Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods
There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil....
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Arabian Journal of Chemistry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878535222002313 |
| _version_ | 1811336616185167872 |
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| author | Walid Kamal Abdelbasset Saud M. Alrawaili Shereen H. Elsayed Tazeddinova Diana Sami Ghazali Bassem F. Felemban Mohammed Zwawi Mohammed Algarni Chia-Hung Su Hoang Chinh Nguyen Omar Mahmoud |
| author_facet | Walid Kamal Abdelbasset Saud M. Alrawaili Shereen H. Elsayed Tazeddinova Diana Sami Ghazali Bassem F. Felemban Mohammed Zwawi Mohammed Algarni Chia-Hung Su Hoang Chinh Nguyen Omar Mahmoud |
| author_sort | Walid Kamal Abdelbasset |
| collection | DOAJ |
| description | There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively. |
| first_indexed | 2024-04-13T17:42:06Z |
| format | Article |
| id | doaj.art-14abf83fc2134892b597fd598c6e93e5 |
| institution | Directory Open Access Journal |
| issn | 1878-5352 |
| language | English |
| last_indexed | 2024-04-13T17:42:06Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Arabian Journal of Chemistry |
| spelling | doaj.art-14abf83fc2134892b597fd598c6e93e52022-12-22T02:37:09ZengElsevierArabian Journal of Chemistry1878-53522022-07-01157103915Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methodsWalid Kamal Abdelbasset0Saud M. Alrawaili1Shereen H. Elsayed2Tazeddinova Diana3Sami Ghazali4Bassem F. Felemban5Mohammed Zwawi6Mohammed Algarni7Chia-Hung Su8Hoang Chinh Nguyen9Omar Mahmoud10Department of Health and Rehabilitation Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, P.O. Box. 173, Al-Kharj 11942, Saudi Arabia; Department of Physical Therapy, Kasr Al-Aini Hospital, Cairo University, Giza 12613, Egypt; Corresponding authors.Department of Health and Rehabilitation Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, P.O. Box. 173, Al-Kharj 11942, Saudi ArabiaDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box. 84428, Riyadh 11671, Saudi ArabiaDepartment of Technology and Catering Organization, South Ural State University, Chelyabinsk, Russian Federation; Zhangir Khan Agrarian Technical University, Uralsk, KazakhstanMechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, P.O. Box 80327, Jeddah 21589, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21955, Saudi ArabiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi ArabiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi ArabiaDepartment of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan; Corresponding authors.School of Life and Environmental Sciences, Deakin University, Geelong, Victoria 3216, AustraliaPetroleum Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, EgyptThere is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.http://www.sciencedirect.com/science/article/pii/S1878535222002313Bioenergy productionFatty acid methyl ester (FAME)Optimization and analysisTransesterification processMachine learning method |
| spellingShingle | Walid Kamal Abdelbasset Saud M. Alrawaili Shereen H. Elsayed Tazeddinova Diana Sami Ghazali Bassem F. Felemban Mohammed Zwawi Mohammed Algarni Chia-Hung Su Hoang Chinh Nguyen Omar Mahmoud Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods Arabian Journal of Chemistry Bioenergy production Fatty acid methyl ester (FAME) Optimization and analysis Transesterification process Machine learning method |
| title | Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods |
| title_full | Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods |
| title_fullStr | Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods |
| title_full_unstemmed | Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods |
| title_short | Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods |
| title_sort | optimization of heterogeneous catalyst assisted fatty acid methyl esters biodiesel production from soybean oil with different machine learning methods |
| topic | Bioenergy production Fatty acid methyl ester (FAME) Optimization and analysis Transesterification process Machine learning method |
| url | http://www.sciencedirect.com/science/article/pii/S1878535222002313 |
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