SER Process Variable Evaluation for the Production of Hydrogen using Calcined Dolomite
Bibliographic record
Abstract
Reaction performance of the sorption enhanced reforming (SER) process for the production of hydrogen was studied using commercial dolomite as inexpensive solid CO2 absorbent. The combined reforming, shift, and CO2 separation reactions were studied using a laboratory-scale fixed-bed reactor as a function of temperature, feed gas composition, dolomite type, and dolomite and catalyst particle sizes. Reactor was loaded with a mixture of calcined dolomite (≈ 23g) and a commercial reforming catalyst (NiO/Al2O3, ≈ 10g). Temperature was varied from 550 to 650°C at 15 atm. Feed gas composition was varied from 6 to 20% CH4/balance N2 and steam, with a feed H2O/CH4 ratio = 4. Two sources of dolomite were used; Rockwell and Stonelite. Particle sizes of dolomite and catalyst were 75>dp>150 μm and 300>dp>425 μm, respectively and were inversely varied. Results show that at 550°C Ca(OH)2 formation is possible, thus reducing the available CaO for carbonation, negatively affecting the performance of the SER system, while 650°C reached the SER thermodynamic equilibrium (TE). The use of dolomite approached the TE of the feed gas compositions studied, disregarding of its source. Kinetic effects observed in the tests suggest that small dolomite and large catalyst particles favor the decrease of CO2 diffusion effects.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".