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

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.