Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (<i>Cussonia bateri</i>) Using Artificial Neural Network

Abstract

Biodiesel has been referred to as a perfect substitute for diesel fuel due to its numerous promising properties. They are renewable, clean, increases energy security, improves the environment and air quality and also provides some good safety benefits. This study is focused on the investigation of the use of natural heterogeneous catalysts for production of biodiesel from jansa seed oil, as well as the implementation of artificial neural network (ANN) for the prediction of biofuel yield and process parameters. The biodiesel was produced through transesterification reaction by reacting jansa seed oil (FFA) with methanol (alcohol) to yield methyl ester. Waste periwinkle shell was prepared in 3 different forms; raw, calcined and acidified. The percentage yield of the methyl ester obtained were calculated and tabulated. The process parameters considered were methanol-oil mole ratio, catalyst concentration, agitation speed, reaction temperature and reaction time. The results of this research work revealed that the calcined periwinkle shell catalyst produced higher yield of biodiesel, compared to the yield obtained from the raw and acidified catalyzed process. The properties of the fatty acid methyl esters were within the standard range. The experimental and predicted yield were marginally the same. Hence, the model accurately predicted the yield with acceptable coefficient of determination and low mean squared error (MSE). The results demonstrate the flexibility of ANN model and the improvement of the model in terms of performance prediction when solving problems with stochastic dataset, especially the transesterification of biodiesel.