当前位置: X-MOL 学术Int. J. Min. Sci. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Development of multiple soft computing models for estimating organic and inorganic constituents in coal
International Journal of Mining Science and Technology ( IF 11.8 ) Pub Date : 2021-03-19 , DOI: 10.1016/j.ijmst.2021.02.003
M. Onifade , A.I. Lawal , J. Abdulsalam , B. Genc , S. Bada , K.O. Said , A.R. Gbadamosi

The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied. However, the combustion characteristics of pulverized coal depend not only on rank but also on the composition, distribution, and combination of the macerals. Unlike the proximate and ultimate analyses, determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise. In 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 models. 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.



中文翻译:

估算煤中有机和无机成分的多种软计算模型的开发

已经对各种有机和无机成分的分布及其对煤燃烧的影响进行了综合研究。但是,煤粉的燃烧特性不仅取决于等级,还取决于黄铁矿的组成,分布和组合。与最接近的分析和最终的分析不同,确定煤中的化学成分需要使用复杂的微观仪器和专业知识。在这项研究中,尝试使用多输入单输出白盒人工神经网络(MISOWB-ANN)从Witbank Coalfields样品中预测黄体(镜质体,惰质体和锂质体)的总量和总矿物质表达式编程(GEP),多元线性回归(MLR)和多元非线性回归(MNLR)。从采用的多个软计算模型中获得的预测模型将使用差异,效率和复合统计指标进行对比,以检验模型的适用性。与其他三个模型相比,MISOWB-ANN提供了更可靠的预测模型,具有最小的差异和最高的效率以及综合统计指标。

更新日期:2021-05-14
down
wechat
bug