ABSTRACT

As one of the most important indicators of coal, calorific value (CV) not only determines the value of coal product, but also has a significant impact on the further processing and utilization of coal. Traditional methods of obtaining prediction data suffer from a range of problems such as too many input variables, low prediction accuracy of a single analysis method and lack of sensitivity analysis of input variables. In this paper, a novel hybrid analysis was presented to predict the CV of coal. Pearson correlation coefficient (PCC) was used to remove correlations between variables in order to provide a suitable combination of input variables for ML models. The results showed that based on the optimal combination of input variables (ash, Fe, Mg and Na), RF model provided a better regression, better fit and better robustness on the testing set than the other three models when the performance indicators and the number of input variables were considered. In addition, sensitivity analyses of input variables showed the relative importance of individual variables and the way in which each variable affects the output variables. The present work provided novel insights and ideas for understanding the prediction of the CV of coal.

摘要

热值（CV）作为煤炭最重要的指标之一，不仅决定着煤炭的产品价值，而且对煤炭的深加工利用有着重要的影响。传统的预测数据获取方法存在输入变量过多、单一分析方法预测精度低、缺乏输入变量敏感性分析等问题。在本文中，提出了一种新的混合分析来预测煤的 CV。Pearson 相关系数 (PCC) 用于消除变量之间的相关性，以便为 ML 模型提供合适的输入变量组合。结果表明，基于输入变量（灰分、铁、镁和钠）的最佳组合，RF 模型提供了更好的回归，在考虑性能指标和输入变量数量的情况下，与其他三个模型相比，在测试集上具有更好的拟合度和鲁棒性。此外，输入变量的敏感性分析显示了各个变量的相对重要性以及每个变量影响输出变量的方式。目前的工作为理解煤炭 CV 的预测提供了新的见解和想法。