Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment

Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking... Fuel 216 (2018) 322–329 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment a, b A. Avinash , A. Murugesan Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641 407, Tamil Nadu, India Department of Mechanical Engineering, K.S. Rangasamy College of Technology, Tiruchengode 637 215, Tamil Nadu, India GR APHICAL A BSTRACT ARTICLE I NFO ABSTRACT Keywords: The present work describes the comparison of biodiesel yield prediction by Response Surface Methodology Waste cooking oil (RSM) and Artificial Neural Network (ANN). The prediction models were developed based on three-level design Transesterification of experiments conducted with waste cooking oil transesterified by varying four process parameters such as Biodiesel catalyst concentration, molar ratio, reaction time, and stirrer speed. The optimum reaction conditions were Response surface methodology found to be 0.75% wt/wt catalyst concentration, 9:1 M ratio, 60 min reaction time and 500 rpm stirrer speed. For Artificial neural network these optimum conditions, experimental fatty acid methyl ester (FAME) content of 95.05 ± 0.26% was ob- tained, which was in good agreement with the predicted yield. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Fuel Elsevier

Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0016-2361
D.O.I.
10.1016/j.fuel.2017.12.029
Publisher site
See Article on Publisher Site

Abstract

Fuel 216 (2018) 322–329 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment a, b A. Avinash , A. Murugesan Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641 407, Tamil Nadu, India Department of Mechanical Engineering, K.S. Rangasamy College of Technology, Tiruchengode 637 215, Tamil Nadu, India GR APHICAL A BSTRACT ARTICLE I NFO ABSTRACT Keywords: The present work describes the comparison of biodiesel yield prediction by Response Surface Methodology Waste cooking oil (RSM) and Artificial Neural Network (ANN). The prediction models were developed based on three-level design Transesterification of experiments conducted with waste cooking oil transesterified by varying four process parameters such as Biodiesel catalyst concentration, molar ratio, reaction time, and stirrer speed. The optimum reaction conditions were Response surface methodology found to be 0.75% wt/wt catalyst concentration, 9:1 M ratio, 60 min reaction time and 500 rpm stirrer speed. For Artificial neural network these optimum conditions, experimental fatty acid methyl ester (FAME) content of 95.05 ± 0.26% was ob- tained, which was in good agreement with the predicted yield.

Journal

FuelElsevier

Published: Mar 15, 2018

References

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