It delved into the pyrolysis-gasification behavior of waste pharmaceutical blisters (WPBs), exploring both kinetics and thermodynamics, and employing machine learning modeling. The decomposition of WPBs occurred in three stages, with temperature intervals of 140–350, 350–500, and 500–900 °C, respectively. Cyclization/aromatization reactions were hindered, as indicated by mass spectrometric analysis, which revealed the main evolved products, including CH, CHOH, CH, and H. Apparent activation energy values obtained from FWO, KAS, Friedman, Cai & Chen models were comparable, showing a decreasing trend ranging from 264.9 to 48.4 kJ mol at α ≤ 0.70, followed by a subsequent increase to 283.1 kJ mol at a conversion of 0.80. The D1 model was more reliable in describing the pyrolysis-gasification process of WPBs. Machine learning modeling results indicated that the ANN19 with a topology structure of 5 ∗ 15 ∗ 1 and genetic programming models showed superior performance in predicting TG data.

中文翻译：

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废药品泡罩的热解气化转化：热动力学和热力学研究、燃气分析和机器学习建模


它深入研究了废药泡罩 (WPB) 的热解气化行为，探索动力学和热力学，并采用机器学习建模。 WPB的分解分三个阶段进行，温度区间分别为140-350、350-500和500-900℃。质谱分析表明，环化/芳构化反应受到阻碍，揭示了主要的演化产物，包括 CH、CHOH、CH 和 H。从 FWO、KAS、Friedman、Cai 和 Chen 模型获得的表观活化能值具有可比性，在 α ≤ 0.70 时表现出从 264.9 到 48.4 kJ mol 的下降趋势，随后在转化率为 0.80 时增加到 283.1 kJ mol。 D1模型在描述WPB的热解-气化过程方面更加可靠。机器学习建模结果表明，具有5 * 15 * 1拓扑结构的ANN19和遗传编程模型在预测TG数据方面表现出优越的性能。