Upper Bound for the Efficiency of a Novel Chemical Cycle of H<sub>2</sub>S Splitting for H<sub>2</sub> Production
Bibliographic record
Abstract
To develop sustainable hydrogen production to meet its needs in oil sands upgrading and refining, a chemical cycle of hydrogen production from splitting hydrogen sulfide, the waste product from the same industrial sector, has been proposed. There are two routes of chemical processes to conduct the chemistry. Process 1 takes 1 mol of H 2 S and converts it into H 2 and S, and process 2 takes hydrogen sulfide, oxygen, and water (1:1:2 H 2 S/O 2 /H 2 O) as a feedstock and produces 2 mol of hydrogen and 1 mol of sulfuric acid. Similar to the sulfur−iodine cycle of water splitting for hydrogen, this cycle consists of an iodine−iodide loop and a sulfur dioxide−sulfuric acid loop. This study uses the thermodynamic data of the reactions involved to calculate an upper bound of the thermal efficiency of the hydrogen sulfide splitting cycle. With the enthalpy of the H 2 S oxidation reactions as part of the energy input to the cycle, the values of the maximum thermal efficiency for processes 1 and 2 are 0.41 and 0.36, respectively. However, if only external energy that meets the requirement of heat and work for reactions and pumping is taken into account, the thermal efficiency can be higher, at 0.66 and 0.70. It is found that the separations of sulfuric acid and hydrogen iodide respectively from their aqueous solutions generated within the cycle are still the most energy-consuming processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".