Control-oriented dynamic model optimization of steam reformer with an improved optimization algorithm

Abstract An effective temperature controller for steam reformers is critical to ensure a high performance reforming process in the connection of Solid Oxide Fuel Cell (SOFC). The establishment of a control-oriented dynamic model plays an important role in the development of a control system. In this work, a high-fidelity lumped parameter model for a steam reformer is constructed based on physical and chemical laws. In order to fit simulated data to experimental data, such as flow rate and temperature characteristics, a new identification method based on a breed particle swarm optimization (Breed PSO) algorithm is introduced for parameter identification. The results show that the identified model can achieve an accurate description of the actual plant and can be used to replace it for the development of a control system.

[1]  Christophe Espanet,et al.  Particle swarm optimization applied to the co-design of a fuel cell air circuit , 2008 .

[2]  Yuwan Cen,et al.  Comparison among three kinds of hybrid particle swarm optimization algorithms , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[3]  Jie Yang,et al.  Parameter optimization for tubular solid oxide fuel cell stack based on the dynamic model and an imp , 2011 .

[4]  Sarwan S. Sandhu,et al.  A reformer performance model for fuel cell applications , 2005 .

[5]  Meiying Ye,et al.  Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization , 2009 .

[6]  Qi Li,et al.  Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization , 2011, IEEE Transactions on Industrial Electronics.

[7]  D Lihou,et al.  OPTIMAL DESIGN OF METHANE STEAM REFORMER , 1971 .

[8]  Xi Li,et al.  Generalized predictive control for fractional order dynamic model of solid oxide fuel cell output power , 2010 .

[9]  Fatola Farhadi,et al.  Radiative models for the furnace side of a bottom-fired reformer , 2005 .

[10]  James Larminie,et al.  Fuel Cell Systems Explained , 2000 .

[11]  Xin-Jian Zhu,et al.  Parameter optimization for a PEMFC model with a hybrid genetic algorithm , 2006 .

[12]  Wolfgang Schroer,et al.  Dynamic simulation model of a steam reformer for a residential fuel cell power plant , 2005 .

[13]  J. van der Schaaf,et al.  Reactor modeling of sorption-enhanced autothermal reforming of methane. Part II: Effect of operational parameters , 2011 .

[14]  Jens R. Rostrup-Nielsen,et al.  Conversion of hydrocarbons and alcohols for fuel cells , 2002 .

[15]  Fabian Mueller,et al.  On the intrinsic transient capability and limitations of solid oxide fuel cell systems , 2009 .

[16]  Brant A. Peppley,et al.  Integrated fuel processors for fuel cell application : A review , 2007 .

[17]  Murat Peksen,et al.  Numerical modelling and experimental validation of a planar type pre-reformer in SOFC technology , 2009 .

[18]  P. Cobden,et al.  Reactor modeling of sorption-enhanced autothermal reforming of methane. Part I: Performance study of hydrotalcite and lithium zirconate-based processes , 2011 .

[19]  S. Galvani,et al.  A particle swarm optimization approach for optimum design of PID controller in linear elevator , 2010, 2010 Conference Proceedings IPEC.

[20]  M. Hyman.  SIMULATE METHANE REFORMER REACTIONS , 1968 .

[21]  Hadi Ebrahimi,et al.  A unified model for top fired methane steam reformers using three-dimensional zonal analysis , 2008 .

[22]  Xue Wang,et al.  Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks , 2007, Sensors (Basel, Switzerland).

[23]  Juliana Piña,et al.  Use of a heterogeneous two-dimensional model to improve the primary steam reformer performance , 2003 .

[24]  R. Chibante,et al.  An experimentally optimized PEM fuel cell model using PSO algorithm , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[25]  Zwe-Lee Gaing,et al.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004 .

[26]  N. Jenkins,et al.  Proton exchange membrane (PEM) fuel cell stack configuration using genetic algorithms , 2004 .

[27]  Thomas F. Coleman,et al.  Optimization Toolbox User's Guide , 1998 .