Integration of PRO/II simulation and XGBoost machine learning for predicting and optimizing hydrogen-rich syngas from air-blown biomass–plastic co-gasification | Synapse
March 3, 2026
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.