THERMAL SCIENCE

International Scientific Journal

OPTIMIZED BIODIESEL PRODUCTION FROM C. INNOPHYLLUM BIO-OIL USING KRIGING AND ANN PREDICITIVE MODELS

ABSTRACT
This work aimed at optimizing the two-stage transesterification efficiency of the production of C. inophyllum biodiesel using artificial neural network and Kriging predictive models. Response surface methodology was used to develop the central rotatable composite design of 27 trial experimental runs with variations in the input process parameters like methanol to oil molar ratio, potassium hydroxide catalyst loading, and reaction time. A multi-layered non-linear regressive artificial neural network model with feed-forward propagation and a numerical surrogate Kriging model was used to predict the C. inophyllum biodiesel yield. The efficacy of the developed model was verified using analysis of variance by com-paring its coefficient of determination and the mean relative percentage deviation values. The optimized C. inophyllum biodiesel as 98.1% is derived with 0.94 v/v of methanol to oil molar ratio, 0.98 wt.% of potassium hydroxide catalyst loading, and 80 minutes reaction time with 70ºC constant reaction temperature as predicted by Kriging model. The optimized parameters were also verified experimentally.
KEYWORDS
PAPER SUBMITTED: 2021-11-27
PAPER REVISED: 2022-01-25
PAPER ACCEPTED: 2022-02-01
PUBLISHED ONLINE: 2022-03-05
DOI REFERENCE: https://doi.org/10.2298/TSCI211127032V
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 5, PAGES [4217 - 4232]
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© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence