Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells

Luis A. Briceno-Mena, Christopher G. Arges, Jose A. Romagnoli

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Widespread adoption of high-temperature polymer electrolyte membrane electrochemical systems, such as fuel cells (HT-PEMFCs), requires models and computational tools for accurate optimization and guiding new materials for enhancing performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models. Applicability of the resulting model for derivative-free optimization is demonstrated. Additionally, a way of considering extrapolation in the optimization task is presented. Results show that although extrapolation is needed to achieve better solutions during optimization, it can be monitored and managed. Tuning the electrode ionomer binder's properties, such as ionic conductivity, in the fuel cell represents a promising pathway for improving HT-PEMFC performance.

Original languageEnglish (US)
Article number108159
JournalComputers and Chemical Engineering
Volume171
DOIs
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells'. Together they form a unique fingerprint.

Cite this