The potential of artificial neural network (ANN) in optimizing bioconversion process: in case of media constituents of citric acid production from palm oil Empty Fruit Bunches (EFB)

Nasaruddin, Ricca Rahman and Jami, Mohammed Saedi and Alam, Md. Zahangir (2011) The potential of artificial neural network (ANN) in optimizing bioconversion process: in case of media constituents of citric acid production from palm oil Empty Fruit Bunches (EFB). In: 2nd International Conference on Biotechnology Engineering (ICBioE 2011), 17 - 19 May 2011, Kuala Lumpur.

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Abstract

This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artificial neural network (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was built using MATLAB software. A 20 dataset of an earlier published paper was used to developed ANN. The predictive and generalization ability of ANN and the results of RSM in the used published paper were compared. The determination coefficient (R2-value) for ANN and RSM models were 0.997 and 0.985, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. Validation process was done and the maximum citric acid production (147.74 g/kg-EFB) was achieved using the optimal solution from ANN which consists of 6.1% sucrose, 9.2% mineral solution and 15.0% inoculum.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	5545/3170
Uncontrolled Keywords:	EFB, ANN, Sucrose, Mineral Solution, Inoculum, Citric acid
Subjects:	T Technology > TP Chemical technology > TP155 Chemical engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Biotechnology Engineering
Depositing User:	Prof. Ir. Dr. Mohammed Saedi Jami, PhD CEng MIChemE
Date Deposited:	14 Oct 2011 11:08
Last Modified:	14 Oct 2011 11:08
URI:	http://irep.iium.edu.my/id/eprint/3170

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