Linear Programming to Solve Coal Blending Problem

17th International Mining Congress and Exhibition of Turkey- IMCET 2001, 2001, ISBN 975-395-417-4 best Blending of combust by Linear computer programing for the Power give at Seyitomer Coal Mine K. Erarslan, H. Aykul, H. Akcakoca & N. Cetin Dumlup? nar University, Department of Mining Engineering, 43 speed of light, Kutahya, Turkey nip In this study, a unidimensional programming illustration is developed to act upon the optimal ember blend in terms of calibre and metre. Coal with various features is mined from different panels of Seyitomer Lignite Coal District and supply to a nearby power ground.The pure tone of the char is extremely inconsistent through the horizontal and vertical directions, which entails the precise homework of ember amalgamate during the mining and stockpiling stages. Otherwise, a large punishment has to be remunerative to the power mark life. In this study, the objective is to span the calorific values necessary by the power plant. The q uality features and production capacities of coal from different panels atomic number 18 determined and are enjoymentd in quality constraints. The power plant requires coal in dickens groups, which are of different qualities and quantities.Therefore, two bilinear programming models complementing each other are developed in gear up to determine the blending conditions that satisfy the needs of the plant. The models are introduced and solved m the LINDO megabucks program. Reasonable solutions are reached and optimal amounts of blending are handled. The model also allows the evaluation of coal panels of low quality. 1 INTRODUCTION Linear Programming (LP) is one of the most widely utilize methods of operation research for stopping point problems.This method is a reasonable and reliable procedure for determining the optimum distribution of imagings, optimal production, minimum cost, level best profit, and so forth , which comprise the objectives (Ozturk, 1997). In this method, stopping point parameters to make the objective optimal are linear or assumed to be linear (Taha, 1992, Hillier and Liebermann, 1995). The general form of the problem is make by objective flow and subjected constraints (1) (2) where Z= objective of the model Cj = coefficient of/* decision variable (1=1,2 Xj = / * decision variable N) oefficient of decision variable (i=l,2,.. ,m) b, = limited resource for iJ* constraint LP approximation is widely used in mining as well as in other industrial fields. Open perdition limits, production scheduling, material flow in treating plants, blending, equipment selection, method selection, transportation, etc. , are its main applications (Chanda and Wilke, 1992, Dijilani and Dowd, 1994, Huang, 1993, Mann and Wilke, 1992, Meyer, 1969, Smith and You, 1995). However, investment, planning, or selection, in other terminology any actions requiring decision, can be optimized.Especially in open pit mines and resistance mines feeding coal to power p lants, the quality and measuring of coal is life-or-death because the burner blocks of power plants are foundinged according to circumstantial features of coal. Inability to match coal quality and quantity to these specific features results in either penalty costs for the coal enterprise or a decrease in the power plants efficiency. In addition, inconsistent coal features lead to wear in the power plants fire units and all integrated components.In this respect, coal-producing enterprises try to match their coal features to power plants specifications by blending and homogenizing coal extracted from different panels and take aims. Satisfying the emergencys of the plant is achieved by selective 719 = mining and/or blending. In this study, a germane(predicate) case is considered. Seyitomer coal enterprise in Kutahya, Turkey has problems of quality and quantity in supplying the nearby power plant. A well-planned and organized blending procedure and, accordingly, production plan is necessary.In this paper, the problem, is modeled in terms of linear programming and reasonable solutions are obtained. 2 SEYITOMER COAL ENTERPRISE AND ITS occupation Seyitomer Lignite Enterprise (SLE) is located 20 km. northwest of Kutahya city center. The toilet is characterized as Late Miocene-early Pliocene. The lignite seams in Seyitomer basin consist of two horizontal levels (0-7 S), referred to as A and B seams, according to their depths. The seams are disjointed from each other by waste interbedded formations whose thickness vary from 10 to 50 m.These two seams may exhibit variation according to their accompaniment in three sub-regions (Seyitomer, Aslanl? , Ayval? ), where the geological coal formation has been determined by drill holes. The thickness of the A seam, located at the top level of the basin, varies in the go of 5-25 m. Sofrelec, 1967). The thickness of B seam varies in the range of 2-30 m. In the basin, these two seams are rarely observed together. The sea m define as A Is deposited only in the Seyitomer region and the coal occurrences in the Aslanl? nd Ayval? regions. The B seam consists of 3 different sublevels, referred to, from Uie top to the run aground of the seam, as B, B2, Bj Their calorific values decrease towards to the seam base as the interbedded layers get thicker. The upper level coal seams B and B2, which have a high calorific value and are produced in sorted coat and quality (+100 mm), have supplied the market for public heating. The B3 coal, which is of low quality and contains fine coal (-100 mm) from the affect plant, is sold to the power plant.At the enterprise, production is performed by the open pit mining method. The overburden, whose thickness varies from 35 to 60 m. , is unsnarled by drilling and blasting. The stripping method is the excavator and truck and dragline method. The 1 electrical excavators have a 10-yd bucketful volume and the dragline has a 70-yd bucket capacity. Production and transportation are also by excavatortruck and loader-truck meu? ods. It is impossible to process the coal with wet washing techniques.For this reason, only crushing, sieving and sorting can be applied to the coals of the region. There are three plants working for the power plant and three plants working for the market in the enterprise. The coal is dispatched to the market or the power plant according to its quality. 720 Recently, in terms of quality and quantity the demands of the plant have not been fulfilled and in order to drown the problem selective mining has been used. Consequently, there is an increasing tendency to use ripping and bulldozers and loaders (Aykul, 2000). APPLICATION OF LINEAR PROGRAMMING IN SLE 3. 1 Definition of the Problem In Seyitomer Coal Mine, six different coal types produced from different panels and levels are treated. The terms for these coals, their average calorific values and one-year quantity to be extracted according to Ideal planning are shown in remit 1. These coals need to be blended in accordance with the specifications need by the power plants ruin units. Seyitomer power plant has four burning units. The operating conditions of these units are shown in Table 2.The annual coal requirement of the power plant is 6,000,000 tons the first three burning units ( social unit 1, unit of measurement 2 and building block 3), with the same requirements, need 4,500,000 tons, while the last burning unit ( unit 4) requires 1,500,000 tons. Table 1. Determined features of coal types according to ideal planning in SLE Coal Type Calorific measuring rod order (ton/year) (kcal/kg) FineCoal(-lOO) (From Plants) 1675 2,000,000 Stock of Kizik 1750 800,000 Stock of Marl 1428 250,000 B train 2000 600,000 B take aim 1800 600,000 B, Level 1600 1,500,000 Table 2.Operating Conditions of Power Plant Power Plant Base soup up subject area Grain Size (mm) Units (Kcal/kg) Unit 1 1750 100 0-200 Unit2 1750+100 0-200 Umt3 1750 100 0-200 Unit 4 1600 00 0-200 The blending requirements of the coal are as follows i. Coal coming from the processing plants (fine coal), and that produced from the stock of Kizik, stock of marl, the B3 level, B2 level and B level can supply Unit 1, Unit 2 and Unit 3. it. Coal produced from the stock of Kizik, stock of marl and B3 level can supply Unit 4.Two different linear models were developed since there are two design specifications in the power plant. Therefore, first, the amount of coal of the B3 level and stock of K? z? k are determined fo. Unit 4 with the jockstrap of the first linear program, and then the rest of the determined amounts are used in the second linear program developed for Unit 1, Unit 2 and Unit 3. After determining these conditions, the main aim is to obtain coal blends that have the maximum heating calorific value in the range of specifications (Kaya, 2000). . 2 Constitution 0/the Model The objective function for Unit 4 maximizing the first blends calorific value, which has a m aximum limitation by the constraints, is shown in Equation 1 (3) Subjected to Equations 4 to 11 (4) (5) The restrictions are shown in Equations 13 to 21. 4,500,000 (13) (14) (15) (16) (17) (18) (19) (20) (21) (6) (7) (8) (9) (10) (11) Here, X2 Amount of coal from stock of K? z? k, t. Xj Amount of coal from stock of marl, t. Xe Amount of coal from B3 level, t.The objective function for Unit 1, Unit 2 and Unit 3 maximizing the second blends calorific value restricted by a subjected constraint is shown in Equation 12. (12) Amount of fine coal from processing plants, t. Amount of coal from stock of K? z? k, t. Amount of coal from B level, t. Amount of coal from B2 level, t. Amount of coal from B3 level, t. 3. 3 Solutions of Models The LINDO package program is used to solve the models. The optimum quality and quantity results of the terminal tables for Unit 4 are shown In Table 3 (Kaya, 2000). Table 3 last(a) results of model for Urul 4 Coal Types Coal Amount Heal Content (ton/year ) (kcal/kg) Stock of K? ? k 500. 000* 1750 (X2) Stock of marl (X,) 250,000* 1428 750. 000* 1600 B, Level (X,) 1,500. 000 Blend of Coals 1620 5* h *Optimam values ai 7 grummet As it may be seen in Table 3, the blend quality of the coal is found to be 1620 kcal/kg. This value is within the range of the specific design values of Unit 4. The final tables for Unit 1, Unit 2 and Unit 3 are shown in Table 4. 721 Table 4. Final results of model for Unit 4 Coal Amount Heat Content Coal Types (ton/year) (kcal/kg) 2,000,000* 1675 Fine coal (Xi) Stock of K? z? k 300,000* 1750