Optimising hydrogen production from supercritical water gasification of biomass and polymer waste: a machine learning-driven approach with differential evolutionary optimisation

Key Points

• This research aims to enhance hydrogen production efficiency from the gasification of biomass and polymer waste using advanced optimization techniques.
• Utilized supercritical water gasification to convert biomass and polymer waste into hydrogen.
• Employed machine learning-driven differential evolutionary optimization for process enhancement.
• Evaluated the effectiveness of optimization on hydrogen yield in various scenarios.
• Achieved a significant increase in hydrogen yield compared to traditional methods.
• Demonstrated improved efficiency metrics with machine learning methods, leading to higher production rates.