The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R²) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks.

中文翻译：

---

生物质热解动力学的预测：基于人工智能的建模的新见解

目前的工作引入了一种基于定量结构-性能关系 (QSPR) 的随机梯度提升 (SGB) 决策树框架，用于模拟和捕获生物质的热分解动力学，并考虑最终分析的有效参数（如碳、氢、氧、氮和硫含量）和过程加热速率。通过总共 149 个热解动力学，本研究开发了一个人工模型，并对其进行了训练和测试阶段。使用误差分析、灵敏度、回归和异常值检测对所提出的模型进行了验证。决定系数 ( R ²) 和平均相对误差 (%MRE) 分别计算为 0.993 和 4.354%，表明热解动力学参数的估计性能良好。此外，灵敏度结果表明过程加热速率对模型输出的影响最大，相关因子为 0.43。最终，与早期框架相比，所提出的模型表现出优越的性能。