数字农科院2.0

Coupling Kinetic Modeling with Artificial Neural Networks to Predict the Kinetic Parameters of Pine Needle Pyrolysis

文献类型： 外文期刊

作者： Langui Xu;Lin Zhang;Xiangjun He;Wenbin He;Ziyong Wang;Weihua Niu;Dong Wei;Yi Ran;Wendan Wu;Mingjun Cheng;Jundou Liu;Ruyi Huang

作者机构：

关键词： Artificial neural networks;Kinetic model;Pine needle pyrolysis;Prediction

期刊名称： BioResources

ISSN： 1930-2126

年卷期： 2024 年 19 卷 4 期

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： The pyrolysis behavior of biomass is critical for industrial process design, yet the complexity of pyrolysis models makes this task challenging. This paper introduces an innovative hybrid model to quantify the pyrolysis potential of pine needles, predicting the entire process of their pyrolysis behavior. Through experimental analyses and kinetic parameter calculations of pine needle pyrolysis, the study employs a kinetic model with a chemical reaction mechanism. Additionally, it introduces an improved dung beetle optimization algorithm to accurately capture the primary trends in pine needle pyrolysis. The developed artificial neural network model incorporates meta-heuristic algorithms to address process error factors. Validation is based on experimental data from TG at three different heating rates. The results demonstrate that the hybrid model exhibits strong predictive performance compared to the standalone model, with coefficients of determination (R²) of 0.9999 and 0.999 for predicting the conversion degree and conversion rate of untrained data, respectively. Additionally, the standard errors of prediction (SEP) are 0.249% and 0.449% for predicting the conversion degree and conversion rate of untrained data, respectively.

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