Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm

Abhirup Khanna, Bhawna Yadav Lamba (), Sapna Jain (), Vadim Bolshev, Dmitry Budnikov, Vladimir Panchenko and Alexandr Smirnov
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Abhirup Khanna: Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
Bhawna Yadav Lamba: Department of Applied Sciences (Chemistry), University of Petroleum and Energy Studies, Dehradun 248007, India
Sapna Jain: Department of Applied Sciences (Chemistry), University of Petroleum and Energy Studies, Dehradun 248007, India
Vadim Bolshev: Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Dmitry Budnikov: Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Vladimir Panchenko: Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia
Alexandr Smirnov: Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia

Sustainability, 2023, vol. 15, issue 12, 1-33

Abstract: In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.

Keywords: biodiesel; deep neural networks; genetic algorithms; machine learning transesterification process; kinetics optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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