Optimization based comparative study of machine learning methods for the prediction of bio-oil produced from microalgae via pyrolysis
Prediction of bio-oil yield using machine learning methods is an effective and economical approach. To examine the correlation between pyrolysis conditions, ultimate, and proximate analysis with bio-oil production is intricate and challenging task for the experimental techniques. Therefore, an effic...
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| Published in | Journal of analytical and applied pyrolysis Vol. 170; p. 105879 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0165-2370 1873-250X |
| DOI | 10.1016/j.jaap.2023.105879 |
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| Abstract | Prediction of bio-oil yield using machine learning methods is an effective and economical approach. To examine the correlation between pyrolysis conditions, ultimate, and proximate analysis with bio-oil production is intricate and challenging task for the experimental techniques. Therefore, an efficient and well-organized model must be created to reliably predict the effect of input parameters on the bio-oil yield. Multiple ML models are integrated with PSO and GA for selection of features and hyperparameters optimization. Here, GPR-GA model performed better with R2 = 0.997 and RMSE = 0.0185 as compared to GPR-PSO model with R2 = 0.994 and RMSE = 0.0120. The values of R2 for DT, ANN, ET, SVM integrated with PSO are respectively 0.91, 0.92, 0.83 and 0.43 and for GA based algorithms the values of R2 are 0.62, 0.93, 0.94, and 0.55 respectively. The significance of input factors on bio-oil yield was thoroughly examined using partial dependence plots and Shapley method. Moreover, an interface was developed by GPR-GA model to predict bio-oil yield. Yield comparison predicted by GPR-GA model and experimental study shows remarkable synchronization with maximum error of 1.48. It offers technologically advanced process in the pyrolysis of microalgae to enhance production of bio-oil.
[Display omitted]
•Optimized ML methods analyzed for bio oil production from microalgae via pyrolysis.•PDPs show temperature, time, and heating rate substantially affect bio oil yield.•GUI was developed utilizing the optimized GPR-PSO model to compute bio oil yield. |
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| AbstractList | Prediction of bio-oil yield using machine learning methods is an effective and economical approach. To examine the correlation between pyrolysis conditions, ultimate, and proximate analysis with bio-oil production is intricate and challenging task for the experimental techniques. Therefore, an efficient and well-organized model must be created to reliably predict the effect of input parameters on the bio-oil yield. Multiple ML models are integrated with PSO and GA for selection of features and hyperparameters optimization. Here, GPR-GA model performed better with R2 = 0.997 and RMSE = 0.0185 as compared to GPR-PSO model with R2 = 0.994 and RMSE = 0.0120. The values of R2 for DT, ANN, ET, SVM integrated with PSO are respectively 0.91, 0.92, 0.83 and 0.43 and for GA based algorithms the values of R2 are 0.62, 0.93, 0.94, and 0.55 respectively. The significance of input factors on bio-oil yield was thoroughly examined using partial dependence plots and Shapley method. Moreover, an interface was developed by GPR-GA model to predict bio-oil yield. Yield comparison predicted by GPR-GA model and experimental study shows remarkable synchronization with maximum error of 1.48. It offers technologically advanced process in the pyrolysis of microalgae to enhance production of bio-oil.
[Display omitted]
•Optimized ML methods analyzed for bio oil production from microalgae via pyrolysis.•PDPs show temperature, time, and heating rate substantially affect bio oil yield.•GUI was developed utilizing the optimized GPR-PSO model to compute bio oil yield. |
| ArticleNumber | 105879 |
| Author | Ahsan, Muhammad Ullah, Hafeez Khan, Muhammad Nouman Aslam Wang, Jiawei Haq, Zeeshan Ul Naqvi, Salman Raza |
| Author_xml | – sequence: 1 givenname: Hafeez surname: Ullah fullname: Ullah, Hafeez organization: School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan – sequence: 2 givenname: Zeeshan Ul surname: Haq fullname: Haq, Zeeshan Ul organization: School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan – sequence: 3 givenname: Salman Raza surname: Naqvi fullname: Naqvi, Salman Raza organization: School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan – sequence: 4 givenname: Muhammad Nouman Aslam surname: Khan fullname: Khan, Muhammad Nouman Aslam email: mnouman@scme.nust.edu.pk organization: School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan – sequence: 5 givenname: Muhammad surname: Ahsan fullname: Ahsan, Muhammad organization: School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan – sequence: 6 givenname: Jiawei surname: Wang fullname: Wang, Jiawei organization: Department of Chemical Engineering and Applied Chemistry, Aston University, Aston Triangle, Birmingham B4 7ET, UK |
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| Keywords | Pyrolysis Microalgae Genetic algorithm Particle swarm optimization Bio oil Machine learning |
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| Title | Optimization based comparative study of machine learning methods for the prediction of bio-oil produced from microalgae via pyrolysis |
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