10.1007/s40095-018-0269-5

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

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  • Zahra Salehi1,
  • Iman Gholaminezhad*2,
  1. Department of Materials Science and Engineering, School of Engineering, Shiraz University, Shiraz, IR
  2. Department of Materials and Life Chemistry, Kanagawa University, Yokohama, Kanagawa, 221-8686, JP
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Published in Issue 2018-03-19

How to Cite

Salehi, Z., & Gholaminezhad, I. (2018). Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells. International Journal of Energy and Environmental Engineering, 9(3 (September 2018). https://doi.org/10.1007/s40095-018-0269-5
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Abstract

Abstract Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based black-box models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input–output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

Keywords

  • Reversible solid oxide cell,
  • Multi-objective,
  • Genetic programming,
  • Pareto,
  • Monte Carlo simulations
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