Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

Abstract

In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst concentration (1 wt %), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively.