Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries. (December 2022)

• Record Type: Journal Article
• Authors: Yang, Yadong; Shahbeik, Hossein; Shafizadeh, Alireza; Masoudnia, Nima; Rafiee, Shahin; Zhang, Yijia; Pan, Junting; Tabatabaei, Meisam; Aghbashlo, Mortaza
• Is Part Of: Renewable energy. Volume 201(2022)Part 2
• Page Start: 70
• Page End: 86
• Publication Date: 2022-12
• Subjects: Biomass microwave pyrolysis -- Machine learning -- Gradient boost regressor -- Biochar -- Bio-oil -- Syngas; CO Carbon monoxide -- CO2 Carbon dioxide -- CH4 Methane -- GBR Gradient boost regressor -- H2 Hydrogen -- H/C Hydrogen-to-carbon -- H/N Hydrogen-to-nitrogen -- O/C Oxygen-to-carbon -- RMSE Root-mean-square error -- RRMSE Relative root-mean-square error -- RFR Random forest regressor -- R2 Coefficient of determination -- SHAP Shapley additive explanations -- SVR Support vector regressor; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.11.028
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Microwave heating is a promising solution to overcome the shortcomings of conventional heating in biomass pyrolysis. Nevertheless, biomass microwave pyrolysis is a complex thermochemical process governed by several endogenous and exogenous parameters. Modeling such a complicated process is challenging due to the need for many experimental measurements. Machine learning can effectively cope with the time and cost constraints of experiments. Hence, this study uses machine learning to model the quantity and quality of products (biochar, bio-oil, and syngas) that evolve in biomass microwave pyrolysis. An inclusive dataset encompassing different biomass types, microwave absorbers, and reaction conditions is selected from the literature and subjected to data mining. Three machine learning models (support vector regressor, random forest regressor, and gradient boost regressor) are used to model the process based on 14 descriptors. The gradient boost regressor model provides better prediction performance (R 2 > 0.822, RMSE <12.38, and RRMSE <0.765) than the other models. SHAP analysis generally reveals the significance of operating temperature, microwave power, and reaction time in predicting the output responses. Overall, the developed machine learning model can effectively save cost and time during biomass microwave pyrolysis while serving as a valuable tool for guiding experiments and facilitating optimization. Graphical abstract: Image 1 Highlights: Biomass microwave pyrolysis is characterized by using machine learning technology. The collected data is subjected to in-depth data mining and mechanistic explanations. Gradient boost regressor provides the best prediction performance with an R 2 > 0.822. SHAP analysis reveals the significance of descriptors in predicting the output responses. A simple computer program is developed to characterize biomass microwave pyrolysis.