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Reseach Article

Modeling of Neem Oil Methyl Esters Production using Artificial Neural Networks

by Y. C. Bhattacharyulu, V. N. Ganvir, Aditaya Akheramka, Amol Ramning
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 27
Year of Publication: 2013
Authors: Y. C. Bhattacharyulu, V. N. Ganvir, Aditaya Akheramka, Amol Ramning
10.5120/12238-8446

Y. C. Bhattacharyulu, V. N. Ganvir, Aditaya Akheramka, Amol Ramning . Modeling of Neem Oil Methyl Esters Production using Artificial Neural Networks. International Journal of Computer Applications. 70, 27 ( May 2013), 10-15. DOI=10.5120/12238-8446

@article{ 10.5120/12238-8446,
author = { Y. C. Bhattacharyulu, V. N. Ganvir, Aditaya Akheramka, Amol Ramning },
title = { Modeling of Neem Oil Methyl Esters Production using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 27 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number27/12238-8446/ },
doi = { 10.5120/12238-8446 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:59.656661+05:30
%A Y. C. Bhattacharyulu
%A V. N. Ganvir
%A Aditaya Akheramka
%A Amol Ramning
%T Modeling of Neem Oil Methyl Esters Production using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 27
%P 10-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of the present work is to develop models inculcating the effect of operating conditions of neem oil methyl esters (NOME) production in an oscillatory baffled reactor, namely temperature, time of reaction, oil to methanol ratio and catalyst concentration on the estimation of parameters like the viscosity of biodiesel produced by using Artificial Neural Networks technique. Experiments were conducted in the laboratory and the results obtained were used to develop the ANN model using MATLAB. The developed model was in good agreement with the experimental values (error within +1%). Based on the outcome of this demonstrative work, it can be concluded that ANN has a great potential in addressing the estimation of biodiesel properties. It is sincerely felt that the methodology adopted in the present work can be extended to more comprehensive data sets and various data from different experimental reactor design setups.

References
  1. Anderson J. A, An Introduction to Neural Networks Prentice-Hall of India, Pvt Ltd New Delhi, (1999).
  2. Rumelhart D E & McClleland Back Propagation Training Algorithm Processing, M. I. T Press, Cambridge Massachusetts, (1986).
  3. Roman M. Balabin, Ekaterina I. Lomakina, Ravilya Z. Safieva Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy, Fuel 90 (2011) 2007–2015.
  4. Machavaram Rajendra; Prakash Chandra Jena; Hifjur Raheman, Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA Fuel (May 2009), 88 (5), pg. 868-875.
  5. Liew Weng Hui, Zahira Yaakob, Siti Rozai mah Sheikh Abdullah, Artificial Neural Network Modeling and Performance Optimization on Biodiesel Production Process, Journal of Applied Sciences Research, 8(9): 4854-4864, 2012.
  6. Fahmi. I and Cremschi. S, University of Tulsa, Process Synthesis of Biodiesel Production Plant Using Artificial Neural Networks As the Surrogate Models, paper presented at Annual Meeting 2011 AIChE,10-20. 2011.
  7. H. Seung-Soo and G. S. May, "Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms," Semiconductor Manufacturing, IEEE Transactions on, vol. 10, pp. 279-287, 1997.
  8. I. Sabuncuoglu and S. Touhami, "Simulation meta modeling with neural networks: an experimental investigation," International Journal of Production Research, vol. 40, pp. 2483-2505, 2002
  9. Hassan Ghorbani1, Ali M. Nikbakht2, Meisam Tabatabaei3, Mahdi Hosseini1+, Poya Mohammadi1, Application of modeling techniques for prediction and optimization of biodiesel production processes, 2011 International Conference on Biotechnology and Environment Management IPCBEE vol. 18 (2011)IACSIT Press, Singapoore.
  10. J. Kumar, A. Bansal. , Selection of Best Neural Network for Estimating Properties of Diesel-Biodiesel Blends. Greece, February. 2007, 16-19.
  11. Kraipat Cheenkachorn, Predicting Properties of Biodiesels Using Statistical Models and Artificial Neural Networks, As. J. Energy Env. 2006, 7(02), 299-306.
  12. P. Nematizade, B. Ghobadian and G. Najafi, Investigation of fossil fuel and liquid biofuel blend properties using artificial neural network, International Journal of Automotive and Mechanical Engineering (IJAME); ISSN: 2180-1606 (Online); Volume 5, pp. 639-647, January-June 2012 ©Universiti Malaysia Pahang.
  13. Roman M. Balabina, Sergey V. Smirnovb, Variable selection in near-infrared Spectroscopy: Benchmarking of feature selection methods on biodiesel data, Analytica Chimica Acta 692 (2011) 63–72.
  14. Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA , Machavaram Rajendra; Prakash Chandra Jena; Hifjur Raheman Fuel (May 2009), 88 (5), pg. 868-875.
  15. G Hassan, Ali M. Nikbakht, MeisamTabatabaei, Mahdi Hosseini, Poya Mohammadi, International Conference on Biotechnology and Environment Management, IPCBEE Vol. 18 (2011), IACSIT Press, Singapore.
  16. Jatinder Kumar, Ajay Bansal, Kathmandu University Journal of Science, Engineering and Technology,Vol. 6, No. II, November, 2010, pg 98-103.
  17. Anindita Karmarkar, Prasanta Kumar Biswas, Souti Mukherjee. Environment-Congenial biodiesel Production from Non- Edible Neem oil. Environ Eng. Res. 2012 December, 17(s1):S27-S32.
Index Terms

Computer Science
Information Sciences

Keywords

Neem Oil Methyl Ester Oscillatory baffled reactor artificial neural network