Evaluation of energetic efficiency of the industrial systems by using benchmark energy factor
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
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.
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 | 0.001 | 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