Techno–Economic Evaluation of Technologies to Mitigate Greenhouse Gas Emissions at North American Refineries
Bibliographic record
Abstract
A petroleum refinery model, Petroleum Refinery Life-cycle Inventory Model (PRELIM), that estimates energy use and CO 2 emissions was modified to evaluate the environmental and economic performance of a set of technologies to reduce CO 2 emissions at refineries. Cogeneration of heat and power (CHP), carbon capture at fluid catalytic cracker (FCC) and steam methane reformer (SMR) units, and alternative hydrogen production technologies were considered in the analysis. The results indicate that a 3–44% reduction in total annual refinery CO 2 emissions (2–24% reductions in the CO 2 emissions on a per barrel of crude oil processed) can be achieved in a medium conversion refinery that processes a typical U.S. crude slate obtained by using the technologies considered. A sensitivity analysis of the quality of input crude to a refinery, refinery configuration, and prices of natural gas and electricity revealed how the magnitude of possible CO 2 emissions reductions and the economic performance of the mitigation technologies can vary under different conditions. The analysis can help inform decision making related to investment decisions and CO 2 emissions policy in the refining sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".