用咖啡渣和枣核混合物热解制氢，AI模型精准预测反应过程。
Hydrogen production from blended waste biomass: pyrolysis, thermodynamic-kinetic analysis and AI-based modelling
- 通过热解实验与AI建模结合，优化混合生物质制氢工艺。
- 75%枣核+25%咖啡渣组合产氢潜力最高，但活化能达313.24 kJ/mol。
- LSTM模型预测热重曲线精度超99.9%，适合工业过程仿真。
本研究通过热解技术探索食品类生物质（如废咖啡渣SCG、枣核DS）的可持续氢能生产潜力，重点分析纯物质及不同比例混合物（75% DS - 25% SCG、50% DS - 50% SCG、25% DS - 75% SCG）的性能。利用工业级分析手段进行近似组成、元素分析、纤维含量、TGA/DTG、动力学与热力学评估。结果显示，75%枣核+25%咖啡渣（混合物3）具备最高产氢潜力，但其活化能最高（Ea: 313.24 kJ/mol）；而混合物1（75% DS - 25% SCG）活化能最低（Ea: 161.75 kJ/mol）。基于等转化率方法（KAS、FWO、Friedman）的动力学建模表明，KAS方法最准确。进一步采用基于木质纤维素数据训练的LSTM模型，对TGA曲线预测的决定系数高达R²: 0.9996–0.9998，展现出极强的建模能力。
原文摘要 · Abstract (English)
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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