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干法选煤的现状与发展摘要：本文在阐述了干法选煤的背景和意义的基础上，通过回顾多年来干法选煤技术，回顾了近年来干法选煤技术在理论技术研究和生产实际应用的发展状况，阐述了目前干法选煤技术发展的热点和进展，阐述了干法选煤技术的巨大发展潜力，同时也存在极大挑战.关键词：干法选煤；空气重介流化床；风力选煤；摩擦电选；振动流化床1.前言伴随着近年来环境保护与提高煤炭加工利用率的趋势发展，我国选煤技术不断发展壮大，业内人士普遍认为洁净煤技术的长久发展离不开高效的选煤技术，所以选煤技术作为洁净煤技术长远发展的重要先驱历来受到了各国的高度重视。目前，我国在煤炭利用方面无论是生产还是消耗均位列第一，目前探明的煤炭储量约为45543.1亿吨。煤炭是我国的主要能源，一直以来煤炭的作为我国的能源资源，在一次能源消费中始终占据70%以上，而煤炭在开的采和使用中造成恶劣的环境污染，直接影响我国的生态环境。另外，由于我国煤炭资源总体先天发展不足，高硫煤、高灰分煤种以及难选煤的比重约占35%左右，且70%的煤炭在干旱缺水生态系统脆弱的西部地区，这给传统的湿法选煤技术在这些地方的大规模推广使用带来了极大困难：第一，西部水资源匮乏，而湿法选煤尽管已经实现洗水循环，平均吨煤消耗3-5t的水；第二，随着我国煤矿开采的推进，许多地方正在开采的年轻褐煤种易泥化；第三，湿法选煤不可避免带来了产品水分高，在运输过程中发生冻车卸车困难等实际问题。第四，我国现在推广流行的湿法选煤，如跳汰、重介选煤投资成本大，对于西部较为落后的地区，投资成本高。早在上世纪80年代，我国选煤专家陈清如院士指出我国的能源战略基地将会伴随经济的发展而西移，并就以上在西部发展选煤工业存在的问题提出了发展干法选煤技术的构想，并积极探索研究，并在这一领域有了很多奠定性的建树。通过深入研究矿物物理化学性质，利用它们之间的差异，包括密度和粒度大小的差别、导电性和磁性强弱之分等。2.干法选煤的现状2.1 风力选煤的现状风力选煤技术发展的历史悠久，发展至今已经有近百年历史，同时也是最早提出的干法选煤技术，通过将空气作为类似磁铁矿粉的介质，煤炭颗粒周期变化的运动气流作用下，颗粒按照密度差别实现分离。风力选煤的技术的工艺流程较简单和粗放、在分选过程中无论设备还是基础设施建设都较湿法选煤低1。但是随着采掘技术的发展，原煤在机械化开采过程中的矸石量和粉煤量加大，以及风力选煤本身的分选效率低已经逐渐被淘汰，如，美国在新世纪之初将风力选煤厂已经全部关闭，而俄罗斯的风力选煤厂数目也下降到10%以下。近年来，我国煤炭科学院唐山分院通过多年研究，吸取国外的风力选煤先进技术，根据我国煤炭存在的问题，研制了可用于工业生产易选或中等可选性煤的新技术，研发了FGX系列的风的力选煤设备，主要应用在动力煤排矸等方面，它有以下不可替代特点：（1）不用水。这满足了干法选煤的初衷，为满足干旱缺水地区的选煤厂建设提供保障。（2）投资少。投资费用仅为湿法选煤10%-20%。（3）劳动生产率高。操作简单方面，不需要太多的操作工人。（4）彻底解决了传统湿法选煤的煤泥水处理问题，减少煤泥损失，提高了煤炭利用率，。（5）选后产品煤水分低。分选过程中，风力可以降低产品水分。（6）适应性强。尤其是很多开采出来的泥化强的煤种（7）占地面积小。12.2 空气重介流化床选煤新技术 空气重介流化床干法分选机结构如图1所示。 图一：空气重介质流化床干选机示意图 工作原理：稳定的流化床是分选的最重要条件之一，重介质形成了气-固两相流化状态。原料煤及重介质磁铁矿粉可以由干选机上方单独的入料口分为两条支路内部的流化床层中，由于空气的动力作用，重介质有了类似水等流体的性质，根据阿基米德原理，即密度比重介质平均密度小的精煤上浮至表面，密度较重介质密度大的矸石就可以与精煤分离开来，对排出来的物料只需要通过细筛即可回收绝大部分的磁铁矿粉，极大的减少了脱介环节的投资费用2国际上，关于空气重介流化床研究开始于上世纪六七十年代，而我国从1984年中国矿业大学开始研究有关方面理论，终于在1994年标志着我国成功运行了第一家中国的空气重介干法选煤厂。运转初期，空气重介流化床选煤潜力大大显现，尤其在减少资金投入，高效的生产率，运转可靠性方面取得了显著效果。但随着生产的进行，也出现一些问题。其中最突出额问题是：1.主选机最重要的设备之一布风板经常堵塞，从而造成了整个床层工作不连续，不能连续正常有效地生产；2.处理量始终无法达到设计标准，随着煤炭消耗和处理量的不断激增，设计50t/h的干选厂无法满足后期生产需要；3 处理的粒度范围很窄（主要处理50-6mm粒度范围）。所以对于后期的大型化生产产生很大的阻力。综合分析其主要原因是：原煤水分高，布风板透风孔径无法找到合适的大小，太大的孔径无法适应压缩空气作用，太小的孔径容易堵孔。由于存在诸多设备、原煤等技术难题未能克服，自第一座应用于实际生产中的干选厂之后，很多研究者根据从理论到实践有针对得一直在不断的探索研究，其中很多问题在探索中已经得到了重大突破。3.新型的干法选煤技术 由于干法选煤在未来我国能源战略领域的广泛应用性和技术的巨大潜力，一直以来就有很多的研究者对其研究和开发。而干法分选技术本身作为一个刚刚兴起的新技术和新思想，不可避免的存在很多重大理论问题需要攻克，很多学者也从它的现阶段应用出发研究，同时也在不断开拓新的课题研究。3.1 气固流化床的新发展 对于空气重介流化床干选厂在实际应用中的问题，很多学者孜孜以求，对影响气固两相流化床分选技术的关键因素4个方面，包括a.分选机的结构（布风装置和排料装置）b.操作参数c.介质特性d.原料煤的特性进行了很多的试验研究。韦鲁滨3等另辟蹊径采用振动卸料代替之前的刮板输送重产物，他们采用数学模拟的方法对原料的分选特性与重产物在分选过程中的排料参数进行研究，通过布风板空气大小与振动因子对可能偏差E、工作密度的效率关系的影响。试验结果是空气大小和振动因子通过共同作用，使分许效果发生了同步变化。3.2 振动流化床技术的产生粗颗粒物料无论在湿法选煤中还是在干法的流化床均能收到很好的浮力作用，可以有效的按密度大小进行分层。但对于较细粒物料，由于其本身粒度不大，所以床层对它的浮力作用减弱。杨旭亮5等根据物理化学中的能量引入原理，通过采用外部力作用使整个床体都与激振装置连接，发生受迫振动，床体框架可以和振动源保持一致的幅频特性和波形发生规律性振动。在这个分选过程中，能量可以从床层下部逐渐地通过床体传递到上部煤炭颗粒上，从而引起所有的颗粒发生运动。实验结果表明：振动流化床通过引入外界能量的方式可以有效排除重产物，得到灰分较低的精煤产品。3.3 摩擦电选对微细粒分选早在上世纪80年代，陈清如院士就提出西部煤炭资源就地建设坑口电站，采用干法选煤技术分选块煤，入电厂锅炉采用摩擦电选处理微细粉煤就地将煤炭转化为电能输送到东部沿海地区。基本原理：煤粉在干燥空气吹动下均匀通过摩擦器，煤粒中的无机和有机物交替发生碰撞，带上不同电荷，煤粉中的不同带电量和带电质导致其在正负极电板中产生不同运动形式，从而可以分离矿物。4.结语 干法选煤技术的研究经过许多选煤研究者的多年努力，取得了很多技术的重大突破，但在面临大型化推广方面还有很大的技术障碍，水资源的短缺是中国的问题，也是世界上各个国家面临的普遍难题，随着我国加快能源基地和工业经济发展的西部移动，干法选煤技术的进一步发展在选煤科技工作者的共同努力下会有更加美好的明天。参考文献：1 剧殿臣，康华，林井祥.复合式干法选煤的现状及经济分析 J.煤炭技术，2010,9.2 卫中宽，空气重介流化床干法选煤技术的研究 J.洁净煤技术，2010，16（2）.3 韦鲁滨，梁世红，魏汝晖，新型空气重介流化床分选特性研究 J.中国矿业大学学报，2011,40（5）.4 王伟文，董海红，陈光辉.气固流化床内宽筛分硅粉颗粒流化特性的数值模拟 J.高校化学工程学报，2011,25（2）.5 杨旭亮，赵跃民，骆振福.振动流态化的能量传递机制及对细粒煤的分选研究 J.中国矿业大学学报，2013,42（2）.6 梅雄，章进喜，陈锋.微粉煤的摩擦电选脱灰试验研究 J.煤炭技术，2012,31（1）任务书院（系） 专业年级 学生姓名 任务下达日期： 20xx年 3月 5日设计（论文）日期：20xx年 3 月5日至 20xx年 6月 15日设计（论文）题目： 永夏4.50Mt/a矿区型炼焦煤选煤厂初步设计设计（论文）专题题目：干法选煤的现状和发展设计（论文）主要内容和要求：主要内容：1 完成一座4.50Mt/a矿区型炼焦煤选煤厂的初步设计； 2 撰写一篇专题论文； 3 专题论文翻译。要 求：1 设计内容技术要求入洗原煤为所在矿区一矿和二矿生产的两种毛煤，入洗比例为：一矿：二矿=51.5：48.5；至少有一个精煤产品满足下列要求：灰分Ad=(8.018.50)%；水分Mt12.00%；设计内容包括：资料分析与计算、方案论证、流程计算、设备选型及主厂房工艺布置；相对详细的工程概算；在进行选煤厂总体布局时应适当考虑选煤厂的办公和生活服务设施； 车间布置图不少于5张，设备流程图、数质量流程图和工业广场总平面布置图各1张；均采用计算机绘制。 2 专题论文要求 论文内容必须与设计内容有关； 论文字数在30005000之间； 论文格式满足一般科技文献出版要求。 3 资料翻译完成不少于3000字的规定英文资料翻译；译文要求能够表达原意，语句通顺，文笔流畅。 4 所有设计文件提供电子文档一份。 5 提交设计说明书、概算书、专题论文及专英翻译合订本一册院长（系主任）签字： 指导教师签字：Coal preparation plant optimization: A critical review of theexisting methodsV. Gupta1, M.K. Mohanty*Department of Mining and Mineral Resources Engineering, Southern Illinois University at Carbondale, Carbondale,Illinois 62901-6603, United StatesReceived 19 July 2005; received in revised form 13 November 2005; accepted 15 November 2005Available online 4 January 2006AbstractA coal preparation plant typically operates with multiple cleaning circuits to clean individual size fractions of run-of-mine coal.Coal preparation plants are traditionally optimized using the equalization of incremental product quality approach. Individualcleaning circuits are operated at the same specific incremental product quality so that the targeted overall plant product quality isachieved. Over the years, it has been well established that equal incremental product quality approach maximizes plant-yield for agiven product quality constraint.However, while dealing with multiple quality constraints, the incremental quality approach may not provide a completesolution to the optimization problem. It may be intuitive to realize that the dirtiest particle(s) in a coal product, with respect toash content, may not be the same particle(s) with respect to sulfur content. Therefore, with increasing number of product qualityconstraints, which may include (but not limited to) limiting ash, sulfur and trace element contents, the plant has to be optimizedbased on each incremental product quality. Understandably, the operating points selected for each circuit to maximize plant-yieldbased on incremental ash content, may not be suitable for obtaining maximum plant yield based on incremental sulfur content.These limitations of the equalization of incremental product quality approach to satisfy multiple product quality constraints havebeen reviewed in detail in this publication with an example of ash and sulfur data collected from an operating coal preparationplant.D 2005 Elsevier B.V. All rights reserved.Keywords: coal; plant optimization; incremental product quality; coal washability1. IntroductionBased on the size consist of the Run-of-mine (ROM)coal, a preparation plant utilizes three or four individualcircuits to clean the entire ROM coal. For example, coalcoarser than 12.5 mm may be cleaned in a heavymedium vessel circuit, 12.5?1 mm in a heavy mediumcyclone circuit, 1 mm?150 Am in a spiral circuit andminus 150 Am size coal in a flotation circuit. Typically,product quality measure such as ash content from eachcircuit is maintained at nearly the same level as thetarget ash content for the overall plant. In other words,if a plant contract requires product specifications of 8%ash, the operating conditions in the individual circuitsare adjusted so that the ash contents of the individualcircuit products are approximately 8%. Although thisapproach of producing equal average product quality0301-7516/$ - see front matter D 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.minpro.2005.11.006* Corresponding author. Tel.: +1 618 453 7910; fax: +1 618 4537455.E-mail address: mohanty (M.K. Mohanty).1Tel.: +1 618 453 7910; fax: +1 618 453 7455.Int. J. Miner. Process. 79 (2006) 917/locate/ijminprofrom each circuit provides a simplistic solution to sa-tisfy contract specifications, it does not guarantee themaximum possible plant yield.Incremental product quality concept is commonlyused to maximize plant yield for a given quality con-straint. By definition, incremental product quality refersto the quality of the dirtiest particle(s) present in anycoal product, whereas the average product quality refersto the overall quality of the composite coal product.Numerous studies have been conducted in the past todevelop suitable procedures for maximizing overallplant yield while satisfying a desired average productquality. Sarkar et al. (1960) suggested a graphical ap-proach for maximization of yield of composite cleancoal at a desired ash content. It was suggested thatcleaning of coarser coal at a higher ash content andfiner coal at a relatively lower ash content gives themaximum yield while satisfying the given product ashconstraint. Walters and Ramani (1976) developed acomputer based plant optimization model in whichthe separating gravity of the small coal is held constantwhile the separating gravity of the coarse coal is incre-mented until the desired product quality is achieved.Abott (1982) derived an equation to prove that theoptimum conditions for maximum profit from a blendof coal produced by two different cleaning processesoccurs when the instantaneous ash (incremental ash)contents of both clean products are equal. Salama(1986, 1991 and 1998) and King (1999) developedgraphical and numerical techniques to optimize theyield of a plant at a given product quality constraint.Graphical methods were based on the Mayer curve (M-curve) to determine the optimum cut points of separa-tion which maximized the plant yield at a given productash content. Rayner (1987) also utilized the graphicaltechnique of plotting M-curve for yield maximizationof a plant at a given ash constraint. Romberg (1990)developed the optimization software, COALTROL, uti-lizing the M-curve for yield maximization. Thesegraphical approaches have limitation since complexityof a coal preparation plant increases with an increase inthe number of cleaning circuits. Salama (1986) verifiedmathematically that equalization of incremental ashgives the optimum yield at a given product ash con-straint. Lyman (1993) developed a computationalmodel based on the incremental ash approach foryield maximization in a coal preparation plant. Subse-quently, Salama and Mikhail (1994) also developed asimulation model for plant yield maximization based onthe incremental ash approach. Sen et al. (1994) deriveda mathematical solution utilizing a Lagrangian functionfor cleaning coal obtained from multiple sources andarrived at the same conclusion, which recommendedthe equalization of incremental product quality toachieve optimum plant yield. Luttrell et al. (2003,2004) also arrived at the same conclusion that theincremental ash approach gives the maximum yieldwhile satisfying a given ash constraint. By showing adirect relationship between the ash content of a coalparticle and its density, Luttrell et al. (2003, 2004)claimed that the plant performance could be optimizedby operating the plants at the same specific gravity cut-point. Although, from their plot shown in Fig. 1, a goodcorrelation between ash content and specific gravity isquite evident the data scatter may very well be resultingdue to varying composition of the coal-ash, whichmight have been overlooked by the investigators. Asimple analysis of coal-ash may indicate the presenceof a variety of minerals, including SiO2, Al2O3, Fe2O3,CaO, K2O and others. Although SiO2, having a specificgravity of 2.65, constitutes more than 50% of the totalash material, the concentration of Al2O3(SG: 3.9 to4.1) and Fe2O3(SG: 5.0 to 6.0) in coal-ash is also quitesignificant. In some coal obtained from Illinois No. 6seam, the combined concentration of these two miner-als (Al2O3and Fe2O3) is as high as 30% (PHYLISSdata Base, 2005). Understandably, the presence of dif-ferent proportions of these minerals in a coal wouldallow specific coal or specific size fractions of a coal tohave different density even when their ash contents arethe same and vice-versa.Almost all of the past investigators discussed plantoptimization with single product quality constraintwith the exception of Honaker et al. (1997), wholooked into multiple product quality constraints, al-though not simultaneously. The equalization of incre-100806040200Plus 1-1/41-1/4x3/43/4x1/41/4x28M28x48M48x100M8.0%04.5%7.5%0.10.2Ash Content (%)1 / Specific Gravity0.9Fig. 1. Relationship between specific gravity and ash content forvarious size fractions of a run-of-mine coal (Luttrell et al., 2003,2004).V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91710mental product quality approach was utilized byHonaker et al. to maximize the plant yield valueson the basis of incremental ash, incremental sulfurand incremental trace element contents in succession.However, no suitable method was prescribed to max-imize the plant yield while simultaneously satisfyingmultiple quality constraints as indicated in their plotsshown in Fig. 2. The maximized plant yield valuesshown in Fig. 2 (a) over a range of product ashcontents appear to be the same yield values shownover a range of ash and sulfur contents in Fig. 2 (b).It appears from these plots that the maximized yieldvalues were determined by equalizing incremental ashcontents in individual plant circuits followed by thedetermination of the average ash and sulfur contentsfor the overall plant product at those operating points.In other words, the maximized yield values obtainedfrom the equalization of incremental sulfur content ofindividual circuits was not given any consideration inthe plot shown in Fig. 2 (b).The present study investigates the limitations ofdealing with multiple product quality constraints dur-ing plant optimization in greater details. Actual ashand sulfur data obtained from the tests conducted foreach unit operations of a four-circuit coal preparationplant have been utilized as example to illustrate thelimitations of the incremental product quality ap-proach. Consequently, the study recommends the ne-cessity of a better optimization method to maximizeplant yield especially to satisfy multiple product qual-ity constraints.2. ExperimentalA four-circuit plant investigated in this study usesheavy-medium to clean all of the coal coarser than 1mm. A heavy medium vessel (HMV) is utilized to cleanthe plus 16 mm size fraction along with a heavy medi-um cyclone (HMC) cleaning the 16?1 mm size frac-tion of the run-of-mine coal. The 1 mm?150 Am andminus 150 Am size coal fractions are cleaned usingbanks of 3-start spirals and froth flotation cells, respec-tively. The washability data of feed reporting to all threedensity based separation circuits, i.e., HMV, HMC andSpirals are provided in Table 1 along with the flotationkinetic data for the flotation circuit feed. As it is shown,the heaviest fraction (2.0 sink) in feed is significantlyhigher for HMC unit than the other two density basedseparators. This indicates that the expected productyields from HMC would be significantly lower thanHMV and spiral.Characteristic partition curves were fitted to theperformance data obtained from at least five actualtests conducted in each cleaning circuit by varyingthe key operating condition. Medium density was var-ied in case of HMVand HMC, whereas splitter positionand froth height were varied for the spirals and flotationcells, respectively. However, apparently due to fluctua-tions in the plant feed along with possible samplingerrors, the flotation tests did not produce any meaning-ful data. Hence, it was decided to simulate the flotationperformance based on laboratory flotation kinetic anal-ysis of the sample of actual feed slurry reporting to theflotation cells in the plant.3. Results and discussionThemodifiedLynchequation(Eq.(1) wassuccessfully fitted to the normalized partition data908580757065605550Conventional YieldOptimized YieldAdvanced Circuit IAdvanced Circuit II456Clean coal yield (%)Product Ash (%)789101112138580757065Conventional YieldOptimized YieldAdvanced Circuit IAdvanced Circuit II(5, 0.97)(6, 1.06)(7, 1.11)(7.5, 1.18)(8, 1.12)(8.5, 1.12)(9, 1.12)(10, 1.12)Clean coal yield (%)Multiple Constraint Points(Ash%, Sulfur%)(b)(a)Fig. 2. Maximized plant yield values obtained from an optimizationmodel while satisfying (a) one quality (ash) constraint and (b) twoquality (ash and sulfur) constraints (Honaker et al., 1997).V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91711obtained from the tests conducted for HMV, HMC andcoal-spirals.PN float eax? 1eax ea? 21wherexnormalized specific gravity (mean specific grav-ity/specific gravity of separation)afitting constant.Although the nature of the model equation remainedthe same, the fitting constant baQ was different for allthree density based separators. Using these model equa-tions, characteristic yieldash and yieldsulfur datapoints were generated using the washability data(Table 1) of feed coal reporting to each cleaning circuitat very close separation density (D50) intervals. Thesedata were utilized to calculate the incremental productash and product sulfur contents corresponding to eachseparation density using the following equation:IGj1Yj1Gj1? YjGjYj1? Yj2where,Yj, Gjyield and grade at jth density cut point or sepa-ration densityYj+1, Gj+1yield and grade at the next higher, i.e.,(j+1)th density cut pointIGj+1incremental grade at (j+1)th density cut point.Pursuing the traditional optimization approach, theincremental product quality was equalized from all fourcleaning circuits to maximize the plant yield. A simpleillustration of the approach is provided in Fig. 3 (a), (b)and (c). As shown in Fig. 3 (a), the incremental ashcontent was equalized at an arbitrary value of 22% toproduce mass yields of 63.84%, 42.70%, 67.05% and47.42% from HMV, HMC, spiral and flotation circuits,respectively. The corresponding average product ashcontents of 7.03, 7.12, 4.71 and 9.97 for the respectivecleaning circuits were determined from the yieldashcurves for each circuit as shown in Fig. 3 (b). Subse-quently, the overall plant yield and plant product ashcontents of 54.67% and 6.73% were calculated as theweighted average of individual circuit yield and productash contents. If the desired plant product ash content isalso 6.73%, then the maximum plant yield achievablewould be 54.67%. The expected plant product sulfurcontent of 1.15% would be determined as the weightedaverage of the individual circuit sulfur contents asillustrated in Fig. 3 (c). However, if the desired productash content was different from 6.73%, then similariterations were carried out at lower (if the desiredproduct ash is lower than 6.73%) or higher (if theTable 1Washability data for the three density based cleaning circuits and the flotation kinetics data for the flotation cleaning circuit of a 4-circuit plantstudied during this investigationHeavy media vesselHeavy media cycloneSp. gr.Weight (%)Ash (%)Sulfur (%)Sp. gr.Weight (%)Ash (%)Sulfur (%)82.750.801.151.2501.251.332.764.480.901.251.311.358.027.018.751.0013.172.30215.223.4619.513.671.51.6250.561.6251.8620.722.801.6251.91.5639.473.031.6252.02.7958.142.871.92.834.3293.620.622.02.853.9584.280.40Total100.037.140.94Total100.050.240.88SpiralFlotationSp. gr.Weight (%)Ash (%)Sulfur (%)Time (s)Weight (%)Ash (%)Sulfur (%)1.151.3700.000.000.001.31.414.395.361.063030.688.310.90815.521.53609.080.900.901.51.6252.1719.202.309047.379.960.901.6252.03.4463.227.8312050.4311.060.902.02.830.5194.631.5515052.2712.050.89Total100.033.881.4021055.4014.360.88Tails100.0046.820.68V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91712desired product ash is higher than 6.73%) incrementalash levels. These iterative steps were continued until thecalculated plant product ash converged to the desiredproduct ash level. The maximum plant yield was cal-culated as the weighted average of individual circuityieldsobtainedattheincrementalashcontent(b)01020304050607080901000102030405060708090100Product Ash (%)Mass Yield (%)Mass Yield (%)(c)Product Sulfur (%)Heavy Medium VesselHeavy Medium CycloneSpiralFroth Flotation(a)02040608010001020304050607005101520253040350.80.98090Incremental Ash (%)Yield (%)Heavy Medium VesselHeavy Medium CycloneSpiralFroth FlotationHeavy Medium VesselHeavy Medium CycloneSpiralFroth FlotationFig. 3. A graphical illustration of determining maximum yield by equalizing incremental ash from each cleaning circuit of a coal preparation plant.V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91713corresponding to the desired overall plant product ashcontent.The incremental product quality approach has beenproved to be an excellent plant optimization approachto maximize clean coal yield, while dealing with onlyone quality constraint, i.e., product ash. However, thisapproach becomes increasingly complex and also maylead to erroneous conclusions if more than one productquality constraints have to be satisfied simultaneously.Incremental product ash, which is also referred to as theinstantaneous ash content that cannot be directly mea-sured, since it is the ash content of a material with asingle precise density (Abott and Miles, 1990). Practi-cally, elementary ash content of a very close densityfraction of a material is the best approximation ofinstantaneous ash. Elementary quality of a product isreferred to the grade of its dirtiest particle or particle(s)with respect to the specific quality being considered.Based on the fundamental principle behind the incre-mental product quality approach, it may be intuitive toexpect that the maximum yieldgrade curve (for exam-ple, yieldash curve) generated for an overall plant willbe at its best when generated by equalizing the specificincremental quality (incremental ash). It is also quiteunderstandable that the dirtiest particle(s) with respectto one assay (for example, ash content) is not necessar-ily the same particle(s) with respect to another assay(for example, sulfur content). Therefore, the maximumoverall plant yield determined by equalizing the incre-mental ash may be different from the maximum plantyield determined by equalizing incremental sulfur con-tent obtained from each circuit. Even in case, the max-imum plant yields obtained from both approaches areequal, the combinations of individual circuit yieldscausing this may be completely different. Thus, main-taining the required quality of the overall plant productwith respect to both ash and sulfur will be difficult ifone tries to achieve the plant yield maximized on thebasis of both incremental ash and incremental sulfurcontents. This phenomenon is further illustrated forclarification using the plant-data obtained from this in-vestigation. As explained previously, the example plantoperates with 4 individual circuits, i.e., heavy mediumvessel (HMV), heavy medium cyclone (HMC), spiraland flotation to treat different size fractions, which con-stitute 39.5%, 43.2%, 14.3% and 3.0% of the run-of-mine coal, respectively. As indicated in the last row ofTable 2, it can be shown that a maximum plant yield of53.4%isachievableataproductashcontentof6.68%byequalizing the incremental ash content of each circuit at16%. However, as shown in Table 3, if the plant yield ismaximized on the basis of an incremental sulfur contentof 2.05% from each circuit, a plant yield of 53.4% willalso result at a sulfur content of 1.10% for the plantproduct. Thus, it may be erroneously concluded that amaximum plant yield of 53.4% is achievable whileTable 2Selected data showing the yieldash and yieldsulfur relationships obtained by equalizing the incremental ash content of each cleaning circuitIncrementalash (%)Heavy medium vesselHeavy medium cycloneSpiralFlotationOverall plantYield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)1161.346.701.0438.096.111.1962.844.071.0041.279.040.9050.916.101.081261.966.751.0539.196.271.2163.964.201.0142.409.120.9051.826.201.091362.386.791.0539.846.371.2364.764.311.0052.416.281.101462.686.831.0640.286.451.2465.374.391.0243.959.280.9052.836.331.111562.916.861.0640.616.521.2565.834.471.0244.509.360.9053.146.391.111663.106.881.0640.896.591.2666.184.531.0345.049.440.9053.406.431.121763.266.911.0641.156.651.2766.444.571.0345.509.530.9053.636.481.121863.406.931.0741.406.721.2866.624.611.0345.959.610.9053.846.521.131963.526.961.0741.706.801.2966.774.641.0346.349.700.9054.046.571.132063.646.981.0742.086.921.3166.884.671.0446.739.790.9054.286.631.142163.747.001.0742.487.051.3266.984.691.0447.089.880.9054.526.691.142263.847.031.0742.707.121.3367.054.711.0447.429.970.9054.676.731.152363.947.051.0742.817.171.3367.124.731.0447.7410.060.9054.786.761.152464.037.071.0842.897.201.3367.184.751.0448.0410.150.9054.876.791.1525842.957.221.3467.234.771.0548.3410.240.9054.946.811.1526842.997.241.3467.284.781.0548.6210.340.9055.016.841.162764.287.151.0843.027.261.3467.324.801.0548.8910.440.9055.076.861.162864.367.181.0843.057.271.3467.364.811.0549.1510.530.9055.136.881.162964.447.201.0843.087.281.3467.404.831.0549.4010.630.9055.186.911.163064.527.231.0943.107.301.3467.434.841.0549.6510.730.9055.246.931.16V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91714satisfying an overall plant product quality of 6.68% ashand 1.10% sulfur. However, further examination of thedata in Table 2 reveals that while optimizing the plant byequalizing the incremental ash content from each circuit,the maximum plant yield value of 53.4% was obtaineddue to the individual circuit yields of 63.10%, 40.89%,66.18%and45.04%,respectively.Ontheotherhand,thedata in Table 3 indicate that the same maximum yield of53.40%, obtained by equalizing the incremental sulfur,was a result of the individual circuit yields of 64.21%,39.01%,66.55%and55.40%,respectively.Sincethissetof yield values is completely different from the previousone, a more appropriate conclusion is that a maximumplant yield of 53.4% can be obtained while satisfyingeither a product ash content of 6.68% or a product sulfurcontent of 1.10% but not both.The product ash versus maximum plant yield rela-tionship generated for an overall plant will be a bettercurve when it is generated by equalizing the incremen-tal ash content than by equalizing the incremental sulfurcontent of each cleaning circuit. This phenomenon isillustrated by the plots shown in Fig. 4 (a) and (b),which were generated by equalizing incremental ashand incremental sulfur contents of four cleaning circuitsof an operating coal preparation plant. Clearly, theproduct ash versus maximum plant yield curve gener-ated by equalizing incremental ash content of eachcircuit is better than the one developed by equalizingincremental sulfur content. Similarly, the product sulfurversus maximum plant yield relationship is better whendeveloped by equalizing the incremental sulfur contentof each circuit. While dealing with large number ofquality constraints, which may include various traceelements, several sets of product qualitymaximumplant yield relationship will be obtained. This mayrequire another search technique to identify the globalmaximum yield for the plant while satisfying all qualityconstraints.If multiple quality constraints can somehow be com-bined to form one constraint, it appears that the equal-ization of incremental product quality approach may beuseful in maximizing clean coal yield from a plant. Forexample, ash and sulfur contents can be combined toform a new constraint, known as sulfur dioxide emis-sion potential, represented by lb/Million Btu or kg/Billion Joule. The numerator of this new constraint isa function of the sulfur content, whereas the denomi-nator is directly correlated to the ash content. Thus,based on the desired ash and sulfur contents in theproduct, the target SO2emission potential may becalculated and then the iterative optimization processcan be initiated by equalizing the incremental SO2emission potential obtained from the individual clean-ing circuits until the target SO2emission potential ofthe overall plant product is reached. However, thisapproach has limited application due to the fact that itmay not always be possible to generate a single qualityconstraint by combining a variety of the required prod-uct constraints, which may include the contents of var-ious trace elements present in a coal. In addition, even ifit is possible to generate single quality constraint as afunction of all desired individual quality constraints, itmay still lead to erroneous conclusions. For a simpleexample, let us suppose that a coal preparation plant isrequired to meet a product specification defined by aminimum heating value of 12,000 Btu/lb (27,912 kJ/kg)and a maximum sulfur content of 1.5%. By combiningthese two assays, we may obtain 2.5 lb SO2/MBtuTable 3Selected data set showing the yieldash and yieldsulfur relationships obtained by equalizing the incremental sulfur content of each cleaning circuitIncrementalsulfur (%)Heavy medium vesselHeavy medium cycloneSpiralFlotationOverall plantYield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)Yield(%)Ash(%)Sulfur(%)1.4559.996.621.0333.385.591.1263.774.180.9942.829.160.9048.525.921.051.5060.566.661.0434.045.651.13950.0010.880.9049.316.031.051.5561.056.691.0434.655.711.1364.614.291.0053.0812.560.8949.926.141.061.6061.526.731.0435.255.771.1464.974.341.0054.4913.600.8850.456.221.061.6561.946.771.0535.875.841.1565.314.391.0055.0814.080.8850.966.291.071.7062.256.801.0536.355.901.1665.554.421.0155.2614.240.8851.326.331.071.7562.546.841.0536.855.961.1765.784.461.0155.3414.310.8851.696.371.081.8062.816.871.0637.316.011.1765.974.491.0155.3814.340.8852.026.411.081.8563.026.911.0637.716.061.1866.114.521.0155.3914.350.8852.306.451.081.9063.256.951.06966.264.541.0255.3914.350.8852.596.491.091.9563.487.011.0738.476.161.2066.384.561.0255.4014.360.8852.856.531.092.0063.717.071.0738.766.201.2066.484.581.0255.4014.360.8853.076.581.102.05839.016.241.2166.554.601.0255.4014.360.8853.396.681.10V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91715(1.075 kg/Billion Joule) as the single constraint andfollow the optimization routine to maximize the plantyield while satisfying the overall plant quality target of2.5 lb SO2/Mbtu (1.075 kg/Billion Joule). However, itmust be realized that this target SO2emission potentialcan be obtained by also achieving a maximum sulfurcontent of 1.25% and minimum heating value of10,000 Btu/lb (23,260 kJ/kg), which is differentfrom the aforementioned actual targets. Therefore,combining many constraints to one new constraintmay not necessarily provide the correct solution.In addition, incremental quality approach of plantoptimization becomes increasingly complex and mayresult in erroneous conclusion when the yieldgraderelationship from individual circuit does not follow aregular increasing or decreasing trend. Although, yieldash relationship for many North American coals typi-cally shows an increasing trend, the yieldsulfur rela-tionship in many cases shows a complete reversal of theinitial increasing trend and turns into a decreasing trendafter certain point. This type of yieldsulfur trend,which is similar to that of the example plant, willshow two different yield values corresponding to onesulfur content. This phenomenon will add significantcomplexities to the iterative approach used in determin-ing the yieldsulfur and yieldash relationships for theentire plant by combining the respective values fromindividual circuits.Furthermore, to optimize the plant for a variety ofproduct ash contents or sulfur contents, a wide range ofincremental quality values have to be common to eachcleaning circuit product. However, at times for sulfurassays, this range is narrow to almost none, as shownin Fig. 5. Hence, the optimal yield could be determinedfor a limited set of product qualities or sometimes forno set of product quality using the incremental qualityapproach.In light of the above discussion on the limitations ofthe incremental product quality approach, the authorsstrongly believe that there is a definite need for otheroptimization algorithms that could determine the opti-mal yield of the plant while satisfying the multipleproduct quality constraints simultaneously. The authorswill report one such approach in a future publication.4. ConclusionsEqualization of incremental product quality has beena well accepted method for achieving maximum plantyield since in many cases product ash is the onlyimportant product constraint to be satisfied. However,this approach has limitations while trying to satisfymore than one important quality constraint. These lim-itations have been discussed in detail by using example0102030405060708090100024613578Incremental Sulfur (%)Mass Yield (%)Heavy Medium VesselHeavy Medium CycloneSpiralFrothFlotationFig. 5. An illustration of dissimilar trends in incremental sulfur andmaximum plant yield relationships.(a)(b)4849505152535455564849505152535455565.506.006.507.007.50Product Ash (%)Product Sulfur (%)Plant Yield (%)Plant Yield (%)Incremental AshIncremental Sulfur1.041.061.041.161.18Incremental SulfurIncremental AshFig. 4. Yieldgrade relationships generated with respect to two incre-mental product qualities.V. Gupta, M.K. Mohanty / Int. J. Miner. Process. 79 (2006) 91716plant data sets in this study. Since the dirtiest particle(s)with respect to ash assay is most likely different fromthat with respect to another assay (for example, sulfur),it is understandable to obtain different maximum yieldvalues while trying to maximize plant yield valuesusing equalization of incremental product ash and sul-fur contents independently. It may be intuitive to expectthe yieldash relationship to be better when obtained byequalizing incremental ash content, whereas the yieldsulfur relationship to be better when obtained by equal-izing incremental sulfur content. If in case the maxi-mum plant yields obtained for both approaches areequal, it may result due to completely different combi-nations of individual circuit yields. In other words, theoperating conditions selected for each cleaning circuitto maximize plant yield on the basis of incremental ashand sulfur will be different from each other. Thus, withincreasing number of product quality constra