Coupling Kinetic Modeling with Artificial Neural Networks to Predict the Kinetic Parameters of Pine Needle Pyrolysis
Langui Xu,
Lin Zhang,
Xiangjun He,
Wenbin He,
Ziyong Wang,
Weihua Niu,
Dong Wei,
Yi Ran,
Wendan Wu,
Mingjun Cheng,
Jundou Liu,
Ruyi Huang
Affiliations
Langui Xu
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Lin Zhang
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Xiangjun He
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Wenbin He
School of Mechanical Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
Ziyong Wang
Henan ALST New Energy Technology Co. LTD, Zhengzhou 450001, China
Weihua Niu
Zhengzhou Yuzhong Energy Co., LTD, Zhengzhou, China
Dong Wei
Zhengzhou Yuzhong Energy Co., LTD, Zhengzhou, China
Yi Ran
Biogas Institute of Ministry of Agriculture and Rural Affairs, Key Laboratory of Development and Application of Rural Renewable Energy, Ministry of Agriculture and Rural Affairs, Chengdu 610041, China
Wendan Wu
Sichuan Pratacultural Technology Research and Extension Center Chengdu 610041, China
Mingjun Cheng
Sichuan Pratacultural Technology Research and Extension Center Chengdu 610041, China
Jundou Liu
Sichuan Agricultural Planning and Construction Service Center, Chengdu 610041, China
Ruyi Huang
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China; Biogas Institute of Ministry of Agriculture and Rural Affairs, Key Laboratory of Development and Application of Rural Renewable Energy, Ministry of Agriculture and Rural Affairs, Chengdu 610041, China
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.
