ABSTRACT

This study aims to develop a globally valid prediction model for coal higher heating value (HHV). For the first time, the Gaussian process regression (GPR) method is performed to build the prediction model. For this purpose, a large dataset (as received basis) composed of a wide range of coal ranks is gathered from different geographic locations throughout the world countries in the related literature. The predictor variables for the prediction model include proximate analysis constituents that are moisture, volatile matter, fixed carbon, and ash. Furthermore, multiple linear regression (MLR) method is employed to predict coal HHV. To evaluate the performances of the developed models, the results obtained from each model are compared with each other and the results of the models given in the related literature by prediction performance criteria. The results prove that the prediction capability of the GPR model is superior to the MLR model and the models reported in the literature. For the testing stage, the attained coefficient of determination (R²), mean absolute percentage error (MAPE), root mean square error (RMSE) are 0.9833, 2.5%, 0.7672, respectively. It can be concluded that the proposed GPR model is a powerful tool to achieve high precision coal HHV prediction.

摘要

本研究旨在开发一种全球有效的煤炭高热值 (HHV) 预测模型。首次采用高斯过程回归（GPR）方法建立预测模型。为此，在相关文献中从世界各国的不同地理位置收集了一个由广泛的煤炭等级组成的大型数据集（作为收到的基础）。预测模型的预测变量包括近似分析成分，即水分、挥发性物质、固定碳和灰分。此外，采用多元线性回归（MLR）方法来预测煤HHV。为了评估所开发模型的性能，将从每个模型获得的结果相互比较，并通过预测性能标准将相关文献中给出的模型的结果进行比较。结果证明，GPR模型的预测能力优于MLR模型和文献报道的模型。对于测试阶段，获得的决定系数（R² )、平均绝对百分比误差(MAPE)、均方根误差(RMSE)分别为0.9833、2.5%、0.7672。可以得出结论，所提出的 GPR 模型是实现高精度煤 HHV 预测的有力工具。