. G. and Spottiswood, D. J., (1982), “Introduction to Mineral Processing”, pp. 46-61.

Klima, M.S. and Luckie, P. T., (1986), “An Interpolation Methodology for Washability Data,” Coal Preparation, Vol 2, pp. 165-177

King, R.P., (2001), “Modeling and Simulation of Mineral Processing System,” BH Publication.

Klima, M.S. and Luckie, P. T., (1989), “Application of an Unsteady State Pulp-Partition Model to Dense Medium Separations,” Coal Preparation, Vol 6, pp. 227-240.

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Lynch, A. J., and Rao, T. C., (1975), “Modelling and Scale-up of Hydrocyclone Classifiers,” Proceedings: Eleventh International Mineral Processing Congress.

20

Mahajan, A.M. and Mohanty, M.K., (2004), “Performance Optimization of A Coal Preparation Plant Using Genetic Algorithms,” Final Technical Report, Illinois Clean Coal Institute, Project Number 03-1/4.1B-4, Carterville, Illinois, August.

McKee, D.J. and Napier-Munn, T.J., (1990), “The Status of Comminution Simulation in Australia,” Minerals Engineering, 3, 1/2, pp. 7-21.

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Morrison, R.D. and Richardson, J.M., (2002), “JKSimMet: A Simulator for Analysis, Optimization and Design of Comminution Circuits,” Mineral Processing Design, Practice, and Control Proceedings, ed., Mular, A.L., Halbe, D.N. and Barrett, D.J., New York: Society of Mining, Metallurgy, and Exploration, pp. 442-460.

Ni, Q. and Lu, M., (1991), “Optimization of Coking Coal Preparation Plant Profits,” Proceedings of the 2nd International Symposium on Mining Technology and Science, pp. 1352-1360.

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A-1

APPENDIX A

A-2

Table A-1: Washability Data for Jig Samples

Test 1 Yeild = 91.34

Density Mean Feed Tailing Tailings with Reconstituted PN to Corrected Fraction Density Feed 100% Product Product PN

1.3 Float 1.25 18.58 0.00 0.00 18.58 100.00 100.001.3 x 1.4 1.35 59.16 0.55 0.05 59.11 99.92 99.901.4 x 1.5 1.45 7.37 0.00 0.00 7.37 100.00 100.001.5 x 1.7 1.6 3.84 1.16 0.10 3.74 97.39 96.861.7 x 2.0 1.85 1.95 11.01 0.95 1.00 51.16 41.182.0 Sink 2.4 9.10 87.28 7.56 1.54 16.97 0.00

100 100 8.66 91.34



1.3 Float 1.25 18.26 0.00 0.00 18.26 100.00 100.001.3 x 1.4 1.35 58.86 0.24 0.02 58.84 99.96 99.961.4 x 1.5 1.45 7.41 0.00 0.00 7.41 100.00 100.001.5 x 1.7 1.6 4.12 0.16 0.01 4.10 99.65 99.601.7 x 2.0 1.85 1.81 5.45 0.49 1.32 72.85 69.432.0 Sink 2.4 9.55 94.15 8.48 1.07 11.17 0.00

100.0 100 9.01 90.99



1.3 Float 1.25 19.64 0.00 0.00 19.64 100.00 100.001.3 x 1.4 1.35 60.99 0.00 0.00 60.99 100.00 100.001.4 x 1.5 1.45 8.75 0.01 0.00 8.75 100.00 100.001.5 x 1.7 1.6 3.18 0.17 0.01 3.18 99.72 99.711.7 x 2.0 1.85 2.03 1.10 0.06 1.97 97.12 97.052.0 Sink 2.4 5.40 98.73 5.26 0.14 2.52 0.00

100 100 5.33 94.67



1.3 Float 1.25 19.84 0.00 0.00 19.84 100.00 100.001.3 x 1.4 1.35 61.18 0.58 0.03 61.15 99.95 99.951.4 x 1.5 1.45 9.99 0.00 0.00 9.99 100.00 100.001.5 x 1.7 1.6 3.13 3.65 0.18 2.95 94.11 93.271.7 x 2.0 1.85 1.70 23.58 1.19 0.51 30.10 20.112.0 Sink 2.4 4.16 72.19 3.64 0.52 12.50 0.00

100.00 100 5.04 94.96



1.3 Float 1.25 21.51 0.00 0.00 21.51 100.00 100.001.3 x 1.4 1.35 63.16 2.57 0.10 63.06 99.84 99.821.4 x 1.5 1.45 7.53 1.01 0.04 7.50 99.48 99.411.5 x 1.7 1.6 2.54 5.04 0.19 2.34 92.36 91.301.7 x 2.0 1.85 2.45 27.32 1.05 1.40 57.12 51.192.0 Sink 2.4 2.81 64.05 2.46 0.34 12.16 0.00

100 100 3.85 96.15Total

Total

Total

Total

Total

A-3

Table A-2: Washability Data for Water-Only Cyclone Samples

Test 1: Mass Yield to Overflow: 50.37%Density Mean Overflow Overflow Underflow Underflow Reconst. Apparent CorrectedFraction Density based on based on based on based on Feed PN PN

Dm 100 units yield 100 units yield1.3 Float 1.25 53.20 26.79 11.69 5.80 32.59 82.20 1001.3 x 1.4 1.35 34.77 17.51 11.88 5.90 23.41 74.81 90.811.4 x 1.5 1.45 5.42 2.73 5.09 2.53 5.26 51.91 62.351.5 x 1.7 1.6 4.45 2.24 8.05 4.00 6.24 35.96 42.541.7 x 2.0 1.85 1.30 0.65 13.39 6.64 7.30 8.94 8.972.0 Sink 2.4 0.86 0.43 49.90 24.77 25.20 1.73 0.00

Total 100.00 50.37 100.00 49.63 100.00


Dm 100 units yield 100 units yield1.3 Float 1.25 58.10 32.68 9.26 4.05 36.73 88.97 100.001.3 x 1.4 1.35 34.56 19.44 18.05 7.90 27.34 71.10 79.921.4 x 1.5 1.45 5.57 3.13 8.93 3.91 7.04 44.49 50.011.5 x 1.7 1.6 1.77 1.00 12.68 5.55 6.55 15.22 17.101.7 x 2.0 1.85 0.00 0.00 10.47 4.58 4.58 0.00 0.002.0 Sink 2.4 0.00 0.00 40.60 17.77 17.77 0.00 0.00

Total 100.00 56.25 100.00 43.75 100.00



Total 100.00 52.18 100.00 47.82 100.00



Total 100.00 27.29 100.00 72.71 100.00

B-1

APPENDIX B

B-2

Table B-1: Equipment Installation Factors

Equipment Installation factor (%) Reference HMV Circuit 40 A HMC Circuit 40 A, B, C Spiral Circuit 38 A Flotation Circuit 55 A JIG Circuit 72 A WOC Circuit 33 B, D Basket Centrifuge 70 A, B Vacuum Disc Filter 40 A Plate & Frame Pressure Filter 40 A Screenbowl Centrifuge 63 A A = U.S. Bureau of Mine, 1987 B = Western Mine Engineering, 2005 C = Personal communication with a coal company D = Personal communication with a coal preparation plant design and construction company

B-3

Table B-2: Capital and Operating Costs – JIG Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 250 525,000 0.142 350 587,500 0.107 575 718,056 0.075 675 938,889 0.076

1,000 1,044,444 0.058

Installed Capital Cost - JIG Circuit

y = 28169x0.5238

R2 = 0.9452

400,000

600,000

800,000

1,000,000

1,200,000

200 300 400 500 600 700 800 900 1000

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.1: Installed Capital Cost Curve – JIG Circuit

Operating Cost - JIG Circuit

y = 4.6806x-0.6393

R2 = 0.9873

0.05

0.07

0.09

0.11

0.13

0.15

200 300 400 500 600 700 800 900 1000

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.2: Operating Cost Curve – JIG Circuit

B-4

Table B-3: Capital and Operating Costs – WOC Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 100 169,697 0.206 200 271,515 0.145 300 373,333 0.150 400 475,152 0.114 500 576,970 0.098

Installed Capital Cost - WOC Circuit

y = 1018.2x + 67879R2 = 1

100,000200,000

300,000400,000

500,000600,000

700,000

50 100 150 200 250 300 350 400 450 500 550

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.3: Installed Capital Cost Curve – WOC circuit

Operating Cost - WOC Circuit

y = -0.0627Ln(x) + 0.4914R2 = 0.927

0.05

0.10

0.15

0.20

0.25

50 100 150 200 250 300 350 400 450 500 550

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.4: Operating Cost Curve – WOC Circuit

B-5

Table B-4: Capital and Operating Costs – HMV Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 100 422,970 0.265 200 536,590 0.218 300 670,680 0.188 400 777,630 0.239 500 1,004,640 0.228 600 1,175,300 0.211 700 1,288,230 0.196

Installed Capital Cost - HMV Circuit

y = 1502.6x + 238411R2 = 0.9893

200,000400,000600,000800,000

1,000,0001,200,0001,400,000

50 250 450 650Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.5: Installed Capital Cost Curve – HMV Circuit

Operating Cost - HMV Circuit

y = -0.0001x + 0.2794R2 = 0.982

0.15

0.20

0.25

0.30

0 200 400 600 800

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.6: Operating Cost Curve – HMV Circuit

B-6

Table B-5: Capital and Operating Costs – HMC Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 100 369,879 0.237 200 646,141 0.228 300 894,194 0.221 400 1,169,283 0.202 500 1,382,765 0.197

Installed Capital Cost - HMC Circuit

y = 8285.2x0.8235

R2 = 0.9995

300,000500,000700,000900,000

1,100,0001,300,0001,500,000

50 150 250 350 450 550Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.7: Installed Capital Cost Curve – HMC Circuit

Operating Cost - HMC Circuit

y = -0.0001x + 0.2487R2 = 0.9636

0.15

0.20

0.25

0.30

0 100 200 300 400 500 600

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.8: Operating Cost Curve – HMC Circuit

B-7

Table B-6: Capital and Operating Costs – Spiral Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 25 69,000 0.320 50 138,000 0.189 100 276,000 0.176 150 414,000 0.134 200 552,000 0.170

Installed Capital Cost - Spiral Circuit

y = 2760xR2 = 1

0

100,000

200,000

300,000

400,000

500,000

600,000

0 50 100 150 200 250

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.9: Installed Capital Cost Curve – Spiral Circuit

Operating Cost - Spiral Circuit

y = 1.2354x-0.4419

R2 = 0.9115

0.10

0.15

0.20

0.25

0.30

0.35

0 25 50 75 100 125 150 175

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.10: Operating Cost Curve – Spiral Circuit

B-8

Table B-7: Capital and Operating Costs – Flotation Circuit

Capacity Capital Cost Operating Cost tph $ $/ton 25 235,964 1.095 50 265,745 1.034 100 531,491 0.896 150 974,400 0.985 200 1,099,636 0.975

Installed Capital Cost - Flotation Circuit

y = 5471.4x + 46954R2 = 0.9642

0200,000

400,000600,000800,000

1,000,0001,200,000

0 50 100 150 200 250

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.11: Installed Capital Cost Curve – Flotation Circuit

Operating Cost - Flotation Circuit

y = 1.2922x-0.054

R2 = 0.9725

0.95

1.00

1.05

1.10

1.15

0 25 50 75 100 125 150 175 200 225

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.12: Operating Cost Curve – Flotation Circuit

B-9

Table B-8: Capital and Operating Costs – Vacuum Disc Filter

Capacity Capital Cost Operating Cost

tph $ $/ton 8.13 256,219 0.48

16.25 296,020 0.27 18.04 345,771 0.29 30.06 375,622 0.19 39.00 430,348 0.17 48.75 457,711 0.14 65.00 537,313 0.13 81.25 564,677 0.11

Installed Capital Cost - Vacuum Disk Filter (Rotary)

y = 118037x0.3537

R2 = 0.9771

200,000

300,000

400,000

500,000

600,000

0 10 20 30 40 50 60 70 80 90Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.13: Installed Capital Cost Curve – Vacuum Disc Filter

Operating Cost - Vacuum Disk Filter (Rotary)

y = 1.7572x-0.6409

R2 = 0.9924

0.00

0.10

0.20

0.30

0.40

0.50

0 10 20 30 40 50 60 70 80 90Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.14: Operating Cost Curve – Vacuum Disc Filter

B-10

Table B-9: Capital and Operating Costs – Plate & Frame Pressure Filter


tph $ $/ton 25 3,487,500 0.452 50 6,975,000 0.389 75 10,462,500 0.334 100 13,950,000 0.294 150 20,925,000 0.275 200 27,900,000 0.241 300 41,850,000 0.236

Installed Capital Cost - Plate & Frame Pressure Filter

y = 139500xR2 = 1

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

0 50 100 150 200 250 300

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.15: Installed Capital Cost Curve – Plate & Frame Pressure Filter

Operating Cost - Plate & Frame Pressure Filter

y = 0.8384x-0.3606

R2 = 0.992

0.100.120.140.160.180.200.220.240.260.28

0 50 100 150 200 250 300

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.16: Operating Cost Curve – Plate & Frame Pressure Filter

B-11

Table B-10: Capital and Operating Costs – Basket Centrifuge

Capacity Capital Cost Operating Cost tph $ $/ton 25 110,429 0.1980 50 110,429 0.0990 100 182,143 0.0781 125 182,143 0.0625 150 182,143 0.0521 200 203,429 0.0416 225 203,429 0.0369 250 203,429 0.0332 300 203,429 0.0277 325 239,143 0.0289

Installed Capital Cost - Centrifuge (Basket)

y = 43122x0.2933

R2 = 0.9931

100,000120,000140,000160,000180,000200,000220,000240,000

0 50 100 150 200 250 300 350Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.17: Installed Capital Cost Curve – Basket Centrifuge

Operating Cost - Centrifuge (Basket)

y = 2.1641x-0.7494

R2 = 0.9858

0.00

0.05

0.10

0.15

0.20

0 50 100 150 200 250 300 350 400

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/to

n)

Figure B.18: Operating Cost Curve – Basket Centrifuge

B-12

Table B-11: Capital and Operating Costs – Screenbowl Centrifuge


tph $ $/hour 25 259,281 12.22 50 518,561 24.44 75 777,842 36.67 100 1,037,122 48.89 150 1,555,684 73.33 200 2,074,245 97.77 300 3,111,367 146.66

Installed Capital Cost - Screen Bowl Centrifuge

y = 10371xR2 = 1

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 50 100 150 200 250 300

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.19: Installed Capital Cost Curve – Screenbowl Centrifuge

Operating Cost - Screen Bowl Centrifuge

y = 0.4889xR2 = 1

020406080

100120140160

0 50 100 150 200 250 300

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/ho

ur)

Figure B.20: Operating Cost Curve – Screenbowl Centrifuge

B-13

Table B-12: Capital and Operating Costs – Dewatering Screen

Capacity Capital Cost Operating Cost tph $ $/hour 25 36,310 2.29 50 72,621 4.57 75 108,931 6.86 100 145,242 9.15 150 217,863 13.72 200 290,484 18.30 300 435,726 27.45

Installed Capital Cost - Dewatering Screen

y = 1452.4xR2 = 1

050,000

100,000150,000200,000250,000300,000350,000400,000450,000500,000

0 50 100 150 200 250 300

Feed Tonnage (tph)

Cap

ital C

ost (

$)

Figure B.21: Installed Capital Cost Curve – Dewatering Screen

Operating Cost - Dewatering Screen

y = 0.0915xR2 = 1

0

5

10

15

20

25

30

0 50 100 150 200 250

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/ho

ur)

Figure B.22: Operating Cost Curve – Dewatering Screen

B-14

Table B-13: Capital and Operating Costs – Thickener

Capacity Capital Cost Operating Cost tph $ $/hour 25 119,769 7.77 50 239,537 15.54 75 359,306 23.31 100 479,074 31.08 150 718,611 46.63 200 958,149 62.17 300 1,437,223 93.25

Installed Capital Cost - Thickener

y = 4790.7xR2 = 1

0200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,000

0 50 100 150 200 250 300

Feed Tonnage (tph)

Inst

alle

d C

apita

l Cos

t ($)

Figure B.23: Installed Capital Cost Curve – Thickener

Operating Cost - Thickener

y = 0.3108xR2 = 1

0102030405060708090

100

0 50 100 150 200 250 300

Feed Tonnage (tph)

Ope

ratin

g C

ost (

$/ho

ur)

Figure B.24: Operating Cost Curve – Thickener

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