Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques
Chemical activation; Errors; Gradient methods; Hydrogen production; Hyperbolic functions; Mean square error; Methane; Nonlinear analysis; Sensitivity analysis; Steam; Steam reforming; Surface properties; Activation functions; Artificial neurons; Gradient-descent; Multilayers perceptrons; Network res...
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Institution of Chemical Engineers
2023
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my.uniten.dspace-258712023-05-29T17:05:22Z Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques Ayodele B.V. Alsaffar M.A. Mustapa S.I. Adesina A. Kanthasamy R. Witoon T. Abdullah S. 56862160400 57210601717 36651549700 35564888500 56070146400 23487511100 57188753785 Chemical activation; Errors; Gradient methods; Hydrogen production; Hyperbolic functions; Mean square error; Methane; Nonlinear analysis; Sensitivity analysis; Steam; Steam reforming; Surface properties; Activation functions; Artificial neurons; Gradient-descent; Multilayers perceptrons; Network response; Non-linear response; Nonlinear response surface technique; Performance; Process intensification; Response surface techniques; Multilayer neural networks Uncertainty about how process factors affect output might lead to waste of resources in laboratory experiments. To address this constraint, a data-driven method might be used to describe the non-linear connection between process parameters and desired output. A Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN) and non-linear response surface method are used to predict hydrogen generation by catalytic steam methane reforming. The impact of training methods (scaled conjugate and gradient descent), hidden layer variation, artificial neuron variation, and activation functions were studied in 80 MLP-ANN combinations (hyperbolic tangent function and sigmoid function). The performance of MLP-ANN models was affected by the training techniques, activation functions, layer count, and number of artificial neurons. The model with the sigmoid function and 3 input layers, 17 artificial neurons in the first layer, 15 artificial neurons in the second layer, and 2 output nodes had the greatest performance among the 40 configurations of scaled conjugate trained ANNs. It projected an 89.55% maximal hydrogen yield with a coefficient of determination (R2) of 0.997 and reduced errors with Mean absolute percentage error (MAPE) and mean squared error (MSE) of 0.199 and 0.121, respectively. Similarly, the gradient descent ANN model with hyperbolic tangent activation function had the greatest performance among the 40 gradient descent trained-ANN configurations. The 3�15�7�2 gradient descent trained ANN model projected a maximum hydrogen output of 89.73% compared to the experimental results of 89.51%. The MLP-ANN models outperformed nonlinear response surface methods, with R2, MAPE, and MSE of 0.231, 0.191, and 0.988, respectively. The updated Garson algorithm indicated that the input parameters impacted the hydrogen production in the sequence reaction temperature>methane partial pressure>steam partial pressure. The sensitivity analysis might assist identify how resources should be spent. � 2021 The Institution of Chemical Engineers Final 2023-05-29T09:05:22Z 2023-05-29T09:05:22Z 2021 Article 10.1016/j.psep.2021.10.016 2-s2.0-85118362159 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118362159&doi=10.1016%2fj.psep.2021.10.016&partnerID=40&md5=7cd7a431cfab2f0b09265f8a6129894d https://irepository.uniten.edu.my/handle/123456789/25871 156 315 329 Institution of Chemical Engineers Scopus |
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Chemical activation; Errors; Gradient methods; Hydrogen production; Hyperbolic functions; Mean square error; Methane; Nonlinear analysis; Sensitivity analysis; Steam; Steam reforming; Surface properties; Activation functions; Artificial neurons; Gradient-descent; Multilayers perceptrons; Network response; Non-linear response; Nonlinear response surface technique; Performance; Process intensification; Response surface techniques; Multilayer neural networks |
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56862160400 Ayodele B.V. Alsaffar M.A. Mustapa S.I. Adesina A. Kanthasamy R. Witoon T. Abdullah S. |
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Article |
| author |
Ayodele B.V. Alsaffar M.A. Mustapa S.I. Adesina A. Kanthasamy R. Witoon T. Abdullah S. |
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Ayodele B.V. Alsaffar M.A. Mustapa S.I. Adesina A. Kanthasamy R. Witoon T. Abdullah S. Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| author_sort |
Ayodele B.V. |
| title |
Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| title_short |
Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| title_full |
Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| title_fullStr |
Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| title_full_unstemmed |
Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| title_sort |
process intensification of hydrogen production by catalytic steam methane reforming: performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques |
| publisher |
Institution of Chemical Engineers |
| publishDate |
2023 |
| _version_ |
1806427614188929024 |
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13.214268 |