459275 Steam Methane Reforming Furnace Control: Design and Implementation on a CFD Model of an Industrial Furnace

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Andres Aguirre, Chemical Engineering, UCLA, Los Angeles, CA, Anh Tran, UCLA, Los Angeles, Helen Durand, Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, Marquis Crose, UCLA, Los Angeles, CA, Zhe Wu, Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA and Panagiotis D. Christofides, Department of Chemical and Biomolecular Engineering and Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA

The steam methane reforming (SMR) process is one of the most effective and economical processes for hydrogen production on a commercial scale, i.e., it alone accounts for more than 40% of the worldwide production of hydrogen [1]. Due to the importance of hydrogen as a raw material for many essential processes in petroleum refineries, e.g., the desulfurization process and hydrocracking process, much research has been performed on the optimization of the operating conditions of the SMR process and on formulating feedback control algorithms to compensate for the effect of process disturbances. It is noteworthy that the SMR process is an overall endothermic process in which methane reacts with superheated steam in a tightly packed nickel-based catalyst network to produce hydrogen, carbon dioxide and carbon monoxide. Therefore, a higher outer reforming tube wall temperature theoretically results in a higher hydrogen conversion. Nevertheless, operating at excessively high temperature decreases the reforming tube expected life span, i.e., an increase in outer reforming tube wall temperature of 20 K relative to its nominal operating temperature can reduce the reforming tube expected lifetime by half [1] [2]. In addition, it might cause the reforming tube wall to lose its integrity and to rupture, which results in production and capital losses. Specifically, the required total capital investment to replace a typical industrial-scale reformer is estimated to be 5 − 8 million USD [2]. This motivates the development of robust feedback control schemes to maintain the SMR process at the optimized operating conditions in the presence of process disturbances.

From previous work, a CFD model of an industrial-scale reforming tube and a CFD model of a pilot-scale reformer have been successfully developed and validated with the available typical plant data and the publically available experimental data reported in literature [3] [4]. Additionally, our previous work to design feedback control schemes for the industrial-scale reforming tube assumes that the outer reforming tube wall temperature can reach the optimized profile instantaneously and does not account for the dynamics of the temperature of the outer reforming tube wall. This work aims to develop a more realistic single industrial-scale reforming tube CFD model by accounting for the dynamics of the outer reforming tube wall temperature when it is controlled by the feedback control scheme. Specifically, the pilot-scale reformer CFD model is employed, and the furnace-side feed flow rates to the individual burners are subjected to three different step change inputs. The evolution of the outer reforming tube wall temperature is recorded as the pilot-scale reformer reaches the corresponding steady-state condition, and the dynamics of the outer reforming tube wall temperature are obtained and implemented into the industrial-scale reforming tube CFD model. Finally, classical (i.e., proportional control scheme and a proportional-integral control scheme) as well as optimization-based control schemes (i.e., model predictive control), for which the manipulated input and controlled output are chosen to be the outer reforming tube wall temperature and outlet area-weighted average hydrogen mole fraction, are used to form the closed-loop CFD simulation to track the desired set-point in the presence of process disturbances.

[1] Latham D. Masters Thesis: Mathematical Modeling of an Industrial Steam Methane Reformer. Queen’s University, 2008.

[2] Pantoleontos G, Kikkinides ES, Georgiadis MC. A heterogeneous dynamic model for the simulation and optimisation of the steam methane reforming reactor. International Journal of Hydrogen Energy. 2012;37:16346-16358.

[3] Lao L, Aguirre A, Tran A, Wu Z, Durand H, Christofides PD. CFD modeling and control of a steam methane reforming reactor. Chemical Engineering Science. 2016;148:78-92.

[4] Aguirre A, Tran A, Lao L, Durand H, Crose M, Christofides PD. CFD Modeling of a Pilot-Scale Steam Methane Reforming Furnace. Advances in Energy Systems Engineering, Kopanos G, Liu P and Georgiadis M (Eds.), Springer, in press.


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See more of this Session: Interactive Session: Systems and Process Control
See more of this Group/Topical: Computing and Systems Technology Division