Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector
Bibliographic record
Abstract
Abstract As one of the main costs of industrial processes, energy consumption is an essential issue that cannot be ignored in the sustainable development of enterprises. Effective energy efficiency evaluation is important, but also challenging. The conventional method typically uses statistic standard for evaluation. However, infected by the market, the raw materials of an industrial process often fluctuate, which will affect the evaluation standard of energy efficiency. Taking this into account will improve the evaluation performance. To this end, a real‐time dynamic energy efficiency evaluation and analysis method based on multi‐objective state transition algorithm with reference vector (RV‐MOSTA) is proposed to address the energy efficiency evaluation problem. The core of the paper is to realize the dynamic energy efficiency evaluation of multiple indicators considering inlet conditions of industrial processes. Therefore, a systematic set of special indicators are firstly developed for the energy efficiency evaluation. Then, RV‐MOSTA is proposed to determine the evaluation standard, which considers the influence of imported inlet conditions and the optimization of multiple evaluation indicators. Furthermore, a degeneration diagnosis method by means of linear discriminant analysis (LDA) can identify the key variables that lead to energy efficiency degeneration. The proposed method is verified through numerical example and an industrial hydrocracking process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".