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008 130925t2013 my da f m 000 0 eng|d
020 _aTHE0003914(Local)
039 9 _a201905140938
_bfauzi
_c201310011520
_dhuda
_c201309261248
_dtraining
_c201309261021
_dtraining
_y201309251633
_ztraining
040 _aUMP
090 _aTP156.D5 F37 2013 rs Bc.
100 0 _aFarah Fatihah Mohd Azhari
245 1 0 _aA simulation study on model predictive control application for depropanizer using aspen hysys /
_cFarah Fatihah Mohd Azhari
260 _aKuantan, Pahang :
_bUMP,
_c2013
300 _axiii, 62 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang - 2013
504 _aBibliography : p. 57-58
520 _aA model predictive control strategy was proposed for control problem in a distillation column. The aim was to demonstrate process models of depropanizer from step test data and to design an advanced process control (APC) scheme to replace conventional controller for distillation column. The simulation study was conducted using ASPEN HYSYS. In order to achieve the objectives, data was collected from process of depropanizer that used proportional integral derivative controller (PID) controller and the step test was run. Model predictive control (MPC) action was calculated using system identification techniques in MATLAB and process model was obtained. MPC was applied and performance of PID and MPC was compared using set point tracking.The results confirmed the potentials of the proposed strategy. Process model 2x2 constrained MPC was develop in this study. Based on the comparison of the two control methods, results presented prove that MPC can replace conventional controller, PID controller for a distillation column control. MPC also shows greater performances than PID in terms of set point tracking. Hence, MPC controller offers better control performances than PID controller, especially in multivariable processes.
650 0 _aDistillation
999 _aVIRTUA40
_c4015
_d4021
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*9992