Optimization of <em>Cerbera manghas</em> Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization
Arridina Susan Silitonga,
Teuku Meurah Indra Mahlia,
Abd Halim Shamsuddin,
Hwai Chyuan Ong,
Jassinnee Milano,
Fitranto Kusumo,
Abdi Hanra Sebayang,
Surya Dharma,
Husin Ibrahim,
Hazlina Husin,
M. Mofijur,
S. M. Ashrafur Rahman
Affiliations
Arridina Susan Silitonga
Department of Mechanical Engineering, Politeknik Negeri Medan, Medan 20155, Indonesia
Teuku Meurah Indra Mahlia
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Abd Halim Shamsuddin
Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
Hwai Chyuan Ong
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Jassinnee Milano
Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Fitranto Kusumo
Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
Abdi Hanra Sebayang
Department of Mechanical Engineering, Politeknik Negeri Medan, Medan 20155, Indonesia
Surya Dharma
Department of Mechanical Engineering, Politeknik Negeri Medan, Medan 20155, Indonesia
Husin Ibrahim
Department of Mechanical Engineering, Politeknik Negeri Medan, Medan 20155, Indonesia
Hazlina Husin
Department of Petroleum Engineering, Faculty of Engineering, Universiti Teknologi Petronas, Persiaran UTP, Seri Iskandar 32610, Perak, Malaysia
M. Mofijur
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
S. M. Ashrafur Rahman
Biofuel Engine Research Facility (BERF), Queensland University of Technology, Brisbane, QLD 4000, Australia
Optimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel.
