Development of multiple soft computing models for estimating organic and inorganic constituents in coal

作     者：M.Onifade A.I.Lawal J.Abdulsalam B.Genc S.Bada K.O.Said A.R.Gbadamosi M.Onifade;A.I.Lawal;J.Abdulsalam;B.Genc;S.Bada;K.O.Said;A.R.Gbadamosi

作者机构：Department of Mining and Metallurgical EngineeringUniversity of NamibiaWindhoekNamibia Department of Mining EngineeringFederal University of TechnologyAkureNigeria DSI/NRF Clean Coal Technology Research GroupFaculty of Engineering and the Built EnvironmentUniversity of the Witwatersrand2050 JohannesburgSouth Africa The School of Mining EngineeringUniversity of the Witwatersrand2050 JohannesburgSouth Africa Mining and Mineral Processing Engineering DepartmentTaita Taveta UniversityVoiKenya School of MiningMetallurgy and Chemical EngineeringUniversity of Johannesburg2006 JohannesburgSouth Africa 

出 版 物：《International Journal of Mining Science and Technology》 (矿业科学技术学报（英文版）)

年 卷 期：2021年第31卷第3期

页      面：483-494页

核心收录：

学科分类：081702[工学-化学工艺] 0709[理学-地质学] 0819[工学-矿业工程] 0808[工学-电气工程] 08[工学] 0817[工学-化学工程与技术] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0807[工学-动力工程及工程热物理] 0815[工学-水利工程] 0813[工学-建筑学] 0814[工学-土木工程] 

主　　题：Multiple soft computing models Coal Organic and inorganic constituents 

摘      要：The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively ***,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the *** the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and *** this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the *** MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.