Machine learning for guiding high-temperature PEM fuel cells with greater power density.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 33659908.
- Also identified by DOI 10.1016/j.patter.2020.100187 and PMC identifier 7892359.
- Licence recorded as CC BY-NC-ND.
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Abstract
High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm<sup>-2</sup> in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen.