Utilization of Carbon Dioxide as Regenerative Agent for Deactivated Co-Ni Steam Reforming Catalysts
Bibliographic record
Abstract
Abstract Carbon emission to the environment is now recognised as a major contribution to the climate change imbroglio. The introduction of carbon tax or implementation of an emissions trading scheme by different national governments is now considered a reality in order to mitigate the long-term adverse effects of the carbon pollution and to encourage the industrial sector to move toward green process technologies. Steam reforming of hydrocarbons is the most important route for the commercial production of hydrogen (a clean fuel) but it is also accompanied CO2 (an undesirable greenhouse gas, GHG) emission. Around 7 tons of CO2 are produced and emitted per ton of produced H2. Thus, the recycling and reuse of CO2 for the process benefits may result in a significant improvement in the process efficiency as well as the environment. It is in this respect that the utilisation of CO2 as a carbon gasifying agent for the steam reformer becomes attractive since carbon deposition is a principal cause for loss in online catalyst (Ni-based systems) performance. Specifically, the present technology deals with the periodic forcing of the steam reformer with CO2 to improve both catalyst activity and longevity. Experiments were carried out over Co-Ni/Al2O3 catalyst in a fluidized bed reactor. Cycle period, t, was varied between 10 to 60 mins at 5 different cycle symmetry, s (0.1= s = 0.9). Both H2 and CO formation rates were higher than that attainable under steady-state operation at all periods investigated. In particular, the time-average H2:CO ratio was lower (<3.0) than the steady-state equivalent for the pure propane steam reforming (14.0) although it increased monotonically with cycle split. Composition cycling with CO2 also improved catalyst stability and longevity compared to steady-state performance at the cycle periods examined. This strategic reactor operation is therefore a potentially useful key to green process engineering in the overall petrochemical plant design to effect greenhouse gas emission reduction.
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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".