Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models
Data-driven machine learning (ML) methods are extensively employed for modeling and simulation of highly complicated processes. ML techniques confirmed their great predictive capability compared to conventional techniques for modeling and management of non-linear relationships between input and outp...
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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 |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878535223002952 |
| _version_ | 1797837122985525248 |
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| author | Abdulrahman Sumayli |
| author_facet | Abdulrahman Sumayli |
| author_sort | Abdulrahman Sumayli |
| collection | DOAJ |
| description | Data-driven machine learning (ML) methods are extensively employed for modeling and simulation of highly complicated processes. ML techniques confirmed their great predictive capability compared to conventional techniques for modeling and management of non-linear relationships between input and output parameters. Biofuels as renewable sources of energy are a significant potential alternative to fossil fuels. Due to the non-linearity and complexity of biofuels production processes and increasing energy conversion, accurate and fast modeling tools are necessary for design and optimization of these processes. Hence, in this research, ML modeling techniques were developed for simulation of biofuel production from energy conversion of Papaya oil through transesterification process. In order to simulate and optimize the content Papaya oil methyl ester (POME) production, Gaussian Process Regression (GPR), Multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models, as well as adaptive boosting for amplification, were employed. The temperature of reaction, catalyst quantity, time of process, and methanol to oil molar ratio were considered as the inputs of models while the POME yield was the model output. The obtained results showed that the R2-score of 0.988, 0.993, and 0.994 were obtained for Boosted MLP, Boosted GPR, and Boosted KNN, respectively, which demonstrate the high predictive ability of these models. Also, the RMSE metric error rates of 9.8071, 4.8150, and 6.5180 corresponded to Boosted MLP, Boosted GPR, and Boosted KNN, respectively. We examined performance using another metric, MAE: 8.38008, 2.3184, and 5.21954 errors were observed for Boosted MLP, Boosted GPR, and Boosted KNN, respectively. The optimized POME production yield of 99.89% was observed at temperature of 62.5 °C, 6.47 min of reaction, catalyst quantity of 0.8125 wt% and methanol to oil molar ratio of 10.33. The obtained results of this study show that the ML techniques are highly recommended for prediction of biofuels production as cost and time saving methods. |
| first_indexed | 2024-04-09T15:20:51Z |
| format | Article |
| id | doaj.art-c91e961a8d584b9ab0e4c1445ee78d69 |
| institution | Directory Open Access Journal |
| issn | 1878-5352 |
| language | English |
| last_indexed | 2024-04-09T15:20:51Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Arabian Journal of Chemistry |
| spelling | doaj.art-c91e961a8d584b9ab0e4c1445ee78d692023-04-29T14:49:10ZengElsevierArabian Journal of Chemistry1878-53522023-07-01167104833Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression modelsAbdulrahman Sumayli0Department of Mechanical Engineering, College of Engineering, Najran University, Najran، Saudi ArabiaData-driven machine learning (ML) methods are extensively employed for modeling and simulation of highly complicated processes. ML techniques confirmed their great predictive capability compared to conventional techniques for modeling and management of non-linear relationships between input and output parameters. Biofuels as renewable sources of energy are a significant potential alternative to fossil fuels. Due to the non-linearity and complexity of biofuels production processes and increasing energy conversion, accurate and fast modeling tools are necessary for design and optimization of these processes. Hence, in this research, ML modeling techniques were developed for simulation of biofuel production from energy conversion of Papaya oil through transesterification process. In order to simulate and optimize the content Papaya oil methyl ester (POME) production, Gaussian Process Regression (GPR), Multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models, as well as adaptive boosting for amplification, were employed. The temperature of reaction, catalyst quantity, time of process, and methanol to oil molar ratio were considered as the inputs of models while the POME yield was the model output. The obtained results showed that the R2-score of 0.988, 0.993, and 0.994 were obtained for Boosted MLP, Boosted GPR, and Boosted KNN, respectively, which demonstrate the high predictive ability of these models. Also, the RMSE metric error rates of 9.8071, 4.8150, and 6.5180 corresponded to Boosted MLP, Boosted GPR, and Boosted KNN, respectively. We examined performance using another metric, MAE: 8.38008, 2.3184, and 5.21954 errors were observed for Boosted MLP, Boosted GPR, and Boosted KNN, respectively. The optimized POME production yield of 99.89% was observed at temperature of 62.5 °C, 6.47 min of reaction, catalyst quantity of 0.8125 wt% and methanol to oil molar ratio of 10.33. The obtained results of this study show that the ML techniques are highly recommended for prediction of biofuels production as cost and time saving methods.http://www.sciencedirect.com/science/article/pii/S1878535223002952Papaya oil methyl ester (POME)ComputationBiofuelMachine learning |
| spellingShingle | Abdulrahman Sumayli Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models Arabian Journal of Chemistry Papaya oil methyl ester (POME) Computation Biofuel Machine learning |
| title | Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models |
| title_full | Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models |
| title_fullStr | Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models |
| title_full_unstemmed | Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models |
| title_short | Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models |
| title_sort | development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil gaussian process regression gpr multilayer perceptron mlp and k nearest neighbor knn regression models |
| topic | Papaya oil methyl ester (POME) Computation Biofuel Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1878535223002952 |
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