Application of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit

TL;DR

This paper introduces a modified multi-model predictive control algorithm for FCCU, improving control of riser outlet and regenerator temperatures using estimated models and handling nonlinearities and constraints effectively.

Contribution

It proposes a multi-model MPC approach based on subspace identification for FCCU, enhancing control accuracy and robustness in nonlinear, multi-input multi-output systems.

Findings

Improved reference tracking performance.

Enhanced disturbance rejection capabilities.

Effective handling of input/output constraints.

Abstract

This paper presents a modified multi model predictive control algorithm for the control of riser outlet temperature and regenerator temperature for the fluid catalytic cracking unit (FCCU). The models of the fluid catalytic cracking unit are estimated using subspace identification (N4SID) algorithm. The PRBS signal is applied as an input signal to estimate the FCCU models. Since the estimated model does not give 100% fit; especially for nonlinear systems having more than one operating conditions, multi-model approach is proposed. In multi model, more than one model of FCCU used in MPC design. The main advantages of proposed method are that it can handle hard input and output constraints and it can be used for multi input multi output processes (MIMO) without increasing the complexity in control design. MATLAB/Simulink is used to estimate the models of FCCU and simulate the results for the controller. The simulation results show that the proposed algorithm provides better result for both reference tracking and disturbance rejection.