Development of machine learning-based models for describing processes in a continuous solar-driven biomass gasifier

Abstract

The synergy of two renewable and efficient sources in producing clean fuels, i.e., solar energy and biomass, can result in high efficiency. In this regard, developing syngas production systems based on solar biomass gasification has attracted much attention. However, experimental setups on solar-driven gasifier processes are costly and time-intensive. In such a situation, an accurate and low-cost alternative is to develop data-driven machine learning (ML) models to predict the processes involved in solar-driven biomass gasifiers. In the present study, several ML models, including random forest (RF), RANdom SAmple Consensus (RANSAC), stochastic gradient descent (SGD), automatic relevance determination (ARD) regression, and elastic net linear (ENL) regression, were developed to accurately predict the various processing of a continuous solar-driven biomass gasifier, including CO production rate and H2 production rate (H2), carbon feeding rate, solar power input, thermochemical reactor efficiency, solar-to-fuel energy conversion efficiency, solar energy input, and carbon consumption rate. Using efficient ML methods, the eight formulas and the eight models for H2, CO production rate, carbon feeding rate, carbon consumption rate, solar energy input, solar power input, thermochemical reactor efficiency, and solar-to-fuel energy conversion efficiency are made in this study. Using the linear form can be reached the best R-Squared (R2) values for all formulas and the model, and the best R2 values are between 0.998 and 0.999 for the formulas by the elastic net and the ARD regression, and also the best R2 values are between 0.998 and 0.999 for the models by the RF and the RANSAC regressor. The R2 values for H2, CO production rate, carbon consumption rate, solar energy input, solar power input, thermochemical reactor efficiency, and solar-to-fuel energy conversion efficiency for formulas, respectively, are 0.998, 0.998, 0.999, 0.999, 0.999, 0.996, and 0.998 by the elastic net. For temperature of the carbon feeding rate, this value is 0.999 by the ARD regressor.