Biomass gasification technology for a distinct renewable electricity/heat production scheme; Developed regression models based on machine learning algorithms for power/hot water/efficiency/emission data analysis

Abstract

Rice straw is a promising feedstock for gasification, and using it in combined heat and power systems allows us to generate renewable electricity and heat while reducing reliance on fossil fuels and combatting climate change. As the demand for clean energy grows, machine learning will become even more crucial for managing our energy systems. A combined heat and power system was integrated based on rice straw gasification and the applications of linear and quadratic regression machine learning algorithms were evaluated using the residual analysis and the analysis of variance. The results show that quadratic models outperform linear models in all cases. Specifically, for efficiency modeling, the quadratic model scores 98.2 %, surpassing the linear model's 89.6 %. In emission modeling, the quadratic model outperforms the linear model with an R-sq score of 97 % versus 88.1 %. The results show that equivalence ratio of gasifier has the greatest impact, accounting for 65 % of the observed variation on emission linear model. In the quadratic emission model, linear factors contribute 58 % and quadratic factors contribute 42 %. Among the linear factors, pressure ratio has a significant influence with a contribution rate of 89 %. In terms of quadratic factors, pressure ratio has the highest influence, with a remarkable contribution rate of 92 %. This research emphasizes the significance of enhancing the precision of regression machine learning algorithms by conducting residual analysis and variance analysis.