A Combined Approach for Production Parameter Selection and On-site Energy Supply Management in Manufacturing Industry
Bibliographic record
Abstract
Integrated management of manufacturing plant’s production and on-site energy supply systems has shown potential economic, environmental and resource efficiency advantages for the industry. However, existing approaches are solely based on pure mathematical models with a high degree of abstraction with limited applicability, which becomes impractical for industrial applications. In this paper a simulation methodology for production parameters selection and on-site energy supply management is presented. In this case, state-based models and operational strategies of manufacturing processes and on-site energy supply options are integrated to represent interdependency between production processes, technical building services and on-site energy supply system. As a result, the proposed methodology covers manufacturing system complexity without compromising the required accuracy. This is applied to a batch based manufacturing plant and the impact of particular production parameters on energy demand profile is evaluated. The results indicate the impact of production parameters on energy supply system. In addition, the proposed approach enables manufacturers to evaluate the implications of potential production approaches in order to select appropriate operational strategies for on-site energy supply systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 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".