Evaluation of energetic efficiency of the industrial systems by using benchmark energy factor
Bibliographic record
Abstract
The electro-energetic efficiency of Industrial Systems and Processes (IS&P) is currently monitored by using different types of Energy Performance Indicators (EPI). The EPI represents a ratio between energy spent [kWh] per unit of product, area, volume, or other quantity directly related to production. The EPI values are supposed to be collected in a centralized data system enabling benchmarking activity at national level. One of the major barriers for this process is related to ethical and legal issues impeding disclosure of proprietary information. The tedious normalization process due mainly to volatile and un-reliable reference value is another major barrier for benchmarking process. As a result the accuracy of benchmarking IS&P represents always a challenge for governments and for corporations implementing ISO 50001. The paper proposes a new concept of using of Mathematical Model Benchmarking (MMB). The unitless indicator i.e. Benchmark Energy Factor (BEF) overcomes the current barriers. The paper presents the basics of MMB and basic use of BEF for a case study inspired from real life. The MMB concept can be used by any IS&P owner enabling easy implementation of ISO 50001. BEF indicator enables a reliable rating system model describing energetic efficiency of any IS&P and can be used by Utilities (for their DSM programs), NRCAN or U.S. Department of Energy - Energy-Star Certification for Plants Program replacing existent inefficient benchmarking practice. Canadian Standard Association is currently preparing Guidelines of benchmarking specific IS&P by using MMB concept.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".