Optimization of tar yield in co-pyrolysis data of biomass and coal using improved regression model

With the continuous growth of global energy demand and the urgent need for the utilization of renewable energy, the copyrolysis technology of biomass and coal has been receiving increasing attention. This study, based on the co-pyrolysis data of biomass and coal from the 9th Dimension Cup National College Mathematical Contest in Modeling 2024, uses the multiple linear regression model as a benchmark, combining LASSO regression and genetic algorithms to optimize the tar yield in the co-pyrolysis reaction of biomass and coal. LASSO regression is employed for variable selection and regularization to improve the model’s prediction accuracy and generalization ability. Additionally, genetic algorithms further identify the optimal ratios and the level of Insoluble N-hexane Substances (INS) that maximize tar yield. Through model optimization and the analysis of experimental data, the best mixing ratio is found to be approximately 51.7%, the optimal INS level is about 48.0%, and the predicted maximum tar yield is 0.275. To a certain extent, our model provide theoretical support for improving the energy conversion efficiency of the biomass and coal co-pyrolysis process.