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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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | https://ir.uitm.edu.my/id/eprint/16445/2/16445.pdf |
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| Summary: | 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. |
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