Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods
Kamal Abdelbasset W. Alrawaili S.M. Elsayed S.H. Diana T. Ghazali S. Felemban B.F. Zwawi M. Algarni M. Su C.-H. Chinh Nguyen H. Mahmoud O.
July 2022Elsevier B.V.
Arabian Journal of Chemistry
2022#15Issue 7
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.
Bioenergy production , Fatty acid methyl ester (FAME) , Machine learning method , Optimization and analysis , Transesterification process
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Department 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
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box. 84428, Riyadh, 11671, Saudi Arabia
Department of Technology and Catering Organization, South Ural State University, Chelyabinsk, Russian Federation
Zhangir Khan Agrarian Technical University, Uralsk, Kazakhstan
Mechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia
Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21955, Saudi Arabia
Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
School of Life and Environmental Sciences, Deakin University, Geelong, 3216, VIC, Australia
Petroleum Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt
Department of Health and Rehabilitation Sciences
Department of Physical Therapy
Department of Rehabilitation Sciences
Department of Technology and Catering Organization
Zhangir Khan Agrarian Technical University
Mechanical and Materials Engineering Department
Department of Mechanical Engineering
Mechanical Engineering Department
Department of Chemical Engineering
School of Life and Environmental Sciences
Petroleum Engineering
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