Bio-diesel synthesis from waste sunflower oil: Experimental and multilayer perceptron neural network modeling

Abstract

Transesterification being the most widely utilized way for biodiesel synthesis, the present work focuses on a single-step transesterification reaction between second-used sunflower oil and methanol as a reactant mixture with potassium hydroxide as a base catalyst. The parameter study exhibits encouraging results in terms of biodiesel/ ester conversion of the second used sunflower oil. Optimum parameters like temperature (62.3 °C), the molar ratio of methanol/ oil (1:5), and potassium hydroxide (2.85 wt%) were estimated for maximum biodiesel conversion (97.06 %). An artificial intelligence (AI) based formalism known as multilayer perceptron neural network (MLPNN) is utilized to develop the prediction model for biodiesel conversion (ml) (y0). The input space of the MLPNN-based model comprises of four operating parameters, viz. temperature (oC), second used sunflower oil (ml), methanol (ml), and reaction time (min). The proposed model is trained with the error-back propagation methodology. The high correlation coefficient (CCTraining=0.978;CCTest=0.999) and low root mean square error (RMSETraining=80.392;RMSETest=27.891) values indicate the excellent accuracy of prediction and generalization of the developed model. As a result, the proposed model can be conveniently engaged for further designing of biodiesel synthesis experimentation.