Integrating the Benefits of Turquoise Hydrogen to Decarbonise High-Emission Industry
Bibliographic record
Abstract
Total indirect greenhouse gas (GHG) emissions from oil and gas operations today are around 5,200 Mt of carbon dioxide equivalent (CO2-eq) yearly, 15% of total energy sector GHG emissions. Most of these emissions occur due to natural gas leaks. Methane, a much more potent GHG than CO2, is the most significant single component of natural gas and, therefore, of these emissions. Part of these emissions results from routine operations such as flaring and venting, representing both an economic and an environmental issue. Many solutions have been developed to recover and use this natural gas instead of venting and flaring it. Three possibilities were simulated with AVEVA PRO/II, and a preliminary economic assessment was carried out with Guthrie's method. 30 kmol/hr of natural gas fed was assumed, according to average site data, therefore, small-scale plants are suitable. A first solution based on compression, though requiring high OPEX (> 280 k$/y), produces very low emissions yearly (1,140 t CO2-eq/y). Another possibility is to couple flaring to a microturbine for energy generation, but this solution is both uneconomical and has a high environmental impact (> 10,000 t CO2-eq/y). The last technology analysed is thermal methane pyrolysis. This possibility, often disregarded in environmental studies, involves the production of turquoise hydrogen and carbon black. Although characterised by high capital costs (almost 3 M$), it can reduce gaseous emissions since it stores the carbon part of hydrocarbons in the solid matrix that is formed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".