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Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization

Xiaorui Liu, Haiping Yang (yanghaiping@hust.edu.cn), Jiamin Yang and Fang Liu
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Xiaorui Liu: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Haiping Yang: State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jiamin Yang: School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China
Fang Liu: School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Energies, 2023, vol. 16, issue 3, 1-11

Abstract: Torrefaction is an effective technology to overcome the defects of biomass which are adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential to characterize the properties of torrefied biomass. However, the preparation and characterization of torrefied biomass often consume a lot of time, costs, and manpower. Developing a reliable method to predict the fuel properties of torrefied biomass while avoiding various experiments and tests is of great value. In this study, a machine learning (ML) model of back propagation neural network (BPNN) hybridized with genetic algorithm (GA) optimization was developed to predict the important properties of torrefied biomass for the fuel purpose involving fuel ratio (FR), H/C and O/C ratios, high heating value (HHV) and the mass and energy yields (MY and EY) based on the proximate analysis results of raw biomass and torrefaction conditions. R 2 and RMSE were examined to evaluate the prediction precision of the model. The results showed that the GA-BPNN model exhibited excellent accuracy in predicting all properties with the values of R 2 higher than 0.91 and RMSE less than 1.1879. Notably, the GA-BPNN model is applicable to any type of biomass feedstock, whether it was dried or not before torrefaction. This study filled the gap of ML application in predicting the multiple fuel properties of torrefied biomass. The results could provide reference to torrefaction technology as well as the design of torrefaction facilities.

Keywords: biomass; torrefaction; fuel property; machine learning; BP neural network; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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