Valorization of refinery flue gas through tri‐reforming and direct hydrogenation routes
Bibliographic record
Abstract
Abstract The rising amount of greenhouse gases has been contributing to global warming and, subsequently, climate change. Industries such as refineries and power plants emit a significant amount of CO 2 into the atmosphere. It is imperative to curb anthropogenic CO 2 emissions, and hence, in this effort, we explore the utilization of a refinery emission stream to produce value‐added chemicals. The chosen emission stream for the said purpose is a typical flue gas stream from refineries. Following the capture step, this CO 2 stream has been leveraged for subsequent valorization processes. Two strategies have been proposed in this paper to valorize the captured carbon dioxide. The first strategy employs the tri‐reforming process with a refinery‐specific fuel gas stream as the co‐reactant. The resulting syngas from tri‐reforming has been converted to chemicals such as methanol (MET) and ethanol. Furthermore, to improve the amount of CO 2 valorized, another approach with green hydrogen has been considered. The second strategy aims at direct hydrogenation of the captured CO 2 stream to produce MET and ethanol. The proposed strategies analyze the feasibility of valorizing captured CO 2 from flue gas to MET and ethanol in terms of gross margin per feed and percentage of CO 2 valorization. The performance assessment and analysis of the proposed processes have been carried out using simulations in Aspen Plus® that exhibited up to 74% valorization of CO 2 into valuable chemicals.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".