Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

C. Chu, A. Boré, X.W. Liu, J.C. Cui, P. Wang, X. Liu, G.Y. Chen, B. Liu, W.C. Ma, Z.Y. Lou, Yushan Tao and A. Bary

Renewable and Sustainable Energy Reviews, 2022, vol. 157, issue C

Abstract: Thermal plasma gasification is considered as an attractive technology to produce high quality syngas from municipal solid waste (MSW). It is imperative to study the effect of operating parameters on syngas quality and find a practical way to predict syngas characteristics. This paper compiled 112 research cases to develop quantitative models for 8 kinds of syngas characteristics and explored the simultaneous effects of input parameters during the plasma gasification by applying stepwise linear regression (SLR) and artificial neural network (ANN) methods. The SLR model has a higher predictive accuracy than the ANN model for gas yield, volume fraction of CH4 and CO2, as well as mechanical gasification efficiency (MGE), with Rtesting2 = 0.659–0.916. The ANN model demonstrates better performance than the SLR model for low heating value (LHV), dry gas ratio, volume fraction of H2 and CO, with Rtesting2 = 0.807–0.939. According to the importance analysis, flow rates of the work gas-N2, feedstock type, flow rates of the work gas-steam, and input power are the most critical parameters for LHV, gas yield, and volume fraction of CH4 and H2, respectively. Input power and specific energy requirements (SER) are the most important factors affecting volume fractions of H2 (25.7–57.3 vol%) and input power plays a dominant role. The models developed in this study could enhance understanding of plasma gasification and are unique to considering multiple input parameters together.

Keywords: Thermal plasma; Gasification; Municipal solid waste (MSW); Stepwise linear regression (SLR); Artificial neural network (ANN); Syngas characteristics (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.rser.2021.112052

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