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

Abstract

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.