Multiphase flow and chemical reactor thermodynamics for hydrolysis and thermochemical production
Bibliographic record
Abstract
Current techniques of hydrogen production (primarily reformation of fossil fuels) are unsustainable, releasing CO2 into the atmosphere, as well as consuming limited reserves of fossil fuels. The copper-chlorine cycle is a promising thermochemical process which can cost-effectively produce hydrogen with less environmental impact. In this thesis, new predictive formulations and experimental data are presented to improve the conversion extent and reaction rates of the hydrolysis reactor in the Cu-Cl cycle. This reactor has critical implications for the design, operation, and efficiency of the Cu-Cl cycle and hydrogen production. The relatively high temperature needed to drive the reaction requires a significant input of thermal energy. This thesis focuses on methods and analysis to reduce the unreacted steam in the hydrolysis reactor, in order to reduce the thermal energy input and improve the cycle’s thermal efficiency. A key outcome from this thesis is the experimental verification of reducing the steam to copper chloride ratio from 16:1 (past studies) to about 3:1. The results of this thesis provide key new data to design a more efficient hydrolysis reactor that can be effectively integrated within the Cu-Cl cycle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".