Parametric and nonparametric model based control of a packed distillation column

Abstract

Parametric and nonparametric model based control systems were applied to control the overhead temperature of a packed distillation column separating methanol–water mixture. Experimental and theoretical studies have been done to observe the efficiency and performance of both control systems. Generalized predictive control (GPC) system based on a parametric model has been tried to keep the overhead temperature at the desired set point. First, a parametric model which is controlled auto regressive integrated moving average (CARIMA) was developed and then the parameters of this model were identified by applying pseudo random binary sequence (PRBS) and using Bierman algorithm. After that this model was used to design the GPC system. Tuning parameters of the GPC system have been calculated using the simulation program of the packed distillation column. Using the predicted parameters, experimental and theoretical GPC systems were found very effective in controlling the overhead temperature. Dynamic matrix control (DMC) system based on a nonparametric model has been used to track the overhead temperature of the packed distillation column. For this purpose, a nonparametric model known as the dynamic matrix was determined using the reaction curve method. A step change in heat input to the reboiler was applied to the manipulated variable and the temperature of the overhead product was observed. After that, the dynamic matrix was used to design the DMC system. Several calculations have been done to define the DMC control parameters. The best values of the tuning parameter were used to realize the DMC system for controlling the overhead temperature experimentally and theoretically. In the presence of some disturbances, the DMC system gives oscillation and offset in experimental studies.