Kang, Xin, Chen, Yong, Fan, Liyuan, and Huang, Deqing (2019) Iterative learning control of methane steam reforming reaction in operating solid oxide fuel cell. In: Proceedings of the 2019 IEEE 8th Data Driven Control and Learning Systems Conference. 8908946. pp. 586-591. From: DDCLS 2019: IEEE 8th Data Driven Control and Learning Systems Conference, 24-27 May 2019, Dali, China.

PDF (Published Version) - Published Version Restricted to Repository staff only

 

1

Abstract

As one kind of new power plants, solid oxide fuel cell (SOFC) possesses distinctive advantages, such as the high efficiency in energy conversion, the tiny effect towards environmental pollution, and the abundant fuel sources, etc. Nevertheless, the drastic chemical reactions inside and the high operating temperature (up to 1000°C) lead to a series of problems in structural analysis, controller design as well as safety concern of SOFC. In this paper, a novel iterative learning control (ILC) scheme are proposed for the precise management of the efficiency of methane conversion in SOFC, where the reaction temperature is selected as the control input for the single-input scenario. The learning convergence condition of ILC, the learning rate as well as the robustness are derived through rigorous analysis. The simple structure and model-free nature of ILC makes it applicable although the methane steam reforming (MSR) process is with complicated dynamics and severe potential uncertainties. The effectiveness of the proposed ILC schemes is confirmed via numerical simulations.

Actions (Repository Staff Only)

Item Control Page