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棕榈叶/聚丙烯生物复合材料的热降解:热动力学与卷积深度神经网络技术

Thermal Degradation of Palm Fronds/Polypropylene Bio-Composites: Thermo-Kinetics and Convolutional-Deep Neural Networks Techniques.

作者信息

Otaru Abdulrazak Jinadu, Albin Zaid Zaid Abdulhamid Alhulaybi

机构信息

Department of Chemical Engineering, College of Engineering, King Faisal University, Al Ahsa 31982, Saudi Arabia.

出版信息

Polymers (Basel). 2025 May 2;17(9):1244. doi: 10.3390/polym17091244.

Abstract

Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union's Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a sustainable environment. This study assesses the influence of palm fronds (PFR) on the thermal degradation of polypropylene plastic (PP) using TGA/FTIR experimental measurements, thermo-kinetics, and machine learning convolutional deep learning neural networks (CDNN). Thermal degradation operations were conducted on pure materials (PFR and PP) as well as mixed (blended) materials containing 25% and 50% PFR, across degradation temperatures ranging from 25 to 600 °C and heating rates of 10, 20, and 40 °C·min. The TGA data indicated a synergistic interaction between the agricultural waste (PFR) and PP plastic, with decreased thermal stability at temperatures below 500 °C, attributed to the hemicellulose and cellulose present in the PFR biomass. In contrast, at temperatures exceeding 500 °C, the presence of lignin retards the degradation of the PFR biomass and blends. Activation energy values between 81.92 and 299.34 kJ·mol were obtained through the application of the Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) model-free methods. The application of CDNN facilitated the extraction of significant features and labels, which were crucial for enhancing modeling accuracy and convergence. This modeling and simulation approach reduced the overall cost function from 41.68 to 0.27, utilizing seven hidden neurons, and 673,910 epochs in 13.28 h. This method effectively bridged the gap between modeling and experimental data, achieving an R value of approximately 0.992, and identified sample composition as the most critical parameter influencing the thermolysis process. It is hoped that such findings may facilitate an energy-efficient pathway necessary for the thermal decomposition of plastic materials in landfills.

摘要

确定可持续且高效的垃圾填埋场塑料废物降解方法,对于沙特绿色倡议、欧盟战略计划以及联合国2030行动计划的实施至关重要,这些计划均旨在实现可持续环境。本研究使用热重-傅里叶变换红外光谱(TGA/FTIR)实验测量、热动力学以及机器学习卷积深度学习神经网络(CDNN),评估了棕榈叶(PFR)对聚丙烯塑料(PP)热降解的影响。在25至600°C的降解温度以及10、20和40°C·min的升温速率下,对纯材料(PFR和PP)以及含有25%和50%PFR的混合(共混)材料进行了热降解操作。TGA数据表明农业废弃物(PFR)与PP塑料之间存在协同相互作用,在低于500°C的温度下热稳定性降低,这归因于PFR生物质中存在的半纤维素和纤维素。相比之下,在超过500°C的温度下,木质素的存在会阻碍PFR生物质及其共混物的降解。通过应用弗林-沃尔-小泽(FWO)和基辛格-赤平-ose(KAS)无模型方法,获得了81.92至299.34 kJ·mol之间的活化能值。CDNN的应用促进了显著特征和标签的提取,这对于提高建模精度和收敛性至关重要。这种建模和模拟方法利用七个隐藏神经元,在13.28小时内经过673,910个轮次,将总成本函数从41.68降低到0.27。该方法有效地弥合了建模与实验数据之间的差距,实现了约0.992的R值,并确定样品组成是影响热解过程的最关键参数。希望这些发现能够为垃圾填埋场中塑料材料热分解所需的节能途径提供便利。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad61/12073120/11ceb0574b9e/polymers-17-01244-g001.jpg

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