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.
Keywords: 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.


1. Jahirul MI, Brown RI, Senadeera W et al. The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks. Energies, 2013; 6: 3764-3806.
2. Bhattacharyulu YC, Ganvir VN, Akheramka A et al. Modelling of Neem Oil Methyl Esters Production using Artificial Neural Networks. International Journal of Computer Applications 2013; 70.
3. Sharma A, Sharma H, Sahoo PK et al. ANN Based Modeling of Performance and Emission Characteristics of Diesel Engine Fuelled with Polanga Biodiesel at Different Injection Pressures. International Energy Journal 2015; 57-72.
4. Ghobadian B, Rahimi H, Nikah AM et al. Diesel engine performance and exhaust emission analysis using waste
cooking biodiesel fuel with an artificial neural network. Renewable Energy 2009; 34(4): 976-982. ISSN 0960-1481.
5. Jahirul MI, Senadeera W, Brown RJ et al. Estimation of Biodiesel Properties from Chemical Composition-An
Artificial Neural Network (ANN) Approach. International Scientific Journal Environmental Science 2014.
6. Kumar J, Bansal A. Application of Artificial Neural Network to Predict Properties of Diesel -Biodiesel Blends. Kathmandu University, Journal of Science, Engineering and Technology 2010; 6: 98-103.
7. Kouassi KE, Abolle, Yao KB et al. Optimization of Rubber Seed Oil Transesterification to Biodiesel Using
Experimental Designs and Artificial Neural Networks. Green and Sustainable Chemistry, 2018; (8): 39-61.
8. Jahirula MI, Senadeeraa W, Brooksb P et al. An Artificial Neutral Network (ANN) Model for Predicting Biodiesel
Kinetic Viscosity as a Function of Temperature and Chemical Composition, 20th International Congress on Modelling and Simulation. 2013.
9. Solomon O, Giwa SO, Adekomaya KO et al. Prediction of selected biodiesel fuel properties using artificial neural network. Front Energy 2015.
10. Raquel M, Sousa D, Labidi S et al. Application and Assessment of Artificial Neural Networks for Biodiesel
Iodine Value Prediction. International Journal of Computer and Information Engineering 2015; 9(5).
11. Piloto R, Sanchez Y, Goyos L et al. Prediction of cetane number of biodiesels from its fatty acid ester composition using Artificial Neural Networks. International Conference on Renewable Energies and Power Quality, 2013; 11. ISSN 2172-038.
12. Najafi B, Fakhr MA, Jamali S. Prediction of Heating value of vegetable oil-based ethyl esters biodiesel using Artificial Neural Network. Journal of Agricultural Machinery Science 2011; 7: 361-366.