Energy savings bottleneck diagnosis of cooling system based on integrated correlation analysis
Bibliographic record
Abstract
Abstract The circulation cooling system is an important auxiliary system in industry and has great potential for saving energy. However, a holistic energy savings retrofit of the system can cause high costs and a low input–output ratio. To address this problem, this paper integrates grey correlation analysis and partial correlation analysis to propose a new energy savings bottleneck diagnosis method for the circulating cooling system. This method deeply analyzes the operation mechanism of the industrial circulating cooling system and summarizes the main energy‐savings factor. Through the energy savings bottleneck diagnosis method based on grey correlation analysis and partial correlation analysis, the energy savings correlation coefficient and energy savings difference coefficient are calculated, and the relationship between each energy savings factor and the specific energy consumption is accurately described and quantified. Furthermore, the system energy savings bottleneck diagnosis is performed and the quantified energy savings priority of each system link is obtained. It can effectively guide the implementation of system energy savings optimization and retrofit. Finally, the effectiveness of the diagnosis method is demonstrated by simulation verification with actual network data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".