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).

翻译：本研究通过热解技术探究食品基生物质的热化学转化，重点阐明了人工智能（AI）在提升过程建模精度与优化效率中的作用，从而推动可持续能源与废弃物管理策略的发展。主要目标是挖掘未充分利用的生物质资源（如废咖啡渣（SCG）与椰枣籽（DS））在可持续制氢方面的潜力。具体而言，研究旨在优化热解过程，并评估这些资源单独及混合使用时的性能。对纯DS、纯SCG及其混合样品（75% DS - 25% SCG、50% DS - 50% SCG、25% DS - 75% SCG）进行了工业分析、元素分析、纤维分析、TGA/DTG分析、动力学分析、热力学分析以及Py-Micro GC分析。混合样品3（25% DS - 75% SCG）展现出最优的产氢潜力，但其活化能最高（Ea: 313.24 kJ/mol）；而混合样品1（75% DS - 25% SCG）则表现出最佳的活化能值（Ea: 161.75 kJ/mol）。基于等转化率法（KAS、FWO、Friedman）的动力学建模确定KAS方法最为精确。这些方法提供了对热解过程的深入理解，并特别强调了人工智能技术的集成应用。一个利用木质纤维素数据训练的LSTM模型，以极高的精度（R^2: 0.9996-0.9998）预测了TGA曲线。