On the hydrodynamics of macroporous structures: Experimental, CFD and artificial neural network analysis

Otaru, A.J.; Alhulaybi, Z.A.; Owoseni, T.A.

doi:10.1016/j.ceja.2023.100545

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…A preliminary assessment and sorting of the raw data from the TGA experiment was performed by selecting some data points along the thermograms for the three different heating rates and considering the initial and final degradation temperatures (25 and 600 • C) used in the experiment. As discussed in [29][30][31], the introduction of the hidden neurons (layers) into the framework in Figure 2 was performed to improve convolution and non-linearity between input and output signals that have been determined experimentally.…”

Section: Machine Learning Backpropagation Neural Network and Datamentioning

confidence: 99%

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

Otaru,

Alhulaybi,

Dubdub

2024

Polymers

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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.

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Section: Machine Learning Backpropagation Neural Network and Datamentioning

confidence: 99%

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

Otaru,

Alhulaybi,

Dubdub

2024

Polymers

Self Cite

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“…There are other non-linear activation functions, such as hyperbolic tangents (TanH), rectified linear units (ReLU), exponential linear units (ELU), etc. However, the choice of Sigmoid activation functions is based on their ability to converge data points that are between 0 and 1 based on changes in arbitrary constants during training [ 20 , 24 , 26 ]. For this reason, the input and output data points were divided by the maximum value possible for a TGA experiment as part of data preparation before training.…”

Section: Machine Learning Algorithms and Datamentioning

confidence: 99%

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

Alhulaybi,

Otaru

2024

Polymers

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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.

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Mathematical formulation of the machine learning backpropagation network and regression modelling of the chemical stability and thermal properties of PLA/HKUST-1 fabricated porous membranes

Alhulaybi,

Otaru

2024

J Therm Anal Calorim

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On the hydrodynamics of macroporous structures: Experimental, CFD and artificial neural network analysis

Cited by 4 publications

References 26 publications

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

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

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

Mathematical formulation of the machine learning backpropagation network and regression modelling of the chemical stability and thermal properties of PLA/HKUST-1 fabricated porous membranes

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