Response surface optimization and artificial neural network modeling of biodiesel production from crude mahua (Madhuca indica) oil under supercritical ethanol conditions using CO2 as co-solvent

Abstract

The present study describes the renewable, environment-friendly approach for the production of biodiesel from low cost, high acid value mahua oil under supercritical ethanol conditions using carbon dioxide (CO₂) as a co-solvent. CO₂ was employed to decrease the supercritical temperature and pressure of ethanol. A response surface method (RSM) is the most preferred method for optimization of biodiesel so far. In last decade, the artificial neural network (ANN) method has come up as one of the most efficient methods for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between RSM and ANN for their predictive, generalization capabilities, parametric effects and sensitivity analysis. Experimental data were evaluated by applying RSM integrated with a desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be temperature (304 °C), ethanol to oil molar ratio (29 : 1), reaction time (36 min), and initial CO₂ pressure (40 bar). For these conditions, an experimental fatty acid ethyl ester (FAEE) content of 97.42% was obtained, which was in reasonable agreement with the predicted content. The sensitivity analysis confirmed that temperature was the main factor affecting the FAEE content with the relative importance of 39.24%. The lower values of the correlation coefficient (R² = 0.868), root mean square error (RMSE = 4.185), standard error of prediction (SEP = 5.81) and absolute average deviation (AAD = 5.239) for ANN compared to those of R² (0.658), RMSE (7.691), SEP (10.67) and AAD (8.574) for RSM proved the better prediction capability of ANN in predicting the FAEE content.