Catalytic Pyrolysis of PET Polymer Using Nonisothermal Thermogravimetric Analysis Data: Kinetics and Artificial Neural Networks Studies

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Thermogravimetric analysis (TGA) is crucial for describing polymer materials’ thermal behavior as a result of temperature changes. While available TGA data substantiated in the literature significantly focus attention on TGA performed at higher heating rates, this study focuses on the machine learning backpropagation analysis of the thermal degradation of poly (vinyl alcohol), or PVA, at low heating rates, typically 2, 5 and 10 K/min, at temperatures between 25 and 600 °C. Initial TGA analysis showed that a consistent increase in heating rate resulted in an increase in degradation temperature as the resulting thermograms shifted toward a temperature maxima. At degradation temperatures between 205 and 405 °C, significant depths in the characterization of weight losses were reached, which may be attributed to the decomposition and loss of material content. Artificial neural network backpropagation of machine learning algorithms were used for developing mathematical descriptions of the percentage weight loss (output) by these PVA materials as a function of the heating rate (input 1) and degradation temperature (input 2) used in TGA analysis. For all low heating rates, modelling predictions were observably correlated with experiments with a 99.2% correlation coefficient and were used to interpolate TGA data at 3.5 and 7.5 K/min, indicating trends strongly supported by experimental TGA data as well as literature research. Thus, this approach could provide a useful tool for predicting the thermograms of PVA materials at low heating rates and contribute to the development of more advanced PVA/polymer materials for home and industrial applications.

Section: Tga Experimental Procedures and Datasupporting

confidence: 79%

Machine Learning Backpropagation Prediction and Analysis of the Thermal Degradation of Poly (Vinyl Alcohol)

Otaru,

Alhulaybi,

Dubdub

2024

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“…In Figure 2 a,b, actual weight % is plotted against degradation temperature (°C) for two materials being heated at 10, 20, and 40 °C·min −1 heating rates. There was a general trend of temperature shifting to maxima with increasing heating rate, as substantiated in [ 16 , 20 , 21 ]. As can be seen in Figure 2 a, the actual weight loss of HDPE materials could be divided into three stages: loss of moisture content, decomposition of actual material, and residual content.…”

Section: Experimental Tga and Datamentioning

confidence: 79%

“…The purpose of adding neurons to the hidden layers is to improve training and was done based on suggestions and findings substantiated in [ 21 , 22 , 23 ]. Figure 6 illustrates an improvement in training results following training using the enhanced DNN framework in Figure 5 .…”

Section: Machine Learning Algorithms and Datamentioning

confidence: 99%

Machine Learning Analysis of Enhanced Biodegradable Phoenix dactylifera L./HDPE Composite Thermograms

Alhulaybi,

Otaru

2024

Worldwide, environmental groups and policymakers are focusing on waste recycling to create economic value and on the decomposition of waste by leveraging on scarce resources. This work, therefore, explores the thermal decomposition of enhanced biodegradable polymer matrices made from a mixture of discarded Phoenix dactylifera L./high-density polyethylene (PD/HDPE) using the machine learning analysis of experimental data. The experimental results of these samples were obtained via thermogravimetric (TGA) analysis under an oxidation–free environment, with heating rates of 10, 20, and 40 °C·min−1 and a degradation temperature range from 25 to 600 °C. The TGA analyses revealed the continued dependence of the actual percentage weight loss by these materials as a test function of the degradation temperature, shifting thermograms to temperature maxima consistent with increasing heating rates. Although high-density polyethylene (HDPE) materials were found to be thermally more stable than Phoenix dactylifera L. (PD) materials, PD/HDPE composite materials contained a significant amount of residual ash. Using a machine learning deep neural network approach for this process, significantly improved learning algorithms have been developed, which reduces the overall cost function (residual error) to almost zero (0.025) after just over a million iterations (epochs) and provides predictions that overlap with the experimental results (R2~1). Learning algorithms, along with optimized synaptic weights and biases, were employed to predict the behaviour of PD materials based on experimental thermograms conducted at higher degradation temperatures, typically ranging between 600 and 1000 °C. Predicted data using the enhanced learning algorithms completely overlapped the experiments (R2~1) for these higher degradation temperatures with near unity correlation if the decomposition of the materials continued until the residue was attained. With this approach, it is possible to predict and optimize the thermal characteristics of PD and HDPE with greater efficiency, which reduces the need for multiple design iterations and experimentation.

“…All five model-free methods can be derived from Equation (2) either integrally to obtain FR method or differentially to obtain FWO, KAS, STK, and DAEM with some assumptions for each method (Chowdhury et al (2023) [16], Aboulkas et al (2010) [20], Dubdub (2023) [21]). Table 2 presents these five isoconversional equations, and Table 3 shows the equations for CR and Criado.…”

Section: Derivation Of the Kinetic Equationsmentioning

confidence: 99%

Comprehensive Kinetic Study of PET Pyrolysis Using TGA

Alhulaybi

Dubdub

2023

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The pyrolysis of polyethylene terephthalate (PET) is a well-known process for producing high fuel value. This paper aims to study the kinetics of PET pyrolysis reactions at 4 different heating rates (2, 5, 10, and 20 K min−1) using thermogravimetric analysis (TGA) data. TGA data show only one kinetic reaction within the temperature ranges of 650 to 750 K. Five different model-free models, namely, the Freidman (FR), Flynn–Wall–Qzawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), and distributed activation energy model (DAEM), were fitted to the experimental data to obtain the activation energy (Ea) and the pre-exponential factor (A0) of the reaction kinetics. The Coats–Redfern (CR) model equation was fitted with the help of master plot (Criado’s) to identify the most convenient reaction mechanism for this system. Ea’s values were determined by the application of the five aforementioned models and were found to possess an average value of 212 kJ mol−1. The mechanism of PET pyrolysis reaction was best described by first-order reaction kinetics; this was confirmed by the compensation. Further thermodynamic parameter analysis indicated that the reaction was endothermic in nature.