GOBF-ARMA based model predictive control for an ideal reactive distillation column.

Area of Science:

• Chemical Engineering
• Process Control
• Green Chemistry

Background:

• Reactive distillation (RD) integrates reaction and separation, offering advantages like improved selectivity and energy efficiency, aligning with green chemistry principles.
• The complex dynamics of RDCs, including nonlinearities and multiple steady states, necessitate advanced control strategies for effective operation.
• Conventional control methods struggle with the intricate interplay of vapor-liquid equilibrium, mass transfer, diffusion, and chemical kinetics inherent in RD.

Purpose of the Study:

• To develop and evaluate a model predictive control (MPC) scheme for an ideal reactive distillation column (RDC).
• To utilize a combined Generalized Orthonormal Basis Filter (GOBF) and Autoregressive Moving Average (ARMA) model for capturing RDC dynamics.
• To demonstrate the controller's effectiveness in maintaining product purity under various operating conditions and constraints.

Main Methods:

• A hybrid GOBF-ARMA model was developed to accurately represent the complex dynamics of the RDC.
• Model predictive control (MPC) was designed based on the derived GOBF-ARMA model.
• Simulations were conducted to analyze the performance of the GOBF-ARMA based MPC for RDC control.

Main Results:

• The GOBF-ARMA model successfully captured the intricate dynamics of the reactive distillation column.
• The MPC controller designed using the GOBF-ARMA model demonstrated satisfactory performance in simulations.
• The proposed control strategy effectively handled reference tracking and disturbance rejection tasks.

Conclusions:

• The combination of GOBF and ARMA models provides an effective approach for modeling RDC dynamics.
• The GOBF-ARMA based MPC is a viable and effective control scheme for maintaining product purity in RDCs.
• This approach offers a promising solution for advanced process control in reactive distillation systems.