煤炭热解过程的热效应是煤炭热解机理研究、反应器设计等过程的关键参数。目前关于煤炭热解热效应的评估方法之间精度差异较大，其中应用最多且可行性最大的方法为基于同步热分析仪(TG-DSC同步联用)来进行测定，但该方法依然对仪器的测量精度依赖较大，且在得到热流曲线之后的热效应计算方法之间精度也存在差异，导致数据的可重复性差，难以适应大规模生产及工艺设计的需要。富油煤含有较多的脂肪侧链、桥键等富氢结构，在热解过程中受热裂解生成更多的焦油，是良好的煤制油原材料。热解工艺的调控是影响富油煤热解所制焦油品质的关键因素，这其中热解过程的热效应又是热解工艺开发的重要参数，因此无论是对富油煤热解还是对广义的煤炭转化来说，都迫切需要开发准确、高效的煤炭热解热效应评估方法。基于富油煤慢速热解实验结果并结合热解反应机理和经验公式，以产物为导向构建富油煤热解反应体系，利用经典热力学分析方法进行反应体系的热力学计算，得到富油煤热解过程的化学反应热，再结合热解过程中的物理吸热量，得到了富油煤中低温慢速热解过程中的热效应。结合已报道的煤炭热解实验结果和热解热效应测定值，利用机器学习方法中的随机森林模型对煤炭热解热效应进行非线性建模和预测。结果表明：基于实验结果，以产物为导向计算得到的富油煤慢速热解热效应数值总体要低于利用TG-DSC同步联用测得的数值，误差均在10%以内。基于机器学习算法预测得到的富油煤热解热效应的预测精度达到0.935 2。综合来看，所构建的2种富油煤热解热效应预测模型具有实用性和适用性。

The thermal effect of coal pyrolysis is a key parameter in the process of coal pyrolysis mechanism research and reactor design. At present, there are a lot of differences in the accuracy of evaluation methods for coal pyrolysis thermal effect. The most widely used and feasible method is based on simultaneous thermal analyzer (TG-DSC synchronous combination). However, this method still relies heavily on the measurement accuracy of the instrument, and there are also some differences in the accuracy of thermal effect calculation methods after the heat flow curve is obtained, resulting in a poor repeatability of the data and difficulty in adapting to the needs of large-scale production and process design. Tar-rich coal is high-quality raw material for coal to tar, it contains more hydrogen-rich structures such as aliphatic side chains and bridge bonds, it could be pyrolyzed to produce more tar during pyrolysis. The regulation of pyrolysis process is the key factor affecting the quality of tar produced by the pyrolysis of tar-rich coal, and the thermal effect of pyrolysis process is an important parameter for selecting pyrolysis process. Therefore, it is urgent to develop an accurate and efficient evaluation method of coal pyrolysis thermal effect for both tar-rich coal pyrolysis and generalized coal conversion. Based on the experimental results for the slow pyrolysis of tar-rich coal, combined with pyrolysis reaction mechanism and empirical formula, the product-oriented pyrolysis reaction system of tar-rich coal was constructed. The chemical reaction heat for the pyrolysis process of tar-rich coal was obtained by the thermodynamic calculation of the reaction system using the classical thermodynamic analysis method. Combined with the physical heat absorption in the pyrolysis process, the thermal effect in the low-temperature slow pyrolysis process of tar-rich coal could be obtained. Then, based on the reported coal pyrolysis experimental results and measured values of pyrolysis thermal effect, the random forest model of machine learning method was used to nonlinearly model and predict the thermal effect of coal pyrolysis. The results showed that based on experimental results, the thermal effect value for slow pyrolysis of tar-rich coal calculated by the product-oriented calculation was generally lower than that measured by TG-DSC synchronous combination, and the error was within 10%. The prediction accuracy for the thermal effect value of tar-rich coal pyrolysis based on machine learning algorithm was 0.935 2. Overall, the two prediction models of thermal effect of tar-rich coal pyrolysis constructed were practical and applicable.