The Economics of High Temperature and Supercritical Water Electrolysis
Bibliographic record
Abstract
The growth of green energy in recent decades has resulted in increasing demand for hydrogen production with net-zero carbon emissions. Water electrolysis provides a solution to meet this demand, however it is currently too expensive to be cost competitive with hydrogen production methods of higher carbon intensity. High-temperatures and pressures can be leveraged to increase the energy efficiency of water electrolysis through kinetics and thermodynamic benefits, thereby reducing the overall cost of green hydrogen [1]. Additionally, performing water electrolysis directly at high pressures can help to avoid the added cost associated with gaseous hydrogen compression. Little is known about the electrolysis of supercritical water and what benefits it might offer in terms of hydrogen cost reduction [2,3]. In this work, experimental data was collected for supercritical water electrolysis and used to build an electrochemical model suitable for use under those conditions. The results of this model, combined with components of a previously published technoeconomic model for a high-temperature and pressure water electrolysis plant [4], indicate that while supercritical water electrolysis is achievable it is not the most economically efficient choice for hydrogen production. High-temperature and pressure water electrolysis performed under optimal conditions can be used to achieve higher economic efficiency when compared with contemporary water electrolysis solutions. Finally, a thorough optimization of the model presents a grim picture for achieving the US Department of Energy’s $2 kgH2 -1 target through water electrolysis without government subsidy. References: [1] D. Todd, M. Schwager, W. Mérida, Thermodynamics of high-temperature, high-pressure water electrolysis, J. Power Sources. 269 (2014) 424–429. https://doi.org/10.1016/j.jpowsour.2014.06.144. [2] H. Boll, E.. Franck, H. Weingärtner, Electrolysis of supercritical aqueous solutions at temperatures up to 800K and pressures up to 400MPa, J. Chem. Thermodyn. 35 (2003) 625–637. https://doi.org/10.1016/S0021-9614(02)00236-7. [3] P.C. Ho, D.A. Palmer, Determination of ion association in dilute aqueous potassium chloride and potassium hydroxide solutions to 600°C and 300 MPa by electrical conductance measurements, J. Chem. Eng. Data. 43 (1998) 162–170. https://doi.org/10.1021/je970198b. [4] T. Holm, T. Borsboom-Hanson, O.E. Herrera, W. Mérida, Hydrogen costs from water electrolysis at high temperature and pressure, Energy Convers. Manag. 237 (2021) 114106. https://doi.org/10.1016/j.enconman.2021.114106. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".