Estimation of Bioethanol Production from <i>Jatropha</i> c<i>urcas</i> Using Neural Network

Abstract

Fossil fuel is one of the main energy sources for almost all country in the world. However, it is non-renewable energy source, not environmental friendly and the limited supply of the fossil fuel encouraged the scientist to discover other alternative way of new renewable energy supply. New alternative source should be considered for prolonged lifetime. Thus, non-conventional energy sources should be placed in the prior consideration, for instant bioethanol. Jatropha curcas seed is a toxic substance; however, it has a very high oil content which is approximately 3545%. After the extraction of oil from the seed, Jatropha seed cake is formed. In the pressed seed cake, it is found that it contains cellulose and glucose that can be used as substrate in bioethanol production. The production of bioethanol can be estimated by neural network using data from previous research. A programme using MATLAB 7.8 was used to develop the neural network. The software consists of Neural Network Toolbox which functions to train the input data and estimate the production of glucose and bioethanol as output data. An input layer represents the criteria of the production properties of glucose and bioethanol concentration. The hidden layer determines either the input data can be proceed to further production of glucose and bioethanol, whereas the output layer gives the estimation values of glucose and bioethanol production. Back propagation algorithm with TANSIG transfer function was used to accomplish the estimation of production of bioethanol. The error value given by the network was 0.0390. Thus, training sessions were considered successful. Therefore, the users could determine and estimate the production of glucose and bioethanol concentration in just a short period of time.