Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches
•Bayesian optimization algorithm (BOA) was implemented to tune hyperparameters.•Hybrid BOA-ANN and BOA-SVR were developed for the prediction of biodiesel yield.•The performance was compared between the developed and the existing models.•Hybrid BOA-SVR outperformed the most recent existing model in t...
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| Published in: | Fuel (Guildford) Vol. 309; p. 122184 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Kidlington
Elsevier Ltd
01.02.2022
Elsevier BV |
| Subjects: | |
| ISSN: | 0016-2361, 1873-7153 |
| Online Access: | Get full text |
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| Abstract | •Bayesian optimization algorithm (BOA) was implemented to tune hyperparameters.•Hybrid BOA-ANN and BOA-SVR were developed for the prediction of biodiesel yield.•The performance was compared between the developed and the existing models.•Hybrid BOA-SVR outperformed the most recent existing model in the literature.•The BOA-SVR model was validated further using extra literate data.
Biodiesel has appeared as a renewable and clean energy resource and a means of diminishing global warming. This study provides Bayesian optimization algorithm (BOA) based machine learning techniques such as artificial neural network (ANN) and Support vector regression (SVR) as the potential tool for modeling biodiesel production using microalgae oil as feedstock. Novelties of this study as in comparison with the existing Raj et al. model include (i) implementation of BOA to tune the model hyperparameters, (ii) hybridization of BOA with ANN, and SVR for modeling biodiesel production for the first time, (iii) the model performance was compared between the developed models and the existing model using several performance indicators (viz., Rpred2, residual analysis, RE, MAE, RMSE), and (iv) validation of the model using extra experimental data published elsewhere. The developed hybrid BOA-ANN and BOA-SVR models show better performance in comparison with the existing Raj et al. model. Comparing BOA-ANN and BOA-SVR, the later model shows excellent performance. Based on root mean square error (RMSE), the developed hybrid BOA-SVR shows higher performance than Raj et al. model with a performance enhancement of 36.03%. The precision of the hybrid BOA-SVR model was further validated with extra literature data. Thus, the proposed model would certify rapid estimation of biodiesel yield from microalgal oil that may reduce laborious, expensive, and time-consuming laboratory trials. |
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| AbstractList | Biodiesel has appeared as a renewable and clean energy resource and a means of diminishing global warming. This study provides Bayesian optimization algorithm (BOA) based machine learning techniques such as artificial neural network (ANN) and Support vector regression (SVR) as the potential tool for modeling biodiesel production using microalgae oil as feedstock. Novelties of this study as in comparison with the existing Raj et al. model include (i) implementation of BOA to tune the model hyperparameters, (ii) hybridization of BOA with ANN, and SVR for modeling biodiesel production for the first time, (iii) the model performance was compared between the developed models and the existing model using several performance indicators (viz., R2pred, residual analysis, RE, MAE, RMSE), and (iv) validation of the model using extra experimental data published elsewhere. The developed hybrid BOA-ANN and BOA-SVR models show better performance in comparison with the existing Raj et al. model. Comparing BOA-ANN and BOA-SVR, the later model shows excellent performance. Based on root mean square error (RMSE), the developed hybrid BOA-SVR shows higher performance than Raj et al. model with a performance enhancement of 36.03%. The precision of the hybrid BOA-SVR model was further validated with extra literature data. Thus, the proposed model would certify rapid estimation of biodiesel yield from microalgal oil that may reduce laborious, expensive, and time-consuming laboratory trials. •Bayesian optimization algorithm (BOA) was implemented to tune hyperparameters.•Hybrid BOA-ANN and BOA-SVR were developed for the prediction of biodiesel yield.•The performance was compared between the developed and the existing models.•Hybrid BOA-SVR outperformed the most recent existing model in the literature.•The BOA-SVR model was validated further using extra literate data. Biodiesel has appeared as a renewable and clean energy resource and a means of diminishing global warming. This study provides Bayesian optimization algorithm (BOA) based machine learning techniques such as artificial neural network (ANN) and Support vector regression (SVR) as the potential tool for modeling biodiesel production using microalgae oil as feedstock. Novelties of this study as in comparison with the existing Raj et al. model include (i) implementation of BOA to tune the model hyperparameters, (ii) hybridization of BOA with ANN, and SVR for modeling biodiesel production for the first time, (iii) the model performance was compared between the developed models and the existing model using several performance indicators (viz., Rpred2, residual analysis, RE, MAE, RMSE), and (iv) validation of the model using extra experimental data published elsewhere. The developed hybrid BOA-ANN and BOA-SVR models show better performance in comparison with the existing Raj et al. model. Comparing BOA-ANN and BOA-SVR, the later model shows excellent performance. Based on root mean square error (RMSE), the developed hybrid BOA-SVR shows higher performance than Raj et al. model with a performance enhancement of 36.03%. The precision of the hybrid BOA-SVR model was further validated with extra literature data. Thus, the proposed model would certify rapid estimation of biodiesel yield from microalgal oil that may reduce laborious, expensive, and time-consuming laboratory trials. |
| ArticleNumber | 122184 |
| Author | Sultana, Nahid Razzak, S.A. Hossain, S.M. Zakir Abusaad, M. Senan, Y. Alanbar, N. |
| Author_xml | – sequence: 1 givenname: Nahid surname: Sultana fullname: Sultana, Nahid email: nszakir@iau.edu.sa organization: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia – sequence: 2 givenname: S.M. Zakir surname: Hossain fullname: Hossain, S.M. Zakir email: zhossain@uob.edu.bh organization: Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain – sequence: 3 givenname: M. surname: Abusaad fullname: Abusaad, M. organization: Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain – sequence: 4 givenname: N. surname: Alanbar fullname: Alanbar, N. organization: Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain – sequence: 5 givenname: Y. surname: Senan fullname: Senan, Y. organization: Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain – sequence: 6 givenname: S.A. surname: Razzak fullname: Razzak, S.A. organization: Department of Chemical Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia |
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| Keywords | Microalgae Bayesian optimization Biodiesel Modeling Machine learning |
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| Snippet | •Bayesian optimization algorithm (BOA) was implemented to tune hyperparameters.•Hybrid BOA-ANN and BOA-SVR were developed for the prediction of biodiesel... Biodiesel has appeared as a renewable and clean energy resource and a means of diminishing global warming. This study provides Bayesian optimization algorithm... |
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| SubjectTerms | Algae Algorithms Aquatic microorganisms Artificial neural networks Bayesian analysis Bayesian optimization Biodiesel Biodiesel fuels Biofuels Clean energy Climate change Diesel Energy sources Global warming Hybridization Learning algorithms Learning theory Machine learning Mathematical models Microalgae Modeling Modelling Neural networks Oil Optimization Optimization algorithms Performance enhancement Root-mean-square errors Support vector machines |
| Title | Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches |
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