Modeling and Control of Steam Methane Reforming Process Using Model Predictive Control
Keywords:
Steam Methane Reforming, Model Predictive Control, dynamic simulation, hydrogen production, process controlAbstract
This study focuses on modeling and controlling steam methane reforming (SMR) using model predictive control (MPC). The problem in the SMR plant is the limitation of the conventional control in controlling a wide range of operating conditions. The nonlinear dynamic behavior of the SMR process has further complicated the situation, as existing methods lack the adaptability to the changing operating conditions and disturbances. To address these challenges, MPC control scheme is proposed. The development of MPC involves developing a simulation process for Steam Methane Reforming (SMR) using Aspen Plus, creating the process model of SMR using the system identification technique, and designing the MPC control scheme. The development of a simulation plant includes constructing the process flowsheet, determining kinetics parameters, and creating a dynamic model. Model input-output selection and data generation are performed to facilitate the development of the process model. Based on the state space identification technique, the process model is developed with a normalized root mean square error (NRMSE) of 0.8567 for CV1 and 0.3005 for CV2. Then, the core focus lies in designing the MPC control structure and tuning the MPC for enhanced performance. During set point tracking, starting from steady-state hydrogen production and increasing by 20% at 2 minutes, the state-space MPC outperformed the PID controllers, displaying more aggressive capabilities in reaching the desired set point. For the disturbance rejection test, the state-space MPC is able to control the reactor outlet temperature with minimal overshoot, as seen in the CV2 profile. This behavior is due to the predictive capabilities of MPC, enabling quicker controller actions than PID. By addressing the traditional control limitations, the proposed MPC aims to enhance the operation and control of SMR plants while optimizing hydrogen production through advanced control strategies.
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