Acid value minimization in continuous biodiesel production from a high viscous nonedible oil

Abstract

In the present study, acid value optimization in continuous biodiesel production from high viscous nonedible rubber seed oil (RSO) is presented. Gradual reduction in acid value from 67.6 (mg KOH/g oil) (raw RSO) to 3.55 (mg KOH/g oil) (pretreated RSO) to 0.25 (mg KOH/g oil) (synthesized biodiesel) is detected at reaction conditions of 9:1 alcohol:feedstock (methanol:oil (molar ratio)) (mol/mol), 4 h of residence time, and 5 wt% (oil) of catalyst concentration. Optimization of experimental factors is studied using well‐known data‐based tools called response surface methodology (RSM) and artificial neural network (ANN), respectively. A significant second‐order quadratic model with alcohol:feedstock molar ratio (mol/mol) as most influencing experimental factor is observed from ANOVA analysis of RSM studies. A mean‐square error (MSE) of 0.001245 is observed with the best validation performance of 0.0014716 at epoch‐3 in ANN modeling. Comparing the coefficient of determination (R2) of value 0.8906 (from RSM studies) with the value of 0.99 (from ANN modeling) reveals that ANN is the best fit model with experimental value in acid value optimization of continuous biodiesel production from RSO.