Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/22653
Title: Machine learning technology in biodiesel research: A review
Authors: Aghbashlo, Mortaza 
Peng, Wanxi 
Tabatabaei, Meisam 
Kalogirou, Soteris A. 
Soltanian, Salman 
Hosseinzadeh-Bandbafha, Homa 
Mahian, Omid 
Lam, Su Shiung 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Machine learning;Artificial neural network;Biodiesel systems;Transesterification;Modeling;Control
Issue Date: Jul-2021
Source: Progress in Energy and Combustion Science, 2021, vol. 85, articl. no. 100904
Volume: 85
Journal: Progress in Energy and Combustion Science 
Abstract: Biodiesel has the potential to significantly contribute to making transportation fuels more sustainable. Due to the complexity and nonlinearity of processes for biodiesel production and use, fast and accurate modeling tools are required for their design, optimization, monitoring, and control. Data-driven machine learning (ML) techniques have demonstrated superior predictive capability compared to conventional methods for modeling such highly complex processes. Among the available ML techniques, the artificial neural network (ANN) technology is the most widely used approach in biodiesel research. The ANN approach is a computational learning method that mimics the human brain's neurological processing ability to map input-output relationships of ill-defined systems. Given its high generalization capacity, ANN has gained popularity in dealing with complex nonlinear real-world engineering and scientific problems. This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research. Moreover, the advantages and disadvantages of using ML technology in biodiesel research are highlighted to direct future R&D efforts in this domain. ML technology has generally been used in biodiesel research for modeling (trans)esterification processes, physico-chemical characteristics of biodiesel, and biodiesel-fueled internal combustion engines. The primary purpose of introducing ML technology to the biodiesel industry has been to monitor and control biodiesel systems in real-time; however, these issues have rarely been explored in the literature. Therefore, future studies appear to be directed towards the use of ML techniques for real-time process monitoring and control of biodiesel systems to enhance production efficiency, economic viability, and environmental sustainability.
URI: https://ktisis.cut.ac.cy/handle/10488/22653
ISSN: 0360-1285
DOI: 10.1016/j.pecs.2021.100904
Rights: © Elsevier Ltd
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : Henan Agricultural University 
University of Tehran 
Universiti Malaysia Terengganu 
Biofuel Research Team 
Agricultural Biotechnology Research Institute of Iran 
Cyprus University of Technology 
Xi'an Jiaotong University 
Ferdowsi University of Mashhad 
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