Feasibility Studies of Pulsed Oxidation Mitigation of CO Poisoning in Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Operation of low temperature polymer electrolyte fuel cells (PEFCs) require up to 99.97% purity hydrogen with CO concentrations mandated to be below 0.2 ppmv by ISO 14687-2 standards [1]. The production and application of such high purity hydrogen increases the fuel price and overall cost of fuel cell operation due to the requirement of additional pre-purification measures. Indian Oil Corporation is the largest producer of reformate hydrogen with up to 99.9% purity in India. It contains traces of methane, carbon monoxide and carbon dioxide as some of the major impurities. This low-cost hydrogen could conceivably be used in PEFCs subject to operando mitigation of CO poisoning with the aid of techniques such as pulsed oxidation [2] and air-bleeding [3] to deliver satisfactory performance [4]. However, the long term impacts of these techniques and their feasibility in terms of overall efficiency and cost must be carefully assessed. In this work, we present a detailed investigation of the pulsed oxidation technique to mitigate the effect of CO poisoning in long term PEFC operation. A single cell PEFC with an active area of 25 cm2 and Pt-Ru as the anode electrocatalyst is subjected to 80 ppm CO fuel at a nominal operating current density. Pulsed oxidation operation for 4000 cycles showed a consistent performance recovery up to 90% compared with pure hydrogen potentials. Performance governing parameters such as average potential, peak potential and droptime are presented to show changes in cell performance and pulsed oxidation efficiency. A one-dimensional transient model is developed to detect variations in cell potential and surface coverages of species during poisoning and its pulsing process. The model results show up to 26 % increase in the overall efficiency of the cell with and without the application of pulsing process as a function of bleed rates (anode flow). The technical and economic feasibility of operating a PEFC with impure hydrogen with pulsing is demonstrated. Acknowledgement: This work was supported by Indian Oil Corporation (R&D) and Simon Fraser University under the SFU-IOCL joint PhD program in clean energy. [1] B. Shabani, M. Hafttananian, S. Khamani, A. Ramiar, A.A. Ranjbar, J. Power Sources. 427 (2019) 21–48. [2] P. J. Sarma, C. L. Gardner, S. Chugh, A. Sharma, E. Kjeang, J. Power Sources (2020) [3] L-Yu Sung, B-Joe Hwang, K-Lin Hsueh, W-Nien Su, C-Chung Yang, J. Power Sources 242 (2013) 264-272 [4] C.L. Gardner, D. Mehta, S. Chugh, E. Kjeang, J. Electrochem. Soc. 166 (2019) F3123–F3135 Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".