Stochastic Modelling Of Bioethanol Fermentation By Saccharomyces Cerevisiae Grown In Oil Palm Residues
In oil palm industry, large quantity of oil palm trunk (OPT) and palm oil mill effluent (POME) are generated. These residues are not fully utilised, in fact, they serve as wastes leading to serious environmental pollution. In this study, it was found that OPT sap contained high glucose concentratio...
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/41066/1/MOHD_DINIE_MUHAIMIN_BIN_SAMSUDIN_24_pages.pdf |
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| Summary: | In oil palm industry, large quantity of oil palm trunk (OPT) and palm oil mill effluent (POME) are generated. These residues are not fully utilised, in fact, they
serve as wastes leading to serious environmental pollution. In this study, it was found that OPT sap contained high glucose concentration (49.08 – 49.16 g/L), and POME contained all essential nutrients required by microorganisms. Therefore, OPT sap was used as carbon source, while POME was utilized as nutrient supplier for
fermentation process to produce bioethanol by Saccharomyces cerevisiae. Process optimization in shake flasks culture using one-factor-at-a-time (OFAT) method
produced bioethanol yield of 0.428 g/g. Response surface methodology (RSM) via face centered central composite design (FCCCD) optimization was also conducted.
The highest bioethanol yield was achieved (0.453 g/g) at OPT sap to POME ratio of 63:37, inoculum size 4.3% (v/v), initial pH 8.0, and incubation time 118 hours.
Optimization was then carried out in a 2.5 L stirred tank bioreactor. The highest bioethanol yield (0.481 g/g) was attained at 33°C, pH 7.0, and agitated at 0.4 m/s
impeller tip speed. However, this result might differ if it is to be reduplicated due to heterogeneity of OPT sap and POME as well as variability in Baker’s yeast’s
performance. Therefore, a predictability test was carried out using Monte Carlo algorithm. Results showed that consistence bioethanol yield with this study could be
expected if the fluctuation of kinetic model parameters is 2.5% or below. Variability beyond 2.5% could result in high variation of bioethanol yield with relative standard
deviation of higher than 5.0%. Therefore, sensitivity analysis was then carried out in order to evaluate the influence of each kinetic model parameter on bioethanol yield. It showed that bioethanol yield highly dependent on the changes of biosolid which represented the yeast growth. Therefore, in order to maximise bioethanol yield, the yeast growth must be carefully monitored. In order to improve bioethanol production, very high gravity (VHG) fermentation was then carried out using concentrated OPT sap and POME. In this study, OPT sap was concentrated using a rotary vacuum evaporator. In addition, evaporation model considering both temperature and pressure was developed and validated. During VHG fermentation, up to 131.2 mmol/(min.mg protein) and 60.5 mmol/(min.mg protein) of glycerol-3- phospatase (G3Pase) and trehalose-6-phosphate phosphatase (TPP) enzyme activity were detected, respectively. These enzymes were produced by Baker’s yeast in order to sustain its growth in high osmotic stress, and high ethanol toxicity, respectively. |
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