Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri

Dewa Safri, Amirul Iqbal (2015) Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri. [Student Project] (Unpublished)

Abstract

This researched is about to evaluate and make a comparison between the prediction and simulating efficiencies of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based on models on sugar fermentable by using empty fruit bunch fiber (EFBF) as a feedstock for bioethanol production. In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The parameters were obtain which are enzyme concentration, substrate concentration and time for using and applying in the RSM. The Artificial Neural Network (ANN) model was developed using MATHLAB Neural Network Toolbox to optimize the enzymatic hydrolysis from the 19 sets of experimental data. Based on the result obtained from both models, it indicates that both RSM and ANN models were fitted well to experimental data. However, ANN model showed a slight edge over RSM model due to higher value of R2 The R2 calculated from validation data for RSM and ANN models were 0.9812 and 0.999833 respectively. Thus, it is proven that ANN model is more powerful tool for modeling and optimization of the empty fruit bunch fiber for sugar fermentation production in term of the reducing sugar yield.

Metadata

Item Type: Student Project
Creators:
Creators
Email / ID Num.
Dewa Safri, Amirul Iqbal
2012275366
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Sanusi, Salmi Nur Ain
317612
Subjects: Q Science > QK Botany > Plant physiology > Physical plant physiology > Growth. Development. Including pattern formation
T Technology > TP Chemical technology > Fuel > Biomass
Divisions: Universiti Teknologi MARA, Johor > Pasir Gudang Campus > Faculty of Chemical Engineering
Programme: Diploma Chemical Engineering
Keywords: Artificial Neural Network (ANN), Bioethanol production, UiTM Pasir Gudang
Date: 2015
URI: https://ir.uitm.edu.my/id/eprint/40820
Edit Item
Edit Item

Download

[thumbnail of 40820.pdf] Text
40820.pdf

Download (212kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number

40820

Indexing

Statistic

Statistic details