OPTIMAL OPERATION OF A DIVIDED-WALL COLUMN WITH LOCAL OPERATING CONDITION CHANGES

Abstract. The aim of this paper is optimal operation of a Divided-Wall Column (DWC) based on Self-Optimizing Control (SOC). By now, the proposed SOC methods have been based on linearization of the process. The novelty of this paper is to overcome this shortcoming of the local optimality of SOC. Theoretically, changes in optimal sensitivity matrix from nominal design, due to changes in the operating condition, make SOC deviate from steady state optimality. These deviations from optimal operation, in already available SOC structures, have to be counteracted by the optimization layer in the control structure hierarchy which involves solving a large nonlinear optimization problem online. The proposed method in this paper solves this problem by modeling the optimal sensitivity matrix with Takagi-Sugeno fuzzy inference. This fuzzy inference system is tuned oine. The proposed method is dynamically validated and compared with conventional SOC. The results showed that the conventional SOC had a high value of loss and deviated from the optimal operation. However, in the same operating condition, the proposed method with the aid of Takagi-Sugeno fuzzy inference system, which involves online calculation of the weighted average of some linear functions, imposed a small loss, made DWC track an optimal trajectory, and removed the need for solving the large nonlinear optimization problem online.