A Mechanistic Model for the Water Gas Shift Reaction Over Commercial Catalysts Containing CuO/ZnO
Bibliographic record
Abstract
The water gas shift (WGS) and reverse water gas shift (RWGS) reactions are important in a great number of chemical processes where the adjustment of the relative amounts of CO2, CO and H2 is important. So-called "low temperature shift", LTS, catalysts, normally operating in the temperature range 175 to 325oC, have been commonly based on CuO/ZnO although Al2O3 is normally also present in commercial catalysts. Such LTS catalysts have also been almost universally used in the past two or three decades to promote the methanol-steam reforming reaction for the production of hydrogen. The general application for such hydrogen has been as the feed to a fuel cell.Our group has done extensive work in developing understanding of this methanol-steam reforming (MSR) process with one of the major objectives being development of the ability to predict CO production rates for a variety of process conditions. It is now generally agreed that the WGS/RWGS reactions play a significant role in determining the composition of the product gas leaving the reformer. Since most of our interest has been in PEM fuel cells, for which CO is a serious anode poison, much of our MSR work has been directed to the development of mechanistic reaction models which can be used to design processes with minimum CO yield. Two publications (Peppley et al, 1999a and 1999b) summarize our proposed MSR reaction model, a key feature of which is a proposed mechanistic WGS/RWGS model.The paper demonstrates the application of this WGS/RWGS model to kinetic data for feeds such as CO+H2O, CO2+ H2, and simulated reformate, H2/CO2/CO/H2O, mixtures. Test pressures were at or near atmospheric. Results are presented for two commercial catalysts- one a CuO/ZnO and the other a CuO/ZnO/Al2O3 formulation. The thermodynamic consistency of the kinetic results is discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".