Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin

This study focused on maximizing the amount of reducing sugar produced from enzymatic hydrolysis of kitchen waste catalyzed by cellulase from Trichoderma viride and Trichoderma reesei, which was used separately to compare the results obtained from each enzyme. Effects of enzyme dosage (X/), substrat...

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Main Author:	Sayed Jamaludin, Sharifah Iziuna
Format:	Thesis
Language:	English
Published:	2014
Online Access:	https://ir.uitm.edu.my/id/eprint/16445/2/16445.pdf
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id	my-uitm-ir.16445
record_format	uketd_dc
spelling	my-uitm-ir.164452024-05-31T08:52:47Z Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin 2014-04 Sayed Jamaludin, Sharifah Iziuna This study focused on maximizing the amount of reducing sugar produced from enzymatic hydrolysis of kitchen waste catalyzed by cellulase from Trichoderma viride and Trichoderma reesei, which was used separately to compare the results obtained from each enzyme. Effects of enzyme dosage (X/), substrate concentration (X2), hydrolysis time (X3) and temperature (X4) were evaluated by Full Factorial Design (FFD) to determine the significant parameters affecting the production of reducing sugar. Optimization of process conditions were also performed using Central Composite Design (CCD) within the range employed for each independent variable. All the variables evaluated using FFD was found to have a significant effect towards the production of reducing sugar. The study has shown that enzymatic hydrolysis catalyzed by cellulase from T. viride is efficient in producing high amount of reducing sugar. A modelling study on enzymatic hydrolysis of kitchen waste was also performed to predict the reducing sugar yield using the datasets obtained from Response Surface Methodology (RSM) studies. A multi-layer feed-forward backpropagation artificial neural network (ANN) models were developed for enzymatic hydrolysis with input variables chosen from RSM studies. A comparative observation between ANN model and RSM model was also performed. Based on the R2 (correlation coefficient) and MSE (mean square error) values, it was concluded that ANN model is more accurate in predicting the reducing sugar yield than RSM model. 2014-04 Thesis https://ir.uitm.edu.my/id/eprint/16445/ https://ir.uitm.edu.my/id/eprint/16445/2/16445.pdf text en public mphil masters Universiti Teknologi MARA Faculty of Chemical Engineering Syed Abd. Kadir, Sharifah Aishah (Prof. Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Syed Abd. Kadir, Sharifah Aishah (Prof. Dr.)
description	This study focused on maximizing the amount of reducing sugar produced from enzymatic hydrolysis of kitchen waste catalyzed by cellulase from Trichoderma viride and Trichoderma reesei, which was used separately to compare the results obtained from each enzyme. Effects of enzyme dosage (X/), substrate concentration (X2), hydrolysis time (X3) and temperature (X4) were evaluated by Full Factorial Design (FFD) to determine the significant parameters affecting the production of reducing sugar. Optimization of process conditions were also performed using Central Composite Design (CCD) within the range employed for each independent variable. All the variables evaluated using FFD was found to have a significant effect towards the production of reducing sugar. The study has shown that enzymatic hydrolysis catalyzed by cellulase from T. viride is efficient in producing high amount of reducing sugar. A modelling study on enzymatic hydrolysis of kitchen waste was also performed to predict the reducing sugar yield using the datasets obtained from Response Surface Methodology (RSM) studies. A multi-layer feed-forward backpropagation artificial neural network (ANN) models were developed for enzymatic hydrolysis with input variables chosen from RSM studies. A comparative observation between ANN model and RSM model was also performed. Based on the R2 (correlation coefficient) and MSE (mean square error) values, it was concluded that ANN model is more accurate in predicting the reducing sugar yield than RSM model.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Sayed Jamaludin, Sharifah Iziuna
spellingShingle	Sayed Jamaludin, Sharifah Iziuna Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
author_facet	Sayed Jamaludin, Sharifah Iziuna
author_sort	Sayed Jamaludin, Sharifah Iziuna
title	Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
title_short	Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
title_full	Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
title_fullStr	Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
title_full_unstemmed	Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin
title_sort	enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / sharifah iziuna sayed jamaludin
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Chemical Engineering
publishDate	2014
url	https://ir.uitm.edu.my/id/eprint/16445/2/16445.pdf
_version_	1804889566135451648

Enzymatic hydrolysis and modelling of fermentable sugar production from kitchen waste / Sharifah Iziuna Sayed Jamaludin

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