利用 AIC 模型和 ANN 模型优化和预测香蕉皮和废塑料共热解过程中的热力学参数

IF 8 Q1 ENERGY & FUELS
Jitendra Choudhary , Aman Kumar , Bablu Alawa , Sankar Chakma
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引用次数: 0

摘要

利用 ANN 和 AIC 模型对香蕉皮(BP)和废聚苯乙烯(PS)塑料在不同加热速率下共热解的热力学参数(包括协同效应)和动力学分析进行了优化和预测。通过热重分析确定了初始、最高和最终降解温度。使用添加剂公式研究了协同效应,确定了理论热行为,并与实验 TGA 数据进行了比较。动力学参数的确定采用了先进的等转化(AIC)模型估算活化能(Eα),克里亚多主图估算反应机理(f(α)),以及频率因子(Aα)的补偿方法。分析表明,PS、BP 和 PS+BP 的平均活化能分别为 182.5、140.6 和 161.8 kJ mol-1。这也清楚地表明了 PS 和 BP 共同热解的正协同作用,活化能比 PS 单独热解降低了 11.3%。研究发现,PS、BP 和 PS+BP 的频率因子分别为 1.0 × 1014、1.0 × 1015 和 1.0 × 1023 s-1。确定 PS、BP 和 PS+BP 的反应机理分别为 R3、D4 和 D4+R3。此外,还利用获得的动力学参数确定了热力学参数,如焓(ΔH)、吉布斯能(ΔG)和熵(ΔS)。最后,设计了 ANN 来处理不同加热速率下的共热解行为。随后,采用训练有素的 ANN 模型(5 × 4×4 × 4)来预测热降解行为。令人印象深刻的是,该模型产生了高度精确的结果,每种情况下的相关系数 R2 > 均为 0.998。优化后的模型进一步用于预测 PS 和 BP 未知混合物的 TGA 数据和活化能。建议的 ANN 模型在优化方面显示出巨大优势,可避免为实现目标而在各种加热速率下进行大量实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimization and prediction of thermodynamic parameters in co-pyrolysis of banana peel and waste plastics using AIC model and ANN modeling

Optimization and prediction of thermodynamic parameters in co-pyrolysis of banana peel and waste plastics using AIC model and ANN modeling

The optimization and prediction of thermodynamic parameters including synergistic effects, and kinetic analysis in co-pyrolysis of banana peel (BP) and waste polystyrene (PS) plastic at different heating rates using ANN and AIC models has been performed. Thermogravimetric analysis was performed to determine the initial, maximum, and final degradation temperatures. The synergistic effect was studied using additive formula to determine the theoretical thermal behavior and compared with experimental TGA data. Kinetic parameters were determined by using the advanced isoconversional (AIC) model for estimation of activation energy (Eα), Criado master plot for reaction mechanism (f(α)), and compensation method for frequency factor (Aα). The analysis showed that the average activation energy values were 182.5, 140.6, and 161.8 kJ mol−1 for PS, BP, and PS+BP, respectively. It also clearly shows positive synergy in co-pyrolysis of PS and BP by reducing 11.3 % activation energy compared to that of PS alone. The frequency factor was found to be 1.0 × 1014, 1.0 × 1015, and 1.0 × 1023 s−1 for PS, BP, and PS+BP, respectively. The reaction mechanism was identified as R3, D4, and D4+R3 for PS, BP, and PS+BP, respectively. Further, the obtained kinetic parameters were used to determine the thermodynamic parameters such as enthalpy (ΔH), Gibbs energy (ΔG), and Entropy (ΔS). Finally, ANN was designed to address the co-pyrolysis behavior subjected to various heating rates. Subsequently, the trained ANN model (5 × 4×4 × 4) was employed to forecast thermal degradation behavior. Impressively, the model yielded highly accurate results with a correlation coefficient R2 > 0.998 in each case. The optimized model was further used to predict TGA data and activation energy for unknown mixtures of PS and BP. The suggested ANN model showed a great advantage in optimizing to avoid extensive experiments at various heating rates to achieve the goal.

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来源期刊
Energy nexus
Energy nexus Energy (General), Ecological Modelling, Renewable Energy, Sustainability and the Environment, Water Science and Technology, Agricultural and Biological Sciences (General)
CiteScore
7.70
自引率
0.00%
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0
审稿时长
109 days
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