An Optimization Approach for Integrating Planning and CO<sub>2</sub> Emission Reduction in the Petroleum Refining Industry
Bibliographic record
Abstract
The petroleum refining industry plays a very important role in international economics and in our daily life. The world refining capacity has increased rapidly during the past decade, and this makes operation planning, scheduling, and general optimization become important tools for the refinery industry. However, environmental regulations and risks of climate change are pressuring the refinery industry to minimize its greenhouse gas emissions. In this research, a mixed-integer nonlinear programming (MINLP) model is proposed for the production planning of refinery processes to achieve maximum operational profit while reducing CO 2 emissions to a given target through the use of different CO 2 mitigation options. The options considered in this study are flow-rate balancing (decreasing the inlet flow rate to a unit that emits more CO 2 ), fuel switching (changes in a certain operation to run with a different fuel that emits less CO 2 emissions, such as natural gas), and installation of a CO 2 capture process (e.g., the monoethanolamine (MEA) process). The objective of the MINLP model is to determine suitable CO 2 mitigation options for a given reduction target while meeting the demand of each final product and its quality specifications, while simultaneously maximizing profit. In this study, a global optimization algorithm is used on the different case studies considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".