Design of a Pilot SOFC System for the Combined Production of Hydrogen and Electricity under Refueling Station Requirements1. Issue 4 (28th May 2019)

• Record Type: Journal Article
• Authors: Pérez‐Fortes, M.; Mian, A.; Srikanth, S.; Wang, L.; Diethelm, S.; Varkaraki, E.; Mirabelli, I.; Makkus, R.; Schoon, R.; Maréchal, F.; Van herle, J.
• Other Names: Ivers#x02010;Tiffée Ellen guestEditor.; Weber André guestEditor.; Bucheli Olivier guestEditor.
• Is Part Of: Fuel cells. Volume 19:Issue 4(2019)
• Page Start: 389
• Page End: 407
• Publication Date: 2019-05-28
• Subjects: Conceptual Design, Electric Vehicle Station, Fuel Cell -- Heat Exchanger Network (HEN) -- Hydrogen Refueling Station (HRS) -- Industrial Chemistry -- Multi‐Objective Optimization (MOO) -- Multi‐Period Optimization -- Process System Engineering (PSE) -- Solid Oxide Fuel Cell (SOFC); Fuel cells -- Periodicals; 621.312429
• DOI: 10.1002/fuce.201800200
• Languages: English
• ISSNs: 1615-6846

Abstract

Abstract: The objective of the current work is to support the design of a pilot hydrogen and electricity producing plant that uses natural gas (or biomethane) as raw material, as a transition option towards a 100% renewable transportation system. The plant, with a solid oxide fuel cell (SOFC) as principal technology, is intended to be the main unit of an electric vehicle station. The refueling station has to work at different operation periods characterized by the hydrogen demand and the electricity needed for supply and self‐consumption. The same set of heat exchangers has to satisfy the heating and cooling needs of the different operation periods. In order to optimize the operating variables of the pilot plant and to provide the best heat exchanger network, the applied methodology follows a systematic procedure for multi‐objective, i.e. maximum plant efficiency and minimum number of heat exchanger matches, and multi‐period optimization. The solving strategy combines process flow modeling in steady state, superstructure‐based mathematical programming and the use of an evolutionary‐based algorithm for optimization. The results show that the plant can reach a daily weighted efficiency exceeding 60%, up to 80% when considering heat utilization.