| Autor: |
David C. Miller, Arun Iyengar, Dewei Wang, Anthony P. Burgard, Miguel Zamarripa-Perez, Alexander Noring, Zhijie Xu, Jie Bao, Brian J. Koeppel |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ECS Meeting Abstracts. :26-26 |
| ISSN: |
2151-2043 |
| Popis: |
This work aims to optimize a solid oxide fuel cell (SOFC)-based natural gas fuel cell (NGFC) system with the potential for higher heating value (HHV) efficiencies as high as 70 percent. This work leverages the IDAES Integrated Platform (IDAES), an equation-oriented tool for the nonlinear optimization of advanced energy systems, which enables a broader exploration of the potential design space than convention case-based analyses. The NGFC system model without carbon capture consists of a natural gas reformer, SOFC power island, and steam turbine bottoming cycle. The mass and energy balances are modeled with first principles models, while the SOFC behavior is captured utilizing a machine learning-based reduced order model (ROM) developed from a validated, high fidelity CFD stack model. The objective of the study is to find the optimal NGFC configuration and operating conditions that maximize the system efficiency and reduce carbon dioxide emissions. The results of the optimization to date have led to improved system performance (higher efficiency). Further work will focus on analyzing different potential configurations to increase efficiency and reduce CO2 emissions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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