engleski

Advanced Operation of the Steam Methane Reformer by Using Gain-Scheduled Model Predictive Control

A rigorous computationally effcient closed-loop system with a gain-scheduled model predictive controller (MPC) is developed for the first time, where a first-principle model of the steam methane reformer is utilized to represent the process dynamics. A dynamic model for a generic primary gas reformer is developed using a homogeneous- phase one-dimensional reaction kinetics model to describe the chemical reactions inside the reforming tubes and to compute the external heat transfer to the reformer tubes by radiation and convection. The gain-scheduled MPC considers critical process parameters of the steam methane reformer such as outlet methane molar concentration and outlet temperature of the reformed gas as the most appropriate and reliable process variables. The developed gain-scheduled MPC demonstrates adaptive and advanced operation of the steam methane reformer at three different steam-to-carbon ratios. By simulation of the set- point changes under the influence of the steam methane reformer critical disturbance rejection performances, it is shown that reformer tubes could operate in a safe temperature range. The model determines optimal trajectories of the reformed syngas outlet temperature and methane outlet molar concentration based on tracking the set point under the influence of the manipulated variables—temperature and mass rate of mixed feed and fuel flow rate for burners. The gain-scheduled MPC is compared with already proven standard process control solutions based on proportional- integral-derivative (PID) controllers. Proposed control strategy benefits include energy savings in the range from 3% to 5% and prolonged lifetime of the reformer tubes and catalyst.

model predictive control ; optimization ; simulation ; steam methane reformer

nije evidentirano