Modeling the photocatalytic degradation of 1,2-Dihydroxybenzene using Multilayer Perceptron Neural Networks
In this study, the modeling of photocatalytic degradation of 1,2 dihydroxybenzene using a multilayer perceptron neural network has been investigated. The multilayer perceptron neural network which consists of input layer, hidden layer with network configuration of 3, 17, 1 respectively were employed...
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| 主要な著者: | , , , |
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| フォーマット: | Conference Paper |
| 出版事項: |
Institute of Physics Publishing
2023
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| 要約: | In this study, the modeling of photocatalytic degradation of 1,2 dihydroxybenzene using a multilayer perceptron neural network has been investigated. The multilayer perceptron neural network which consists of input layer, hidden layer with network configuration of 3, 17, 1 respectively were employed for predictive modeling using 20 datasets consisting the pH of the solution, the amount of the photocatalyst and the volume of the oxidant. The analysis of the network revealed that the volume of the oxidant was the most relevant factor that influences the degradation of the 1,2 dihydroxybenzene while the amount of photocatalyst has the least effect. The multilayer perceptron neural network model successfully predicts the photocatalytic degradation of the 1,2 dihydroxybenzene with coefficient of determination (R2) of 0.974. The predicted and the actual degradation of the 1,2 dihydroxybenzene was in close agreement with minimal error of prediction as indicated by the residual plot. This study has demonstrated the suitability of the multilayer perceptron neural network as a robust tool for modeling the prediction of 1,2 dihydroxybenzene degradation by photocatalytic process. � 2020 Institute of Physics Publishing. All rights reserved. |
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