Abstract
Data detailed on Machine Learning have been presented. According to news originating from Beaumont, Texas, by NewsRx correspondents, research stated, “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.” Financial support for this research came from Deputyship for Research & Innovation, Ministry of Education Saudi Arabia. Our news journalists obtained a quote from the research from Lamar University, “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.”
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