01925nam a2200193 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100003000109245012700139260003400266300005800300502009100358504002800449520123700477650001701714vtls000074686KUKTEM20251114204543.0130925t2013    my da  f m    000 0 eng|d  aTHE0003914(Local)  aUMP0 aFarah Fatihah Mohd Azhari10aA simulation study on model predictive control application for depropanizer using aspen hysys /cFarah Fatihah Mohd Azhari  aKuantan, Pahang :bUMP,c2013  axiii, 62 p. :bill. (some col.) ;c30 cm. +e1 CD-ROM  aProject paper (Bachelor of Chemical Engineering) --  Universiti Malaysia Pahang - 2013  aBibliography : p. 57-58  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. 0aDistillation