PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems
| Autor: | Briceno-Mena, Luis A., Arges, Christopher G., Romagnoli, Jose A. |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1021/acs.iecr.1c04237 |
| Popis: | Widespread adoption of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs) requires models and computational tools that provide accurate scale-up and optimization. Knowledge-based modeling has limitations as it is time consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling on the other hand, is easier to implement, but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are uniquely combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFC was used to generate simulated data (887,735 points) and used to pretrain a neural network source model. Furthermore, the source model developed for HT-PEMFCs was successfully applied to HT-PEM ECHPs - a different electrochemical system that utilizes similar materials to the fuel cell. Experimental datasets from both HT-PEMFCs and HT-PEM ECHPs with different materials and operating conditions (~50 points each) were used to train 8 target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs and between 6.38 and 8.46% for HT-PEM ECHPs). Comment: 13 pages, 8 figures |
| Databáze: | arXiv |
| Externí odkaz: |
|