Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw

In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). The par...

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Main Author:	Norhalim, Nur’atiqah
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TP155-156 Chemical engineering
Online Access:	http://eprints.usm.my/41069/1/NUR%E2%80%99ATIQAH_BINTI_NORHALIM_24_Pages.pdf
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spelling	my-usm-ep.410692018-07-17T07:15:43Z Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw 2015 Norhalim, Nur’atiqah TP155-156 Chemical engineering In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). The parameters studied such as temperature, agitation speed and concentration of enzyme in the process were investigated in order to get an optimum yield of xylose during enzymatic hydrolysis process. Data collected from an experimental design using response surface methodology (RSM) were used to develop the FANN modeling. The data samples has been split into training, testing and validation data set before re-sampling with bootstrap re-sampling method. Then, the FANN model was used to predict the model performance with one hidden layer and the PSO and GA were used to predict the optimum conditions of the process. The number of nodes in the hidden layer obtained is six where the performance on the model is satisfactory with the architecture of FANN, 3-6-1. The correlation coefficient of training and testing set were indicated at 0.9970 and 0.9975 respectively though the correlation coefficient of validation obtained was 0.8501. The optimization of xylose production using the GA method obtained conditions of 50.3˚C, 154 rpm and 1.6944 g/l. The optimum xylose production was predicted as 0.1845 g/l at optimal condition obtained by using GA. Meanwhile with PSO, the optimum temperature observed was at 50 °C, 132 xviii rpm for optimum value of agitation speed and 1.6474 g/l optimum xylanase concentration respectively. The optimal yield of xylose predicted was 0.1845 g/l using PSO for the enzymatic hydrolysis process. The laboratory experiment was carried out to validate the prediction of optimization result. It is shown from the experiment that the concentration of xylose obtained by using prediction optimum parameters for both PSO and GA are 0.2331 g/l and 0.2398 g/l respectively. The average error for the prediction and experimental values for the optimization are 29.97% and 26.34% for GA and PSO respectively. Therefore, the enzymatic hydrolysis on the production of xylose has been enhanced by predicting the optimum conditions utilizing the developed model that fits the experimental data. 2015 Thesis http://eprints.usm.my/41069/ http://eprints.usm.my/41069/1/NUR%E2%80%99ATIQAH_BINTI_NORHALIM_24_Pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Kimia
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TP155-156 Chemical engineering
spellingShingle	TP155-156 Chemical engineering Norhalim, Nur’atiqah Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
description	In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). The parameters studied such as temperature, agitation speed and concentration of enzyme in the process were investigated in order to get an optimum yield of xylose during enzymatic hydrolysis process. Data collected from an experimental design using response surface methodology (RSM) were used to develop the FANN modeling. The data samples has been split into training, testing and validation data set before re-sampling with bootstrap re-sampling method. Then, the FANN model was used to predict the model performance with one hidden layer and the PSO and GA were used to predict the optimum conditions of the process. The number of nodes in the hidden layer obtained is six where the performance on the model is satisfactory with the architecture of FANN, 3-6-1. The correlation coefficient of training and testing set were indicated at 0.9970 and 0.9975 respectively though the correlation coefficient of validation obtained was 0.8501. The optimization of xylose production using the GA method obtained conditions of 50.3˚C, 154 rpm and 1.6944 g/l. The optimum xylose production was predicted as 0.1845 g/l at optimal condition obtained by using GA. Meanwhile with PSO, the optimum temperature observed was at 50 °C, 132 xviii rpm for optimum value of agitation speed and 1.6474 g/l optimum xylanase concentration respectively. The optimal yield of xylose predicted was 0.1845 g/l using PSO for the enzymatic hydrolysis process. The laboratory experiment was carried out to validate the prediction of optimization result. It is shown from the experiment that the concentration of xylose obtained by using prediction optimum parameters for both PSO and GA are 0.2331 g/l and 0.2398 g/l respectively. The average error for the prediction and experimental values for the optimization are 29.97% and 26.34% for GA and PSO respectively. Therefore, the enzymatic hydrolysis on the production of xylose has been enhanced by predicting the optimum conditions utilizing the developed model that fits the experimental data.
format	Thesis
qualification_level	Master's degree
author	Norhalim, Nur’atiqah
author_facet	Norhalim, Nur’atiqah
author_sort	Norhalim, Nur’atiqah
title	Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
title_short	Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
title_full	Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
title_fullStr	Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
title_full_unstemmed	Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
title_sort	neural network modeling and optimization for enzymatic hydrolysis of xylose from rice straw
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Kimia
publishDate	2015
url	http://eprints.usm.my/41069/1/NUR%E2%80%99ATIQAH_BINTI_NORHALIM_24_Pages.pdf
_version_	1747820870954385408

Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw

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