ABSTRACT

The determination of gross calorific value (GCV) of solid fuel is important because GCV is frequently required in the design of most combustion and other thermal systems. However, experimental determination of GCV is time-consuming, which necessitated the development of different empirical equations to estimate GCV using the elemental composition of the solid fuels. With the growing popularity of empirical equations for estimation of GCV of solid fuels, there is a need to develop reliable and suitable models for the prediction of GCV of coal from the South African coalfields (SAC). In this study, empirical models were developed to determine the relationship between the proximate analysis of coal with its GCV, using soft computing and regression analyses. A total of 32 coal samples were used to develop three empirical models based on soft computing techniques, namely; adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN), and regression analysis using multilinear regression (MLR). The performances of the proposed models were evaluated using coefficient of determination (R²), mean absolute percentage error (MAPE), mean squared error (MSE) and variance accounted for (VAF). The R², MAPE, MSE and VAF for the ANFIS are 99.92%, 2.0395%, 0.0778 and 99.918% while for the ANN, they are 99.71%, 2.863%, 0.2834 and 99.703%. The R², MAPE, MSE and VAF for the MLR are 99.46%, 3.551%, 0.5127 and 99.460%. From the soft computing and regression analysis studies conducted, the ANFIS was found as the most suitable model for predicting the GCV for these coal samples.

摘要

固体燃料的总热值 (GCV) 的确定很重要，因为在大多数燃烧和其他热系统的设计中经常需要 GCV。然而，GCV 的实验确定是耗时的，这需要开发不同的经验方程来使用固体燃料的元素组成来估计 GCV。随着用于估计固体燃料 GCV 的经验方程的日益普及，需要开发可靠且合适的模型来预测来自南非煤田 (SAC) 的煤的 GCV。在这项研究中，使用软计算和回归分析，开发了经验模型来确定煤的近似分析与其 GCV 之间的关系。基于软计算技术，共使用 32 个煤样开发了三个经验模型，即：自适应神经模糊推理系统 (ANFIS)、人工神经网络 (ANN) 和使用多线性回归 (MLR) 的回归分析。使用确定系数（R² )、平均绝对百分比误差 (MAPE)、均方误差 (MSE) 和占方差 (VAF)。ANFIS的 R ²、MAPE、MSE 和 VAF 分别为 99.92%、2.0395%、0.0778 和 99.918%，而 ANN 的 R 2 、MAPE、MSE 和 VAF 分别为 99.71%、2.863%、0.2834 和 99.703%。MLR的R ²、MAPE、MSE和VAF分别为99.46%、3.551%、0.5127和99.460%。从进行的软计算和回归分析研究中，发现 ANFIS 是预测这些煤样 GCV 的最合适模型。