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008 100205t2009 my a f m 000 0 eng d
020 _aTHE0002154(Local)
039 9 _a201905131702
_byusri
_c201108030929
_dFida
_c201107132306
_dVLOAD
_c201002081450
_dkam
_y201002051025
_zkam
040 _aUMP
090 _aQD377.P4 C44 2009 rs Bc.
100 3 _aChew, Li Mei
245 1 0 _aModel predictive control on fed-batch penicillin fermentation process /
_cChew Li Mei
246 3 _aModel predictive control on fed-batch penicillin fermentation process
_h[computer file]
260 _aKuantan :
_bUMP,
_c2009
300 _a85 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang - 2009
520 3 _aIn this research study the development of optimization strategies for a fed-batch penicillin fermentation process using model predictive controller was simulated using MATLAB 7.1 software. To facilitate the study, model predictive control (MPC) based on unstructured model for penicillin production in a fed-batch fermentor has been developed. A mathematical model of the system is derived based on published materials, the data is generated using PENSIM, dynamic response is analyzed, transfer function is developed and finally the MPC is implemented into the fermentation process. MPC offers an adaptive and optimizing control strategy which deals with multiple goals and constraints. The results of a study of the applicability of Model Predictive Control (MPC) in the process were obtainable. In order to obtain best optimization result for the fed-batch penicillin fermentation process, two optimization algorithms were selected. First, dynamic optimization using direct shooting method and second is implementation single step ahead Dynamic Matrix Control (DMC). Comparison of these two different approaches shows that DMC algorithm showed the best result with an optimization procedure.
650 0 _aFermentation
650 0 _aPredictive control
650 0 _aPenicillin
999 _aVIRTUA40
_c1692
_d1698
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*6502*9992