MODEL IDENTIFICATION AND CONTROL OF INTERACTING LEVEL PROCESSES USING OPTIMIZATION ALGORITHMS

Abstract

Interacting level processes are complex systems that are commonly found in industrial applications, where multiple tanks are interconnected and controlled by a common control system. In this study, we propose a model identification and control approach for interacting level processes using optimization algorithms such as model predictive control (MPC) or linear quadratic regulator (LQR) control. The proposed approach involves the identification of a mathematical model of the interacting level process using system identification techniques, and the use of the identified model to design an optimized control strategy. A case study involving a two-tank interacting level process is presented, and simulation results demonstrate the effectiveness of the proposed approach in improving control performance.