Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR)
In this study, different distinct approaches of machine learning (ML) including Multi-layer perceptron (MLP), Gradient Boosting with DT (GBDT), and Gaussian process regression (GPR) were employed in order to predict the amount of Papaya oil methyl ester (POME) biodiesel production. To optimize the P...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Arabian Journal of Chemistry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878535223002630 |
| _version_ | 1827957483391418368 |
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| author | Abdulrahman Sumayli Saad M. Alshahrani |
| author_facet | Abdulrahman Sumayli Saad M. Alshahrani |
| author_sort | Abdulrahman Sumayli |
| collection | DOAJ |
| description | In this study, different distinct approaches of machine learning (ML) including Multi-layer perceptron (MLP), Gradient Boosting with DT (GBDT), and Gaussian process regression (GPR) were employed in order to predict the amount of Papaya oil methyl ester (POME) biodiesel production. To optimize the POME production, yield of these models were optimized with focus on maintaining generality and enhancing the prediction accuracy. The influencing transesterification factors on the biodiesel manufacture like the temperature of reaction (℃), amount of sodium hydroxide as catalyst (wt.%), treatment time (min), and methanol to papaya oil molar ratio were chosen as the inputs. NaOH was employed as a catalyst at the phase boundary for the reaction between papaya oil and short chain alcohols. Considering the MAPE criterion, the MLP, GBDT and GPR models have shown the error rates of 8.9670E-02, 2.0324E-01 and 7.2080E-02, respectively. Similarly, the GPR process gets the best R2 criterion score of 0.996, followed by GBDT with 0.989 and MLP with 0.971. The Mean Absolute Error (MAE) also shows the best model is the Gaussian process, which has an error rate of 4.7. In addition, the optimal POME yield production value was estimated through the proposed method to be about 99.96%, in the optimized values of 64 ℃, 0.875 wt%, 7.375 min, and 10.875 for the temperature reaction (℃), amount of catalyst, treatment time, and methanol to papaya oil molar ratio, respectively. |
| first_indexed | 2024-04-09T15:20:47Z |
| format | Article |
| id | doaj.art-bdf5a7a918264b5ea8f50a69d43a724a |
| institution | Directory Open Access Journal |
| issn | 1878-5352 |
| language | English |
| last_indexed | 2024-04-09T15:20:47Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Arabian Journal of Chemistry |
| spelling | doaj.art-bdf5a7a918264b5ea8f50a69d43a724a2023-04-29T14:48:56ZengElsevierArabian Journal of Chemistry1878-53522023-07-01167104801Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR)Abdulrahman Sumayli0Saad M. Alshahrani1Department of Mechanical Engineering, Najran University, Najran, Saudi Arabia; Corresponding author.Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi ArabiaIn this study, different distinct approaches of machine learning (ML) including Multi-layer perceptron (MLP), Gradient Boosting with DT (GBDT), and Gaussian process regression (GPR) were employed in order to predict the amount of Papaya oil methyl ester (POME) biodiesel production. To optimize the POME production, yield of these models were optimized with focus on maintaining generality and enhancing the prediction accuracy. The influencing transesterification factors on the biodiesel manufacture like the temperature of reaction (℃), amount of sodium hydroxide as catalyst (wt.%), treatment time (min), and methanol to papaya oil molar ratio were chosen as the inputs. NaOH was employed as a catalyst at the phase boundary for the reaction between papaya oil and short chain alcohols. Considering the MAPE criterion, the MLP, GBDT and GPR models have shown the error rates of 8.9670E-02, 2.0324E-01 and 7.2080E-02, respectively. Similarly, the GPR process gets the best R2 criterion score of 0.996, followed by GBDT with 0.989 and MLP with 0.971. The Mean Absolute Error (MAE) also shows the best model is the Gaussian process, which has an error rate of 4.7. In addition, the optimal POME yield production value was estimated through the proposed method to be about 99.96%, in the optimized values of 64 ℃, 0.875 wt%, 7.375 min, and 10.875 for the temperature reaction (℃), amount of catalyst, treatment time, and methanol to papaya oil molar ratio, respectively.http://www.sciencedirect.com/science/article/pii/S1878535223002630Modeling and simulationOptimizationBiodiesel productionMachine learningTransesterificationPapaya oil methyl ester |
| spellingShingle | Abdulrahman Sumayli Saad M. Alshahrani Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) Arabian Journal of Chemistry Modeling and simulation Optimization Biodiesel production Machine learning Transesterification Papaya oil methyl ester |
| title | Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) |
| title_full | Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) |
| title_fullStr | Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) |
| title_full_unstemmed | Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) |
| title_short | Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR) |
| title_sort | modeling and prediction of biodiesel production by using different artificial intelligence methods multi layer perceptron mlp gradient boosting gb and gaussian process regression gpr |
| topic | Modeling and simulation Optimization Biodiesel production Machine learning Transesterification Papaya oil methyl ester |
| url | http://www.sciencedirect.com/science/article/pii/S1878535223002630 |
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