A Review on Application of ANN Model for the Prediction of Fuel Properties of Biodiesel


  • Nithyananda B S Assistant Professor, Department of Mechanical Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India. https://orcid.org/0000-0002-7736-3729
  • Anand A Assistant Professor, Department of Mechanical Engineering, National Institute of Engineering, Mysuru, Karnataka, India.
  • GV Naveen Prakash Professor, Department of Mechanical Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.


Second Generation Feedstock, Artificial Neural Network (ANN), Biodiesel


The methyl esters of vegetable oils, known as biodiesel are becoming increasingly popular because of their low environmental impact and potential as a green alternative fuel for diesel engine. Non-edible oils are second generation biodiesel feedstock which contributes meagre of total global biodiesel production. Still there is a wide scope to explore potential non-edible oil feedstocks for biodiesel. Chemical structures are different from one feedstock to other in terms of chain length, degree of unsaturation, double bond configuration and number of double bonds. These contribute for fuel properties of biodiesel. The experimental characterization of biodiesel and its blends requires significant amount of sample, standardized equipment’s and time. Therefore, it is very much required to consider the prediction model to estimate fuel properties of biodiesel. ANN Prediction modelling can be a useful tool in accurately predicting biodiesel fuel properties instead of choosing costly and time-consuming experimental tests. The main aim of this paper is to review the literatures to discuss the application of ANN approach to characterize the biodiesel and its blends. This review has concluded that the ANN approach has high potential to accelerate the biodiesel research in India.

How to cite this article:
Nithyananda BS, Anand A, Prakash GVN. A Review on Application of ANN Model for the Prediction of Fuel Properties of Biodiesel. J Adv Res Mech Engi Tech 2019; 6(1&2): 32-37.


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