The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning
Biodiesel is a renewable energy produced from transesterification of vegetable oils in the presence of various catalysts. The biodiesel production yield depends on types of feedstocks, reaction time, temperature, ratio of methanol to oil, and types of catalysts. Machine learning can be applied for p...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024014907 |
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| author | Pirapat Arunyanart Lida Simasatitkul Pachara Juyploy Peerapat Kotluklan Jirayu Chanbumrung Samitthichai Seeyangnok |
| author_facet | Pirapat Arunyanart Lida Simasatitkul Pachara Juyploy Peerapat Kotluklan Jirayu Chanbumrung Samitthichai Seeyangnok |
| author_sort | Pirapat Arunyanart |
| collection | DOAJ |
| description | Biodiesel is a renewable energy produced from transesterification of vegetable oils in the presence of various catalysts. The biodiesel production yield depends on types of feedstocks, reaction time, temperature, ratio of methanol to oil, and types of catalysts. Machine learning can be applied for prediction of biodiesel yield in a single process but the combination of biodiesel production processes, including a variety of alkali and acid catalysts, should be investigated. This work developed the modeling by using machine learning to predict the biodiesel yield of alkali catalysts and acid catalysts. The machine learning algorithms consisted of artificial neural network (ANN) and artificial neural network – particle swarm optimization (ANN-PSO). The proposed model was examined for Case I (alkali process) and Case II (combination process). For Case I with 19 input variables and a single output variable, the ANN with the trainlm algorithm and the Tansig- Tansig activation function was the most suitable model, offering the MAPE of 1.3510 and R square of 0.99272. While in Case II, the ANN with the trainbr algorithm performed the most accurately in terms of R square of 0.97229 and MAPE of 2.32145. Finally, the feature importance for Case I and Case II presented that the molar ratio of methanol to oil was the most important variable for the prediction of biodiesel yield. |
| format | Article |
| id | doaj-art-f78db02f006b46c39b5449cde1614e8b |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-f78db02f006b46c39b5449cde1614e8b2024-12-19T10:58:42ZengElsevierResults in Engineering2590-12302024-12-0124103236The prediction of biodiesel production yield from transesterification of vegetable oils with machine learningPirapat Arunyanart0Lida Simasatitkul1Pachara Juyploy2Peerapat Kotluklan3Jirayu Chanbumrung4Samitthichai Seeyangnok5Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, 10800, ThailandDepartment of Industrial Chemistry, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, 10800, Thailand; Corresponding author.Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, 10800, ThailandDepartment of Industrial Chemistry, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, 10800, ThailandDepartment of Industrial Chemistry, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, 10800, ThailandDepartment of Industrial Chemistry, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, 10800, ThailandBiodiesel is a renewable energy produced from transesterification of vegetable oils in the presence of various catalysts. The biodiesel production yield depends on types of feedstocks, reaction time, temperature, ratio of methanol to oil, and types of catalysts. Machine learning can be applied for prediction of biodiesel yield in a single process but the combination of biodiesel production processes, including a variety of alkali and acid catalysts, should be investigated. This work developed the modeling by using machine learning to predict the biodiesel yield of alkali catalysts and acid catalysts. The machine learning algorithms consisted of artificial neural network (ANN) and artificial neural network – particle swarm optimization (ANN-PSO). The proposed model was examined for Case I (alkali process) and Case II (combination process). For Case I with 19 input variables and a single output variable, the ANN with the trainlm algorithm and the Tansig- Tansig activation function was the most suitable model, offering the MAPE of 1.3510 and R square of 0.99272. While in Case II, the ANN with the trainbr algorithm performed the most accurately in terms of R square of 0.97229 and MAPE of 2.32145. Finally, the feature importance for Case I and Case II presented that the molar ratio of methanol to oil was the most important variable for the prediction of biodiesel yield.http://www.sciencedirect.com/science/article/pii/S2590123024014907BiodieselBiodiesel yieldTransesterificationMachine learningArtificial neural network |
| spellingShingle | Pirapat Arunyanart Lida Simasatitkul Pachara Juyploy Peerapat Kotluklan Jirayu Chanbumrung Samitthichai Seeyangnok The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning Results in Engineering Biodiesel Biodiesel yield Transesterification Machine learning Artificial neural network |
| title | The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| title_full | The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| title_fullStr | The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| title_full_unstemmed | The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| title_short | The prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| title_sort | prediction of biodiesel production yield from transesterification of vegetable oils with machine learning |
| topic | Biodiesel Biodiesel yield Transesterification Machine learning Artificial neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024014907 |
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