Raw material management networks based on an improved P‐graph integrated carbon emission pinch analysis (CEPA‐P‐graph) method
Bibliographic record
Abstract
Abstract Raw material management plays an essential role in the environment production and carbon emission problem. A P‐graph is an effective tool that can be used to solve the raw material management problem. However, raw material management based on the P‐graph is complicated and inaccurate. Therefore, this paper proposes an improved P‐graph integrating carbon emission pinch analysis (CEPA‐P‐graph) method to resolve raw material management problem with simpler structures and less results. The proposed method is applied in raw material planning in terms of the regional energy planning problem, and two ethylene plants under carbon emission constraints are examined. As the parameter information of the problem is utilized by pinch technology, the search domain of structure optimization is further reduced, and the computing complexity is reduced, while the advantages of the P‐graph multi‐solution are guaranteed. The experimental results demonstrate the validity of the proposed method. Furthermore, the CEPA‐P‐graph could reduce the complexity of solution structure generation by ~70% and reduce the carbon emission per unit product by 17%.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".