TY - JOUR
TI - Optimized biodiesel production from C. innophyllum bio-oil using Kriging and Ann predicitive models
AU - Venkatesan Hariram
AU - Johnrose Godwin John
AU - Arumugam Saravanan
AU - Mari Vinoth Kumar
AU - Moorthy Balachandar
AU - Stalin Johnson Samuel
AU - Sivamani Seralathan
AU - Vasudev Kondeti Lakshmi
JN - Thermal Science
PY - 2022
VL - 26
IS - 5
SP - 4217
EP - 4232
PT - Article
AB - 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.