Hydrogen production from blended waste biomass: pyrolysis, thermodynamic-kinetic analysis and AI-based modelling

• URL: http://arxiv.org/abs/2510.15960v1
• Date: Sat, 11 Oct 2025 15:51:41 GMT
• Title: Hydrogen production from blended waste biomass: pyrolysis, thermodynamic-kinetic analysis and AI-based modelling
• Abstract summary: This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis.<n>The main objective is to explore the potential of underutilized biomass resources, such as spent coffee grounds (SCG) and date seeds (DS) for sustainable hydrogen production.
• Score: 1.5889411586232436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized biomass resources, such as spent coffee grounds (SCG) and date seeds (DS), for sustainable hydrogen production. Specifically, it aims to optimize the pyrolysis process while evaluating the performance of these resources both individually and as blends. Proximate, ultimate, fibre, TGA/DTG, kinetic, thermodynamic, and Py-Micro GC analyses were conducted for pure DS, SCG, and blends (75% DS - 25% SCG, 50% DS - 50% SCG, 25% DS - 75% SCG). Blend 3 offered superior hydrogen yield potential but had the highest activation energy (Ea: 313.24 kJ/mol), while Blend 1 exhibited the best activation energy value (Ea: 161.75 kJ/mol). The kinetic modelling based on isoconversional methods (KAS, FWO, Friedman) identified KAS as the most accurate. These approaches provide a detailed understanding of the pyrolysis process, with particular emphasis on the integration of artificial intelligence. An LSTM model trained with lignocellulosic data predicted TGA curves with exceptional accuracy (R^2: 0.9996-0.9998).

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