OVERALL INTEGRATION OF THE MANAGEMENT OF H<sub>2</sub>AND CO<sub>2</sub>WITHIN REFINERY PLANNING USING RIGOROUS PROCESS MODELS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract New CO2 legislation forces the petroleum refining industry to review its operations and processes to cope with the new limitations of allowable CO2 emissions. Simultaneously, petroleum refineries, which are extremely complex entities, face another challenge represented by clean fuel products (low sulfur content) regulations. In an attempt to provide operational solutions to these issues, a CO2 management model was incorporated with an existing hydrogen management model that we have recently developed. To this end, this article presents an overall integrated model that solves simultaneously the refinery planning, hydrogen, and CO2 management problems. It addresses the optimum CO2 strategy selection through integration of refinery planning with the hydrogen network and CO2 emissions. The overall model was formulated as a mixed integer nonlinear program (MINLP). The model consists of the refinery emission sources and the considered mitigation options. Model performance was tested through different case studies with various reduction targets. The optimization results showed that the integration of the planning, hydrogen, and CO2 models lead to better profit margins and that CO2 mitigation options worked successfully together to meet a given reduction target. The obtained results also showed that the load shifting option can contribute up to a 3% reduction of CO2 emissions, while the fuel switching option can provide a 20% reduction. To achieve greater than 30% reductions, a CO2 capture technology must be employed in the petroleum refining industry. Keywords: CO2 managementHydrogen managementProcess integrationProcess systems engineeringRefinery planning
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it