Modelling of batch biopolymer fermentation
This research is about modelling of Cupriavidus necator (C. necator) growth and polyhydroxyalkanoates (PHA) production in batch fermentation and fitting the models to the data using Runge-Kutta 4th Order Method by minimizing the error between experimental data and predicted data using the Simplex Me...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2010
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| Online Access: | http://umpir.ump.edu.my/id/eprint/3165/ http://umpir.ump.edu.my/id/eprint/3165/ http://umpir.ump.edu.my/id/eprint/3165/1/CD5955_MARYAM_ISMAIL.pdf |
| Summary: | This research is about modelling of Cupriavidus necator (C. necator) growth and polyhydroxyalkanoates (PHA) production in batch fermentation and fitting the models to the data using Runge-Kutta 4th Order Method by minimizing the error between experimental data and predicted data using the Simplex Method in MATLAB R2009b software. The models and the fitting methods were first tried on data of yeast biomass growth and intracellular enzyme cytochrome p-450 production in batch fermentation while the data of C. necator growth and PHA production is being generated. Hence, data were obtained from three sources which are Jailani et. al., (1995), Ali (2009) and Firdaus (2010). The biomass growth model developed was based on Logistic Model while the model for PHA production was developed based on the assumptions that each cell contain the same amount of PHA and that PHA degrades with the same rate. Predicted data was obtained using function ode45 in MATLAB R2009b software which implements Runge-Kutta 4th Order Method while minimum error was obtained through function fminsearch in the same software which implements Simplex Method. After completing the works, it was found that the models fit very well on data Salihon et. al., (1995) and Ali (2009). However, the models were not well fitted on data Firdaus (2010) as the values of parameters were not converged. |
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