预测生物质热解动力学参数的随机森林模型

基于大量已发表的生物质热解实验数据，采用数值方法拟合全局反应热解模型的动力学参数，建立生物质热解的训练和验证数据库，并利用随机森林算法研究生物质热解动力学参数与生物质种类和加热条件之间的非线性关系，发展预测生物质热解动力学参数的随机森林模型. 训练和验证的结果显示：随机森林模型能够较好地预测训练数据库中的生物质热解的动力学参数(R²>0.92)，并能够准确预测验证数据库中的多种生物质的热解过程(R²>0.93). 此外，变量重要性分析结果显示：纤维素质量分数对于反应级数和活化能影响较大，木质素对于反应级数的影响最大. 加热条件对于活化能的影响可以忽略，但是对指前因子和反应级数的影响显著.

Random forest model for predicting kinetic parameters of biomass devolatilization

Two biomass devolatilization databases, including the training and validation databases, were constructed from diverse available experiments in literature. The kinetic parameters were fitted under the framework of order-based global biomass devolatilization model. The random forest (RF) method was employed to investigate the complex nonlinear correlations between the kinetic parameters with chemical compositions and heating condition, in order to develop the RF model to accurately predict the kinetic parameters of biomass devolatilization based on its chemical compositions and heating condition. The training and validation results show that the RF model can well predict the kinetic parameters of different biomass types under different heating rates (determination coefficient R²>0.92), also with an accurate prediction of the biomass devolatilization process (R²>0.93). The variable importance measurement (VIM) results show that the fraction of cellulose (CL) has significant effect on the reaction order and activation energy, and the fraction of lignin (LIG) has the maximum effect on the reaction order. The effect of heating rate is negligible for activation energy, but considerable for the frequency factor and reaction order.