469050 Model Predictive Control of Semicontinuous Distillation Process
The system is dynamic and control driven and the cycle time is a function of initial tank hold up, column diameter, number of trays and controller parameters . In the last 16 years, the main focus on semicontinuous research was on the design of the process. These studies have shown that the semicontinuous system can economically outperform the conventional continuous configuration for low production rates . In all these studies, proportional integral (PI) controllers were used. PI controllers are easy to implement and show satisfactory performance for the system but tuning its parameters is tedious since the conventional tuning rules cannot be implemented for this dynamic system. On the other hand, due to high variable interactions and nonlinearities of the system, advanced control strategies such as model predictive control (MPC) can improve the performance and the economics of the system.
In this study, for the first time, the implementation of MPC on a semicontinuous system is studied. The separation of benzene, toluene and o-xylene was chosen as a case study. The first principal model of the semicontinuous system was simulated in gPROMS using its public model library (PML) . The simulation results were sent to MATLAB (where the MPC algorithm was implemented) through the gO:MATLAB feature of gPROMS. The subspace identification method was adopted to identify a linear time invariant state-space model to be used in the MPC . The model was identified using the input-output data collected from the process. Subsequently, a shrinking horizon MPC was implemented to obtain the desired purity of intermediate component in the tank and the average purities of the distillate and bottom streams by the end of the cycle while reducing the operating cost. In this implementation, the PI controllers drive the process and stabilize the cycle while the MPC determines the set-points of those PI controllers. Compared to the optimal PI control configuration (the best previously known), the MPC strategy resulted in an 11% improvement in the energy consumption of the system.
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