Estimation of bioethanol production from jatropha curcas using neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fossil fuel is one of the main energy sources for almost all country in the world. However, it is nonrenewable 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 35-45%. 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.

Original languageEnglish
Title of host publicationKey Engineering Materials
Pages943-947
Number of pages5
Volume594-595
DOIs
Publication statusPublished - 2014
Event2013 International Conference on Advanced Materials Engineering and Technology, ICAMET 2013 - Bandung
Duration: 28 Nov 201329 Nov 2013

Publication series

NameKey Engineering Materials
Volume594-595
ISSN (Print)10139826

Other

Other2013 International Conference on Advanced Materials Engineering and Technology, ICAMET 2013
CityBandung
Period28/11/1329/11/13

Fingerprint

Bioethanol
Neural networks
Glucose
Seed
Fossil fuels
Oils
Backpropagation algorithms
Poisons
Cellulose
MATLAB
Transfer functions

Keywords

  • Bioethanol
  • Jatropha seed cake
  • Neural network
  • Prediction

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

Abd Rahman, N., Tan Kofli, N., Yaakob, Z., & Gauri, S. (2014). Estimation of bioethanol production from jatropha curcas using neural network. In Key Engineering Materials (Vol. 594-595, pp. 943-947). (Key Engineering Materials; Vol. 594-595). https://doi.org/10.4028/www.scientific.net/KEM.594-595.943

Estimation of bioethanol production from jatropha curcas using neural network. / Abd Rahman, Norliza; Tan Kofli, Noorhisham; Yaakob, Zahira; Gauri, S.

Key Engineering Materials. Vol. 594-595 2014. p. 943-947 (Key Engineering Materials; Vol. 594-595).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abd Rahman, N, Tan Kofli, N, Yaakob, Z & Gauri, S 2014, Estimation of bioethanol production from jatropha curcas using neural network. in Key Engineering Materials. vol. 594-595, Key Engineering Materials, vol. 594-595, pp. 943-947, 2013 International Conference on Advanced Materials Engineering and Technology, ICAMET 2013, Bandung, 28/11/13. https://doi.org/10.4028/www.scientific.net/KEM.594-595.943
Abd Rahman, Norliza ; Tan Kofli, Noorhisham ; Yaakob, Zahira ; Gauri, S. / Estimation of bioethanol production from jatropha curcas using neural network. Key Engineering Materials. Vol. 594-595 2014. pp. 943-947 (Key Engineering Materials).
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