Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R ² analyses, respectively.

煤炭仍然是世界重要的能源。由于使用低等煤，因此干燥是关键问题。有许多尝试开发有效的干燥过程。最突出的方法似乎是热干燥。对于热干燥过程，最重要的主题是煤的水分含量随时间变化。在这项研究中，利用对流干燥实验建立了一个基于决策树回归方法的新模型来预测煤的水分含量。所开发的模型在干燥条件变化的情况下预测即时煤的水分含量时给出了令人满意的结果。决策树深度为6时，MSE和R ²分析的最佳测试结果分别为0.056和0.802 。