AI framework improves the efficiency of syngas production

Producing clean syngas from biomass and plastic waste offers a promising route to sustainable energy, but the underlying thermochemical processes are extremely complex and difficult to optimize. The study introduces an interpretable machine learning framework capable of accurately predicting the yields of key syngas components and the H₂/CO ratio during co-gasification. By analyzing the influence of raw material composition and operating parameters, the model identifies key factors controlling product distribution and provides mechanistic insights into the reaction system. The results support improved syngas quality, reduced experimental labor and more efficient process optimization, and provide practical guidelines for renewable fuel production and waste utilization.

Synthesis gas, a mixture consisting mainly of CO, H₂, CH₄, CO₂ and light hydrocarbons (C₂-C₄), is an important intermediate for electricity generation and chemical synthesis. The gasification of biomass is…