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选煤厂工艺布置摘要：本文介绍了近几年我国选煤厂厂房与车间建设的特点，并对澳大利亚模块式重介选煤厂的工艺特点及在选煤厂设计方面的选煤工艺、 工艺布置进行了剖析，并对选煤厂工艺布置提出了建议。关键词：工艺布置 选煤工艺 钢结构 模块化 Layout of coal preparation plantAbstract: This paper introduces the characteristics of the Chinas coal preparation plant and workshop building recent years,carried out the analysis of process characteristics of the HM coal preparation plant in Australian and Put forward of the recommendations to the preparation plant process layout .Keywords: process layout Coal preparation process Steel Structure Modular1.我国近年厂房布置的特点这么多年以来，我国选煤厂的厂房一直采用钢筋混凝土的框架结构，厂房有多层，而且体积比较大，建设周期较长，投资高。上个世纪末期， 我国开发了高效率，低消耗的新型选煤厂，采用了新的方式，通常在第二层布置主要设备，通过减少设备之间的高差，来降低厂房的高度，这样不仅减少了厂房的体积，而且也减少了电力的消耗，降低成本；设备的支撑进行标准化，以利于加工和制造，施工需要的时间较短；这样以来为适应市场竞争、投资早点收到效益创造了有利的条件。随着洗选设备可靠性的提高，设备选型应该尽量选用大型的设备，尽可能各个环节采用单台设备，这样不仅可以简化工艺，缩短煤流通道，而且减少工艺环节，提高设备利用率，进而提高选煤厂的管理能力，提高经济效益。这种处理效果对大型选煤厂来说最为显著。2.模块式的重介洗煤厂2. 1工艺方面( 1) 澳大利亚模块式重介选煤厂使用的是世界上最大的重介旋流器( 直径1 150-1 300) 作为主要工艺的分选设备，它最大的优点有: 入选物料的粒度的上限比较大( 达到 80 mm) , 入选物料的粒度范围宽，单台装备处理的能力较大( 达到600-700t/h) , 分选效率高，能够到 95%。此种方式突破了采用小直径的重介质旋流器组分选的一般模式。( 2) 工艺系统内部的各个环节比较简单、有比较高的生产效率, 利于控制及管理。有以下特点:1. 调节控制重介密度的方法: 以前控制重介密度是通过调节清水和浓介的量来实现的。因此控制因素通常较多，所以一般密度控制的稳定性和准确度均难以达到要求，所以控制和调节起来很难。但是控制介质密度在模块化的选煤厂比较容易实现，它一次性把需要补充的介质全部加够，在全厂生产的过程中让合介桶的介质密度一直比提前设定好的介质密度要高，所以补加浓介质作业便可取消，因此控制重介密度便可只通过调节补加水量来实现。这样，调节因素最少, 因而介质密度的自动控制比较容易于实现和实施。另外，加水方式与传统方法也不相同( 传统方法是只往合格介质桶内加水，由于合格介质桶容量较大，所以重介悬浮液密度变化比较缓慢，滞后较严重，影响工作效率) 。模块化的选煤厂除了向合介桶内补加水外，主要方法是在合介泵入料管上加一个增水管。根据密度计的在线检测值调节水管上的电动阀，从而自动微调添加的水量。这种方法十分简捷方便，在几秒钟内就可以调节好，精确度非常高，很好地稳定了介质密度、提高了分选效果。2.在分选前进行预先脱泥，令煤泥不进入分选系统，因此不需要为了脱除合介中的煤泥含量来降低介质的浓度、粘度而再设置分流箱来分流这个环节，从而使其工艺系统省去分流调控环节，管理及控制均变得比较简单容易。除此之外：由于不需要打分流，合介中损失的水量减少，为了把介质的密度调节好而需补加的水量就减少，这样才使其具有前面所说的优点，即在合格介质桶泵的入料管的上方加入少量清水从而达到对介质的密度的灵敏调控。3.合格介质桶在液位较低时会发出报警信号，根据报警信号来控制新介质的补加。一般而言，一个班进行一次介质补加便可。介质可以通过以下途径补加：通过将稀介桶中的稀介质经过磁选机选出精矿处理后，加入到合格介质桶中。如此才能稳定合介的密度，同时可除去重介分选过程中的大量煤泥，提高介质品质，因而改善了设备的分选效果。4.稀介质通过单筒高效磁选机进行回收，不仅磁选效率特高( 可达99.8% 以上) ，而且每台机器的处理量较大。从而采用这样的高效磁选机进行磁选作业便可替代以前的的两段主再磁选作业，并且这种磁选效果更好一些。并且还能省去两段磁选作业中间的介质浓缩环节，大大简化了介质回收流程。2. 2工艺布置方面（1）由于各个设备的选型进行大型化，各个作业环节单机化，并且全厂采用单系统。这种工艺布置具有一系列的优点：一、选煤厂房的体积变小了。由于各环节单机化，省略了许多转载、分配作业，使设备与对应的设备定位安装，因而布置紧凑，减少了厂房的体积。采用模块式结构的选煤厂厂房高度低，体积一般不到同类厂房体积的1/2。二、设备单机化，数量减少。由于采用大型化的设备，设备的数量减少，便于实现控制自动化，便于操作管理，设备维修量也减少了。三、设备运行能耗也相应降低。设备台数少，厂房高度低，并且模块式重介选煤厂的吨煤电耗与跳汰选相差不大。（2）厂房使用模块化、装配式的全钢结构，相对于传统框架结构的厂房有着突破性的意义。厂房使用的钢结构件能够在工厂提前制造，现场组装，省工省时，使施工速度得以加快。同时于单层厂房的大厅上部装有20 t 桥吊，能够完成中大型机械装备整体吊装，很大程度缩小了设备的安装调试时间。因此这种主厂房的施工周期仅仅需要几个月。3车间工艺布置各个车间内的布置及所有装备的布置均参照工业总平面图、工艺流程图、设备选型等要求，把厂房和设备布置。3-1各主要车间的平面布置原则3-1-1受煤车间（包括受煤坑及翻车机房）受煤车间横向布置时 如果铁路站场为尽头时，为了便于重车或空车的调车作业，一般把受煤车间布置在靠重车进车方向的一端。受煤车间纵向布置时 如果铁路站场不是尽头时，受煤车间一般布置在铁路站厂内侧，减少铁路股道通过胶带输送机地道的上部，减少皮带长度，减少受煤距离，降低成本。为了缩短铁路站场的长度，受煤坑下的转截点一般布置在重车进车方向的一端。3-1-2 准备车间与原煤受煤场当受煤方式为受煤坑时，通常将入洗物料经过筛分分级，然后将大块破碎后再进行存储。当受煤车间采用翻车时，原煤贮煤厂一般直接贮存入洗原煤，不经过筛分破碎车间处理。条件许可时，可将筛分破碎车间与原煤贮存场采用联合建筑。3-1-3主厂房为了便于煤泥水和污水能以自流的形式流出主厂房，一般将主厂房选择在地形较高的位置。主厂房平面为矩形时，为了减少土石方工程量，长轴方向的柱网线应当平行于地形的等高线。3-1-4装车仓、装车点及产品煤贮煤仓装车仓和装车点一般设在铁路站场外侧。必须设在内侧时，同受煤坑的布置原则一样。如果设置装车仓时，一般就不需要再设产品贮煤厂。如果设置装车点时，应当设置产品贮煤厂。3-1-5浓缩车间浓缩机最好选择在低于主厂房而又高于煤泥沉淀塔的地形上，这样煤泥水能以自流的形似出入。但选择沉淀塔位置时并没有严格的要求。浓缩车间应尽量靠近主厂房煤泥水流出的一胯间，以便缩短管道的长度。3-1-6煤泥沉淀池应该把煤泥沉淀池布置在地势低的地方，以便于主厂房生产的污水能自流到该处，同时也要考虑煤泥沉淀池排放水能顺利排出场外。煤泥沉淀池应设置在铁路站场附近，以便于煤泥装车。在沉淀池和铁路线之间，应该有适当的煤泥晾干场地。煤泥沉淀池最好布置在主导风向的下端，长轴方向最好平行于主导风向，以减少煤泥飞扬。3-2 选煤厂车间工艺设备布置设备的定位应标注安装尺寸。设备布置要结合安装、检修、操作、安全、环保的要求，按照设备型号特征、数量和生产工艺要求进行。设备布置的基本原则如下：1 ）同一类型的设备尽量排列整齐布置，且在同一标高上。相对大型厂房而言，为避免振动设备过于集中而产生共振，同时为了方便操作、检修和管理，以及减少土建工作量，应尽量对称或同轴布置两台或两台以上同类设备。设备上的阀门、闸门、手把等操作部件距地板高度应适宜工人操作，通常在1.5m左右。操作台至少要有 1.5m左右的宽度 ，不是单独使用时要求更宽。2 ）工艺设备布置要合理、紧凑，但也不能过于拥挤，要留出适当的操作、检修空间。同一类型的设备要能灵活的调整。尽可能减少转载点和运输设备，采用自流作业。设备之间的线路、管路、溜槽要尽量缩短，在穿过跨间和楼板时，不要撞梁或柱子。在粉尘等小颗粒多的位置（如原煤筛分破碎车间，储煤场），应设除尘降灰设施。 3 ）振动较大的设备和重型设备应尽量布置在厂房底层。对于要求整机安装的大型设备，在土建施工前就应对其定位安装。 4 ） 管路、溜槽的安装角度应保证物料畅通，根据溜槽、管路倾角参考值进行选取。煤仓的倾角要大于物料安息角，结合其粒度、水分合理确定。为保证厂房的采光和通风，大型设备尽量不要布置在门窗附近。4选煤厂工艺布置的建议1 ）在厂房工艺布置时，要综合考虑工艺特点，对设备进行合理的布置，尽量使物料实现自流，避免转载。此处以脱介筛筛下物走向为例进行说明：(1) 方案一脱介筛筛下物经漏斗收集，以自流的方式到稀介桶，然后用泵抽到磁选机净化，净化后的合格介质自流至合介桶2)溜槽和管路，要满足一定要求，也要避免弯路，减少管路的长度。为了便于设备的安装和检修作业，需要空出主要设备的检修场地和起吊设备所需高度，还要照顾到主厂房的通光和透风。因地下作业环境差，条件不好，所以，除受煤坑、大翻车机房等外，不设地下设施。5结论虽然煤炭的十年黄金期已经过去，但煤炭将仍然是我国所依赖的主要一次能源。为了提高煤炭的利用率，为了节约能源，节约资源的利用率，我国将提高煤炭的入洗率，那么选煤厂将承担着这样的重任，因而选煤厂应当将厂型大型化，设备大型化，设备操作简单化，管理方便。从国内外选煤厂建设的情况来看，今后的选煤厂将向多样化、灵活化、个性化、便利化、低层化、自动化、可靠化、最优化、陈本最低化等方向发展和改造。总之，经过选煤者的奋斗，我国在选煤行业超越世界先进水平还是有希望的。参考文献1. 匡亚莉. 选煤工艺设计与管理.徐州：中国矿业大学出版社，2009.52. 戴少康.选煤工艺设计的思路与方法.北京：煤炭工业出版社，20033. 郝凤印，李文林.选煤手册（工艺与设备）.北京： 煤炭工业出版社，19934. 杨金铎，房志勇.房屋建筑构造.北京：中国建材工业出版社，19935. 刘顺，赵承年，路迈西.选煤厂设计.北京：煤炭工业出版社，19876. 李寻，刘顺.选煤厂设计.北京：煤炭工业出版社，19957. 选煤厂设计手册编写委员会.选煤厂设计手册.北京：煤炭工业出版社，19788. B.A.威尔斯，胡力行等.选矿工艺学.北京：冶金工业出版社，19859. 谢广元.选矿学。徐州：中国矿业大学出版社，200110. 王淀佐.矿物加工学.徐州：中国矿业大学出版社，200311. 夏宗庚. 国内外选煤厂设计技术的发展. 煤炭加工与综合利用，1996，（01）：48-5112. 梁金钢,赵环帅,何建新. 国内外选煤技术与装备现状及发展趋势J.选煤技术,2008,(1):60-6413. 周少雷，邓晓阳. 我国的选煤厂设计与新技术应用. 煤炭加工与综合利用，1999，（05）：9 -1214. 段锡章，夏宗庚，薛钊. 我国选煤厂设计的发展概述. 煤炭加工与综合利用,1999，（03）：6-815. 我国选煤厂设计现状. ，2011论文 61 英文翻译原文Prediction of economic operating conditions for Indian coal preparation plantsabstractThe most important optimization concept, which, has long been recognized in coalprepar- ation with multiple cleaning circuits, is the constant incremental quality approach. However, this approach maximizes the overall plant yield for a targeted product quality, without putting any em- phasis on coal value/price. So, sometimes confusion arises in the determination of the overall pl- ant yield that would more than offset the price due to lower quality of product. In this paper a me- thod is presented to maximize the coal value by considering the equal incremental quality approa- ch as well. Here the predicted yield of composite coal has been calculated by using a designed pro- bable error value, then the value of a particular coal is maximized. A case study with six differrent coals of different characteristics is presented to ascertain the merit of this approach. This techni- que offers the coal preparation engineer an effective and straight forward method for determining the optimum cut points of separation for different coals to achieve maximum economic gain.1. IntroductionIndian coals in general are of drift origin and high ash content with poor washability characteristics, which make these unsuitable for use without further upgrading. At the same time the quality of run-of-mine (ROM)coal hasprogressivelydeteriorateddue to the availability of inferior grade coal reserves and high degree of mechanization introduced in large opencast mines. Furthermore, the Ministry of Environment and Forest (MoEF), Government of India, has imposed arestriction on the use of high ash coal (N34%) in power plants located 1000 km away from the pit-head, sensitive localities, and critically polluted areas. All these factors necessitated a long term strategy for improvingthe qualityof coal by adopting an appropriate cost effective washing technology.Coal washing has been used since the early 1950s in India to meet the required coal quality. Infact , the coal is washed when it is beneficial to do so, and the optimum operating condition schosn fo rwashing are decided on a cost basis. While taking up this exercise, it was immediately realized that the solution to the cost versus quality problem came from two aspects: technological and economics vis-vis optimum ash level of the washed coal. The technological optimum condition addresses the concept of constant incremental product quality to maximize the plant yield18, while the economic optimum condition deals with coal value/price.Presently, some models and commercial software package, like EPRIs coal cleaning cost model 9, CCS 10, CPO 11, Utah-MODSIM 12, an Excel-based cost-estimate model developed by the United States Geological Survey 13, SIU-SIM SIMULATOR 14 and Apex provided by Western Mine Engineering, Inc., are available for economic analysis of coal preparation. However, these software packages render little practical insight for the Useful Heat Value pricing structure of coal and wide frequent variations of ROM coal qualities. Moreover, in actual practice most of the coal washing devices are not perfect and their performance depends on feed qualities, which in turn necessitates a correction to account for the extent of imperfection. In the proposed method thepricestructure of coal,imperfection of washingdevices and variation of feed quality have been implemented and can be changed according to the processre quirements. The approach adopted hereis to maximize the value of coal subjected to equal quality constraint by using a spread sheet-based program within a framework of price structure, equipment imperfection, and feed quality variation. This method has been found to be a practical approach for determining the optimum operating parameters for different washing equipment to maximize the value of coal. 2. Method2.1. Optimum specific gravity of separation from price structureIf a sample of coal whose fractional ash content is x1 and price y1 rupees per ton then the mass and ash content of this coal for one rupee will be 1y1 tons and x1 y1 tons respectively. Similarly another coal having ash content x2and pricey2 will have the mass1 y2 tons and ash content x2 y2 tons for one rupee. This clearly shows that these two coals have different weights and contain different amounts of ash, although their values are same (one rupee). The increment having weight of 1y21y1 tons containing x2y2x1y1 tons of ash will therefore have no value. Hence the fractional ash (critical ash) content of an increment of zero value is x2 y2x1 y1 1 y21 y1 orx2y1x1y2 y1y2. From the above derivation it is seen that if any quantity of coal containing a critical ash ofx2y1x1y2 y1y2 was added to another coal, the value of the latter would remain substantially unchanged, whatever its original ash content. If a coal containing less than this critical ash had been added, the value would have been increased and conversely if coal of more than the critical ash had been added, the value would have been reduced. So every particle of coal containing less than this critical ash is therefore of some value and should be recovered. As the value of a coal was unaffected by the addition of an increment containing critical ash, then it would also be unaffected if such an increment was taken a way, and indeed it would still be unaffected if all the ash present was removed in the form of such an increment. o the separation should be made at a specific gravity of separation corresponding to this critical ash. This critical ash depends upon the pricing structure of the coal. For example, the pricing mechanism for non-coking coal in India is grade-based as shown in Table 1. The Useful Heat Value (UHV) is calculated on the basis of an empirical relationship given byWhere A and M are ash and moisture contents, respectively. In the case of a coal having moisture less than 2% and volatile matter content less than 19%, the UHV would be the value arrived as above, reduced by 150 Kcal/kg for each 1% reduction in volatile mattercontent below1 9% fraction pro-rata 15. Based on this pricing mechanism the critical ash Content of Indian non-coking coal(5% moisture content)is found to be 59.49% as shown in Table 2. The price of a coal sample depends on its UHV only. The UHV of a coal sample is decreased by 138 units if its ash or moisture content is increased by one unit. The moisture and ash have equal impact on UHV. But in the process of coal washing only the ash content of clean coal is decreased by selective separation of ash-forming materials from the combustible materials. So the critical ash content of the coal depends on its moisture content as shown in Fig. 1. But as the pricing structure is grade-based the critical ash will no longer remains the same when it is maximized for its value.2.2. Optimization approachIn composite washeries, the raw coals received are crushed and screened into several size fractions. Each size fraction is then treated separately in a suitable washing circuit that operates according to parameters determined by laboratory float-and-sink tests, and every effort is made to obtain the maximum possible yield of the primary clean product. The exercise under taken here is to utilize the concept of constant incremental ash for all parallel circuits to maximize the price ofclean coal within a framework that is not only practical but also makes it accessible for the coal preparation engineers to determine the optimum cut points for washing of different coals. But in actual practice most of the coal washing devices are not perfect. This happens due to the fact that a greater portion of the lower density middlings is misplaced into the refuse stream compared to the higher density middlings getting misplaced to the clean coal stream. The misplace-ment of higher quality (lower ash) material lowers the effective incremental ash. Hence a correction must be made to account for the impact of misplaced solids on incremental quality. It was also realized that the efficiencies of density-based separators tend to decline as the particle size decreases 16. To over come this, finer particles must be treated at a higher specific gravity cut points to maintain optimum yield (same incremental quality) 6,17. Here, we follow Osbornes approach (1988) for the variation of probable error or Ecart probable moyen (Ep) with particle size, equipment size, and separation density which is where f1is a factor accounting for the variation of Ep with particle size, f2is a factor accounting for variation of Ep with equipment size, f3 is the manufacturers guarantee factor (Table 3) and Esis a function representing the variation of Epwith separation density for each type of equipment, which is equal to 0.047D500.05 (for dense-medium baths) and 0.027D500.01 (fordense-medium cyclones). D50repre-sents the relative density of separation. Knowing the Ep and D50 values, the sharpness factor a was calculated for each density-basedseparator from the following equation:This relationship holds good for a sharpness factor greater than 5,which is typical for dense-medium bath and dense-medium cyclone operations 18. The predicted cumulative yield and cumulative ash of the coarser and finer size fractions of coal were calculated at each specific gravity by using modified Lynch equation. This equation predicts the fraction of the feed material of a particularspecific gravity which reports to the reject coal and it can be expressed as:where x is the normalized specific gravity (mean specific gravity / specific gravity of separation) and n is the fitting constant.These data were utilized to calculate the elementary (incremental) product ash to each separation density using the following equationwhere Ykand Akare the cumulative yield and cumulative ash at the kth density cut point or separation density,respectively,Yk+1and Ak+1 are the cumulative yield and cumulative ash at the next higher, i.e.,(k+1)th density cut point respectively, and IAk+1is the incremental ash at (k+1)th density cut point. From theoptimizationstandpoint, another issue that had tobe dealt with was the problem associated with the composition and quality of coal. Coal contains a variety of minerals that vary widely in coal seams with respect to kind, abundance, and distribution. The density of coal depends on the quantity and quality of these minerals. Although the constituents of ashes are reported as oxides, they are largely amixture of silicates, oxides, and sulfates, with smaller quantities of other compounds. So the presence of different proportions of these minerals in a coal would allow specific coal or specific size fractions of a coal to have different densities even when their ash contents are the same and vice-versa. Therefore, it is unlikely to obtain a linear relation between the ash content of particles within a narrow size class with the reciprocal of particle density. Thus cubic relationships were assumed between Y, (Y*A), and IA with separation density in the range from 1.4 to 2.0 and polynomial equations up to a third degree were fitted for the different coals. The generalized form of the equation iswherer=1denotestheequationforcumulativeyieldandr=2denotes the equation for cumulative yieldcumulative ash and kis the specific gravity. The model parameters (x0, x1, x2, and x3) for cumulative yield, the product of cumulative eyield and ash, and elemen tary ash of the clean coal were estimated for different coals and size fractions.2.3.Spreadsheet methodThe approach taken here was to maximize the yield subject to the equality constraint (constant incremental ash) by using a spread-sheet-based program and optimization routines. Here Solverthe optimization routine, available in Excel was used. The Solver is typically used to minimize or maximize a cell in a spreadsheet. This routine makes it possible to quickly identify an optimal value for a formula in a target cell which is related, either directly or indirectly, to other cells in the spreadsheet. These cells can be selected and adjusted by the Solver to produce the result specified in the target cell subject to user-defined constraints.First the data are arranged in an Excel spreadsheet, and the Parameters are put into the spread sheet and the for mula for the model Is entered by referencing to the cells containing the model parameters. When invoked, this integrated routine allows the user to specify a target cell to be maximized. In the present case, the cell containing the values of net increase in value is selected as the target cell. The Max button is then toggled within the dialog box to indicate that a maximization problem is to be undertaken. Then the range of cells to be adjusted by the maximization routine is specified within the By changing cells box. For this case the specific gravity of cut is specified. as constraints within the Solver dialog box. If necessary, the Options button can be selected prior to maximization to adjust the parameters for numerical precision and convergence limits. After entering all of the requested input values the maximization routine is initiated by choosing the solve button. For nonlinear problems, it can be helpful to try different starting values for the adjustable cells, especially when Solver has found a solution that is significantly different from themexpected one.2.4. Case studyTalcher coals come under Mahanadi Coalfields Limited (India) of Gondwana coal category and are considered as high ash percent (3545%) coals. There are seven collieries/mines present in this coal field (viz. Jagannath colliery, Kalinga mines, Ananta colliery, Bharatpur colliery, Dera colliery, Hingula mines and Lingaraj colliery). The present Talcher area for supplying washed coal to a thermal power station. As per the fuel supply agreement, the coals of Ananta Open Cast Project (OCP) (Seam-II), Bharatpur OCP(Seam-II), Kalinga OCP(Seam-IIC), Hingula OCP(Seam-VIII), Hingula OCP(Seam-IX), and Ananta OCP (Seam-III) are supplied to the thermal power station. Accordingly, six coal samples (approximate weight of four tons each) were collected as perIS436, Part1 from eachcollieryandtransportedtothelaboratoryfor testing. Each coal sample was prepared by mixing and coning andquartering the whole coal. The testingof sampleswas carried outasper relevantIS codes (viz. IS 436(part I,Sec I), 1350 (part I and part II), 6345and 13810).Two different cases are studied in this work. In the first case, an attempt was made to maximize the net increase in the value of clean coal by washing each individual coal in two size fractions(10013 mm) and(130.5 mm)without timposing any prefixed composit eash content of the clean coal. In the second case the same two size fractions were washed to maximize the net increase in value at a prefixed ash content of 34% (MoEF). Six coals from different collieries were processed in two size fractions (10013 mm and 130.5 mm) in a composite washery. The coarser size fraction (10013 mm) and the finer size fraction (130.5 mm) were processed in a dense-medium bath and dense-medium cyclone, respectively.2. Results and discussionTable 4 presents (i) the overall ash of individual coals, (ii) weight percentages of washing size fractions 100+13 mm and 13+0.5 mm along with the ash content and, (iii) the values of regression parameters corresponding to Equation 6inthespecifiedspecificgravity range. Table 5 presents the optimum yield, specific gravity of cut, and elementary ash at cut point for washing each coal to achieve maximum value of composite cleans. The results indicate that the maximum composite yields are different for different coals and vary from a minimum of 62.46% for Hingula OCP (Seam-IX) coal to a maximum of 88.76% for Ananta OCP (Seam-II) coal, whereas the composite ash for mostofthecoalswasfoundtobe29.06%(except Hingula OCP Seam-VIII and Seam-IX coal). This result agrees well with the result obtained by Roberts and Shaffer company, one of the leading U.S. engineering firms 19.They claimed that typical high-ash Indian coals of Talcher coalfield can be economically cleaned to the 2530% ash range through application of state-of-the-art U.S. technology. On the other hand, Mathur et al. had examined the economics of beneficiating F grade coal of Talcher coal field and concluded that the washing is not at all attractive 20. In fact, the results of Mathur et al. are confusing and misleading as the equations used in their model are seems to be questionable. For example, the model utilized an exponential relation- ship between the clean coal yield and ratio of clean coal ash to feed coal ash, without putting any attention for misplacement. A significant variation was also observed in their collected data.It is observed that the net increase in value ranges from a minimum of Rs29.76 for Hingula OCP (Seam-IX) coal to a maximum value ofRs133.18 for Kalinga OCP (Seam-IIC) coal. It is interesting to note that though the raw feed quality is best in terms of ash content for Ananta OCP (Seam-II) coal, it is not the best for maximum possible obtainable value.On the other hand, Kalinga OCP(Seam-IIC) coal is worst interms of raw feed quality but is the best for net increase in value. In other words, to get a maximum profit it is not necessary to process the best quality (lower ash) raw feed coal, because the increase in value of a particular coal depends much on its washability characteristics. Thisn study shows that any further reduction in ash below this economical pointcauses too greatloss in value forthe profit. Hence, this econom- ical point warrants the careful consideration of operating parameters for washing equipment. In this case the specific gravity of cut, which is one of the most important operating parameters, for all the coals is well within the normal operating ranges of washeries. Sometime confusion arises, regarding the economic optimality, for the prefixed clean coal ash as fixed by MoEF. So to avoid this confusion the optimum yield, specific gravity of cut, elementary ash at cut point, and the net increase in value were tabulated in Table 6 for a prefixed clean coal ash of 34%. The yield increased for all the coals but the net increase in value drastically decreased. At the same time, the operating specific gravities of cut were well above the normal operating ranges of washeries, which create operational difficulties. The composite ash content at maximum obtainable value for most of the coals is much more less than that fixed (34%) by MoEF. Only for Hingula OCP (Seam-IX) coal was the composite ash content at the maximum obtainable overcomes the confusion that the upper ash limit fixed by the MoEF is to reduce the existing pollution level, transportation load etc. but not to maximize the value of coal.4. ConclusionsThis study shows that constant incremental quality approach is definitely an important optimization concept and certainly not the only concept that is taken in to account while deciding the optimum operating parameters for washing equipment. The maximization of value for clean coal in a coal washery is highly sensitive to many operational parameters, but consideration of all these parameters during optimization calculation is very challenging. The proposed method can effectively be used for maximizing the value of clean coal and deciding the optimum operating parameters for different feed coals, whose individual washability characteristics may be known or updated from time to time. The imperfection in separation that is associated with the different washing equipment can easily be handled by this method. The successful application of this method for maximizing the value of clean coal is illustrated.References1 F.W. Mayer, A new washing curve, Gluckauf 86 (1950) 498509.2 J. Abott, The optimisation of process parameters to maximise the profitability from a three-component blend, Proceedings 1st Australian Coal Preparation Conference, Newcastle, Australia, 1982, pp. 87105.3 R.X. Rong, G.J. Lyman, Computational techniques for coal washery optimization parallel gravity and flotation separation, Coal Prep. 2 (1985) 5167.4 G.J. Lyman, Computational procedures in optimization of beneficiation circuits based on incremental grade or ash content, Trans. Instn. Min. Metall. 102 (1993) C159C162.5 K. Sen, A. Choudhury, R. Das Gupta, S. Ghose, R. Haque, Composite washing of coals from multiple resources: optimization by numerical technique, Int. J. Miner. Process. 41 (1994) 147160.6 G.H. Luttrell, C.J. Barbee, F.L. Stanley, Optimization cut-points for heavy medium separa- tions,in:R.Q.Honaker,W.R.Forrest(Eds.),AdvancesinGravityConcentration, Society of Mining, Metallurgy and Exploration Inc., Littleton, CO, 2003, pp. 8191.7 G.H. Luttrell, R.Q. Honaker, R.H. Yoon, Optimization of the coal fuel supply chain: acoal preparation perspective, Proceedings 29th International Technical Confer-ence on Coal Utilization, 2, 2004, pp. 13561364.8 V. Gupta, M.K. Mohanty, Coal preparation plant optimization: a critical review of the existing methods, Int. J. Miner. Process. 79 (2006) 917.9 C.E. Zebula, D.D. Ferris, K.E. Harrison, E.R. Torak, Coal Cleaning Cost Model, Electric Power Research Institute, Palo Alto, CA, 1993.10 B.J. Arnold, P.W. Gallier, Using coal cleaning simulators to optimize clean coal production, Coal Prep 94, 11th International Coal Preparation Exhibition and Conference, 1994, pp. 145157.11 Q. Ni, M. Lu, Optimization of coking coal preparation plant profits, Proceedings of the 2nd International Symposium on Mining Technology and Science, 1991,pp. 13521360.12 J.A. Herbst, G.D. Schena,L.S. Fu,Incorporating state of the art models into amineral processing plant simulator, Trans. Instn. Min. Metall. 98 (1989) C1C11.13 S. Bhagwat, X. Zhang, H. Fan, Estimation of coal cleaning costs: a spreadsheet based interactive software for use in estimation of economically recoverable cost reserves, Final Report Submitted to U.S, Geological Survey, Reston, VA, 2000.14 Z. Huang, M. Mohanty, H. Sevim, A. Mahajan, B. Arnold, Techno-economic analysis of coal preparation plant design using Siu-Sim simulator, Int. J. Coal Prep. Util. 28 (2008) 1532.15 Web site: www.coalindia.nic.in/pricing.htm16 D.G. Osborne, Coal preparation technology, graham & trotman, London 1 (1988) 600.17 C.J. Clarkson, Optimization of coal production from mine face to customer, Proceedings of the 3rd Large Open Pit Mining Conference, Makcay, Australia, 1992, pp. 433440.18 A.K. Mukherjee, R.K. Dutta, P.V.T. Rao, Development of mathematical model for prediction of washery performance, Coal Prep. 22 (2002) 109122.19 S.M. Smouse, W.C. Peters, R.W. Reed, R.P. Krishnan, Economic analysis of coal cleaning in india using state-of-the-art computer models, Proceedings of Fourth International Conference on Effect of Coal Quality on Power Plants, EPRI TR-, 104982, 1995, pp. 476506.20 R. Mathur, S. Chand, T. Tezuka, Optimal use of coal for power generation in India, Energy Policy 31 (2003) 319331.英语原文 212 中文译文对印度选煤厂经济运行条件的预测摘要：恒定的增加质量的方法被含有多种选煤系统的选煤厂认为是最重要的优化方法。但是，这种方法将产品产量最大化作为目标来增加选煤厂收益，没有考虑煤的价格。所以，有时候在决定选煤厂产量时将超出补偿价格由于降低产品质量而出现困惑。在这篇文章里提出一个方法：即通过考虑等价增加煤炭质量来实现增加煤炭价值的最大化。这里混合煤的预测产量已可以通过一个设计好的可能偏差值计算出来，从而使得特殊煤种价值最大化。一个针对6种不同特点的不同煤的案例研究被提出来证实这种方法的优点。这种技术为选煤工程师提供了有效、直接、迅速的方法确定不同煤种的最优分选点使之达到最大经济效益。关键词：选煤 电子数据表 最优化1. 前言一般来说印度的煤有灰分高，可选性差的特点，因此不进一步提高品位，它们不适合使用。与此同时，由于劣质煤炭储量的使用和高度机械化在露天型煤矿的使用毛煤质量逐渐恶化。此外，印度政府组织-环境和森林的外交部对使用高灰煤（34%）的发电厂强加限制，使之坐落在1000公里以外，批判污染的地区。所有这些因素都需要采取适当的符合成本效益的一项长期战略，提高煤炭质量洗涤技术。在印度，选煤在1950年早期就用来实现达到要求的煤质量。实际上，当有利可图时才选煤，最佳操作条件选择在洗煤成本的基础上决定。在这项实践中，它立刻意识到该解决方案的成本与质量的问题来自两方面的内容：分别来自技术和经济上的最佳灰分含量的确定。通过最佳的技术条件来增加产品质量，以最大限度地提高选煤厂产量1-8，而经济的最佳状态，与煤炭的价值/价格有关。目前，一些模型和商业软件包，如EPRI的煤炭清洁成本模型9，CCS10，CPO11，Utah-MODSIM 12，由美国开发的基于Excel的成本估计模型12，由美国地质调查局13，SIU-SIM模拟器14和由西部矿山工程提供的Apex开发的基于Excel的成本估计模型都可提供选煤的经济分析。然而，这些软件包都没有为煤炭有用热值的定价结构以及广泛的频繁变化的原煤质量提供多少实用的见解。此外，在实际应用中的洗煤设备大多是不完美的，其分选效果取决于入料的品质，而这些反过来也需要矫正以反映设备的不完美程度。根据所提出的方法，煤的价格结构，不完善的选煤设备和入料质量的变化已实施并可根据工艺要求调整。这里采用的方法根据一个基于电子表格的程序在一个价值结构内最大程度的增大由于设备缺陷和入料质量变化而受到质量限制的煤的价值。这是一个实用的方法，用于确定最佳不同的选煤设备的运行参数，以最大限度地提高煤炭的价值。2.方法2.1根据价格结构确定的最佳分选密度如果煤的样品，其部分灰分含量为x1且价格为Y1卢比每吨，则此煤的质量和灰分含量分别为一个卢比1/y1吨，X1/Y1吨。同样，另一个灰分含量x2和价格Y2的煤，一卢比将有1/y2吨和灰分含量x2/y2吨。这清楚地表明，这两种煤有不同的重量，并含有不同量的灰分，虽然它们的价值是相同的（1卢比）。因此，增加的1/y2-1/y1吨含有x2/y2-x1/y1吨的灰分将没有任何价值。因此，增加而没有价值的临界灰分是(x2/y2x1/y1)/(1/y21/y 1) or（x2y1x1y2）/（y1y2）。从上面的推导可以看出，如果煤中含有临界灰分为（X2Y1-X1Y2）/（Y1-Y2）的煤被添加到另一个煤，后者的值将基本上保持不变，不论它原先的灰分含量。如果含有小于这个临界灰分的煤含量增加了，煤的价值会增加，相反，如果大于临界灰分的煤含量增加了，煤的价值将减少。因此，每一个粒子的煤中含有小于这个临界灰分的，具有一定的价值，应该被回收。因为煤的价值不会被加入包含临界灰分的增量影响，那么如果去掉这样的量它也将不会受到影响，事实上如果这种灰分的增量被删除的话，它确实仍然不会受到影响。因此，分离应该对应于这个临界的灰分，选择在一个特定的密度分离。这个临界的灰分取决于煤炭的价格结构。例如，在印度的非炼焦煤的定价机制是根据如表1中所示分等级。有用的热值（UHV）是以UHV =8900-138（A+M） (1)的经验关系式的基础上计算的，其中，A和M分别为灰分和水分含量。以水分小于2和挥发物含量小于19的煤的为例，有用热值将减少150千卡/公斤，挥发性物质含量每减少1，当会发挥含量在19以下 15。基于此定价机制，印度非炼焦煤的临界灰分含量为（含湿5）59.49，如表2中所示。一个煤样的价格取决于其有用发热量。如果煤样的灰分或水分含量增加一个单位，其有用发热量减少138个单位。水分和灰分含量对煤的发热量有相同的影响。但是在选煤过程中只有精煤的灰分随着灰分物质从可燃物质中选择性分离出来而减少。因此，煤的临界灰分含量取决于其水分含量，如图1所示。但当它的价值最大化的时候，以临界灰分为分级等级的定价结构不再存在。2.2优化方法 在复合选煤厂，原煤被破碎和筛分成几个粒度级。然后每个粒度级用适当的分选工艺分选，由实验室浮沉试验确定操作参数，一切努力都是为了获得最大的精煤产率。这次活动是在一个框架内利用常熟增量灰分的方法处理类似的流程获得最大的精煤价值，不仅实用，而且选煤工程师很容易利用它确定不同煤种的最佳分选点。但在实际应用中，大多数的洗煤设备是不完善的。之所以会发生这种情况，是由于较大部分的密度较低的中煤误进入尾煤流中，而更高密度的中煤误进入精煤流中。更高质量的物质（低灰分）错位降低了有效的增量灰。因此必须做一个修正来修正错位的固体在增量质量的影响。还认识，随着粒径减小基于密度的分选机的效率趋于下降 16。为了克服这一点，更细的颗粒必须在更高的密度点被处理，以保持最佳的产率（质量相同的增量）6,17。在这里，我们按照奥斯本的方法（1988年）对变化的可能错误或Ecart：可能与颗粒大小，设备的尺寸，以及分选密度：Ep= f1f2f3Es (2)其中，f1是一个因素占颗粒大小的变化的Ep，F2是一个因素说明EP与设备的尺寸变化，f3是制造商的担保因素（表3），Es是一个函数代表与每种类型的设备的分离密度的Ep的变化，这相当于0.