Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective
Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/4/1122 |
| _version_ | 1797395830790946816 |
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| author | Krishna Kumar Gupta Kanak Kalita Ranjan Kumar Ghadai Manickam Ramachandran Xiao-Zhi Gao |
| author_facet | Krishna Kumar Gupta Kanak Kalita Ranjan Kumar Ghadai Manickam Ramachandran Xiao-Zhi Gao |
| author_sort | Krishna Kumar Gupta |
| collection | DOAJ |
| description | Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used. |
| first_indexed | 2024-03-09T00:41:24Z |
| format | Article |
| id | doaj.art-2c13fde378a54965b131f9bc6dc378d5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:41:24Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2c13fde378a54965b131f9bc6dc378d52023-12-11T17:45:56ZengMDPI AGEnergies1996-10732021-02-01144112210.3390/en14041122Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative PerspectiveKrishna Kumar Gupta0Kanak Kalita1Ranjan Kumar Ghadai2Manickam Ramachandran3Xiao-Zhi Gao4Department of Mechanical Engineering, MPSTME, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Shirpur Campus, Dhule 425 405, IndiaDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, IndiaDepartment of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737 136, IndiaData Analytics Lab, REST Labs, Kaveripattinam, Krishnagiri 635 112, IndiaSchool of Computing, University of Eastern Finland, FI-70211 Kuopio, FinlandOwing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.https://www.mdpi.com/1996-1073/14/4/1122biodieselmachine learninglinear regressionrandom forest regressionAdaBoost regression |
| spellingShingle | Krishna Kumar Gupta Kanak Kalita Ranjan Kumar Ghadai Manickam Ramachandran Xiao-Zhi Gao Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective Energies biodiesel machine learning linear regression random forest regression AdaBoost regression |
| title | Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective |
| title_full | Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective |
| title_fullStr | Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective |
| title_full_unstemmed | Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective |
| title_short | Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective |
| title_sort | machine learning based predictive modelling of biodiesel production a comparative perspective |
| topic | biodiesel machine learning linear regression random forest regression AdaBoost regression |
| url | https://www.mdpi.com/1996-1073/14/4/1122 |
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