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

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 productio...

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Main Author: Dewa Safri, Amirul Iqbal
Format: Student Project
Language:English
Published: 2015
Subjects:
Growth. Development. Including pattern formation
Biomass
Online Access:http://ir.uitm.edu.my/id/eprint/40820/1/40820.pdf
http://ir.uitm.edu.my/id/eprint/40820/
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spelling my.uitm.ir.408202021-02-16T10:24:56Z http://ir.uitm.edu.my/id/eprint/40820/ 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 Growth. Development. Including pattern formation Biomass 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. 2015 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/40820/1/40820.pdf 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)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Growth. Development. Including pattern formation
Biomass
spellingShingle Growth. Development. Including pattern formation
Biomass
Dewa Safri, Amirul Iqbal
Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
description 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.
format Student Project
author Dewa Safri, Amirul Iqbal
author_facet Dewa Safri, Amirul Iqbal
author_sort Dewa Safri, Amirul Iqbal
title Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
title_short Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
title_full Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
title_fullStr Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
title_full_unstemmed Modelling of enzymatic hydrolysis of empty fruit bunch fiber (EFBF) by artifical neural network (ANN) for fermentable sugar production / Amirul Iqbal Dewa Safri
title_sort modelling of enzymatic hydrolysis of empty fruit bunch fiber (efbf) by artifical neural network (ann) for fermentable sugar production / amirul iqbal dewa safri
publishDate 2015
url http://ir.uitm.edu.my/id/eprint/40820/1/40820.pdf
http://ir.uitm.edu.my/id/eprint/40820/
_version_ 1692994614480338944
score 13.211869

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