Design and analysis of a thermolysis reactor for scaled-up copper-chlorine hydrogen production cycle
Bibliographic record
Abstract
Alternative hydrogen production methods are being explored with the goal of\nfinding efficient and economical process. The copper-chlorine (Cu-Cl) cycle for\nhydrogen production has been the focus of the Clean Energy Research\nLaboratory (CERL) at the University of Ontario Institute of Technology\n(UOIT). The Cu-Cl cycle has lower thermal energy requirements compared to\nother methods and utilizes waste heat from power plants and/or some\nindustrial processes. The cycle includes the electrolysis, hydrolysis and\nthermolysis reaction steps. Decomposition of copper oxychloride (CuOCuCl2)\noccurs in the thermolysis reactor between 480??C and 530??C. A thermolysis\nreactor design is presented here with the purpose of scaling it up for a pilot\nplant of the Cu-Cl process. Transient thermal simulations were conducted with\n2.0kg of cuprous chloride (CuCl), single and dual heating sources, and 1, 2 and\n4 Wm-2K-1 internal surface convection rates. The dual heater configuration\nprovided the required temperature distribution to allow decomposition to\noccur. Experimental data with dual heat sources showed that surface\ntemperatures reached 531??C ?? 14.0??C. Faster heating was observed with\ngranular CuCl in comparison to solidified CuCl, because the material was\nallowed to mix in the reactor while it was melted. Simulations with 10.35kg\nCuCl confirmed adequate surface temperatures for decomposition at low\nconvection rates. Fouling in the phase separation section was observed: XRD\nanalysis showed that the bottom was crystalized CuCl while the upper section\nwas a mixture of predominately CuCl2 dihydrate and CuCl. The vapor\nproduction was due to temperatures exceeding 530??C at the CuCl-crucible\ninterface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".