Soft Sensor Design for Estimation of SOFC Stack Temperatures and Oxygen-to-Carbon Ratio

Friday, 31 July 2015: 14:00
Lomond Auditorium (Scottish Exhibition and Conference Centre)
B. Dolenc, D. Vrecko, D. Juricic (Dept. of Systems and Control, Jozef Stefan Institute), A. Pohjoranta (VTT Technical Research Centre of Finland), J. Kiviaho (VTT Technical Research Centre of Finland Ltd), and C. Pianese (Dept. of Industrial Engineering, University of Salerno)
The lifespan of a solid oxide fuel cell (SOFC) stack depends on several factors, such as the internal stack temperature and temperature gradients as well as the fuel gas oxygen-to-carbon (O/C) ratio within the stack. An excessively high stack operating temperature generally accelerates degradation processes while large temperature gradients over the stack increase the thermal stress within the stack which may lead to the delamination of repeating unit components. A too low O/C ratio inflicts carbon deposition which quickly leads to stack breakage. Therefore, monitoring these variables is of vital importance. Although measuring temperatures within the stack and measuring the fuel gas composition at the stack inlet is feasible, this raises the overall equipment cost. In this paper a data-driven design of soft sensors for stack minimum and maximum temperatures as well as the O/C ratio at the anode and pre-reformer inlet is presented. For temperature estimation both dynamic and static models are derived and their performance is compared. The dynamic model is identified using subspace identification, which results in a causal state-space model. The non-causal static model assumes that a combination of process variables at the stack inlet and outlet describe its internal condition. The estimation of gas composition at the inlets, which is required for O/C ratio estimation, is based solely on static relationships. The soft sensors are designed in such a way that adding extra inputs yields no further increase in estimate accuracy. The empirical data required for modelling were obtained from a complete SOFC power generation unit. The results show that estimates of all the relevant variables can be accomplished by simple linear regression models.
Keywords: SOFC, soft sensor, O/C estimation, temperature estimation