🤖 AI Summary
This study investigates the sustainable hydrogen production potential of two underutilized food-derived biomass wastes—spent coffee grounds and jujube pits—via pyrolysis. Thermogravimetric analysis (TGA/DTG), pyrolysis-micro gas chromatography (Py-Micro GC), and kinetic modeling (KAS, FWO, and Friedman methods) were employed to systematically characterize pyrolytic behavior and H₂ yield for individual feedstocks and three blended formulations (Blend 1–3). An AI-enhanced modeling framework was innovatively developed, integrating long short-term memory (LSTM) neural networks to achieve highly accurate prediction of thermogravimetric profiles (R² = 0.9998) and to elucidate inter-material synergistic effects. Results show that Blend 3 delivers the highest hydrogen yield, while Blend 1 exhibits the lowest apparent activation energy (161.75 kJ/mol). This work establishes a scalable, integrated kinetics–AI optimization paradigm for targeted hydrogen production from waste biomass.
📝 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).