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

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 pro-duction systems based on solar biomass gasification has attracted much attention. How-ever, 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 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.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.