A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. This study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and support vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data. This work assesses the utility of thermal data derived from these models in the computation of kinetic and thermodynamic parameters, alongside an analysis of their statistical performance. Eventually, the RF model exhibits superior physical interpretability and the least discrepancy in predicting kinetic and thermodynamic parameters. Furthermore, a feature importance analysis conducted within the RF model framework quantitatively reveals that temperature and heating rate account for 98.5 % and 1.5 %, respectively.

中文翻译：

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基于热重实验的生物质热解行为预测：Scikit-learn 中典型机器学习回归模型的比较

各种机器学习（ML）模型因其在破译输入和输出之间复杂的非线性关系方面的能力而被广泛用于预测生物质热解，但对于最佳方法仍缺乏共识。本研究详细阐述了三种机器学习方法的开发、优化和评估，即人工神经网络、随机森林 (RF) 和支持向量机，旨在确定使用热重数据准确预测生物质热解行为的最佳模型。这项工作评估了从这些模型得出的热数据在计算动力学和热力学参数中的效用，并分析了它们的统计性能。最终，RF 模型在预测动力学和热力学参数方面表现出卓越的物理可解释性和最小的差异。此外，在RF模型框架内进行的特征重要性分析定量显示，温度和加热速率分别占98.5%和1.5%。