Integration of PRO/II simulation and XGBoost machine learning for predicting and optimizing hydrogen-rich syngas from air-blown biomass–plastic co-gasification

Key Points

• Hydrogen-rich syngas production is optimized through a synergistic approach of simulation and machine learning.
• Using XGBoost, the model achieved a predictive accuracy of 90%, indicating its effectiveness in determining syngas yield.
• This analysis combines PRO/II simulation and machine learning to enhance gasification outcomes in an innovative manner.
• The results support developing energy-efficient strategies for renewable fuel production from mixed waste materials.