Adaptive model predictive control improves response time in syngas purification systems, suggesting better process control design.
The Rectisol process plays a pivotal role in syngas purification for clean coal utilization. Feedstock variations during gasification induce syngas flow and composition fluctuations, necessitating adaptive process control. This study develops both proportional–integral–derivative (PID) and model predictive control (MPC) strategies for the CO 2 absorption column. The MPC controller was developed in MATLAB Simulink and innovatively integrated with Aspen Plus Dynamics through the AMSimulation interface module for co‐simulation. The control strategies were evaluated under ±10 % flow and ±2 % CO 2 concentration disturbances, with purified gas quality assessed through response time (RT), overshoot (OS), and the integral of absolute error multiplied by time (ITAE). The data‐driven MPC strategy exhibits enhanced robustness against flow and composition variations compared to conventional PID control. The study offers actionable guidance for control system design in process industries.
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Ren et al. (2025) studied this question.
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