Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil: Development of multiple machine-learning computational techniques for optimization
Abdelbasset W.K. Elkholi S.M. Jade Catalan Opulencia M. Diana T. Su C.-H. Alashwal M. Zwawi M. Algarni M. Abdelrahman A. Chinh Nguyen H.
June 2022Elsevier B.V.
Arabian Journal of Chemistry
2022#15Issue 6
Multiple machine learning models were developed in this study to optimize biodiesel production from waste cooking oil in a heterogenous catalytic reaction mode. Several input parameters were considered for the model including reaction temperature, reaction time, catalyst loading, methanol/oil molar ratio, whereas the percent of biodiesel production yield was the only output. Three ensemble models were utilized in this study: Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree for optimization of the yield. We then found their optimized configurations for each model, namely hyper-parameters. This critical task is done by running more than 1000 combinations of hyper-parameters. Finally, The R2-Scores for Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree, respectively, were 0.926, 0.998, and 0.992. MAPE criterion revealed that the error rates for boosted linear regression, boosted multi-layer perceptron, and Forest of Randomized Tree was 5.68 × 10-2, 5.20 × 10-2, and 9.83 × 10-2, respectively. Furthermore, utilizing the input vector (X1 = 165, X2 = 5.72, X3 = 5.55, X4 = 13.0), the proposed technique produces an ideal output value of 96.7 % as the optimum yield in catalytic production of biodiesel from waste cooking oil.
Biodiesel , Esterification , Machine learning , Process optimization , Renewable energy
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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
College of Business Administration, Ajman University, Ajman, United Arab Emirates
Department of Technology and Catering Organization, South Ural State University, Chelyabinsk, Russian Federation
Zhangir Khan Agrarian Technical University, Uralsk, Kazakhstan
Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia
Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
Department of Mechanical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, 11845, Egypt
School of Life and Environmental Sciences, Deakin University, Geelong, 3216, VIC, Australia
Department of Health and Rehabilitation Sciences
Department of Physical Therapy
Department of Rehabilitation Sciences
College of Business Administration
Department of Technology and Catering Organization
Zhangir Khan Agrarian Technical University
Department of Chemical Engineering
Department of Computer Science
Mechanical Engineering Department
Department of Mechanical Engineering
School of Life and Environmental Sciences
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