Real‐time resource efficiency indicators for monitoring and optimization of batch‐processing plants
Bibliographic record
Abstract
Abstract This paper presents a framework for the definition and calculation of Real‐Time Resource Efficiency Indicators (REIs) for batch processes. The indicators are based on the allocation of resource and energy inputs to the individual batches and recipe operations as the basis for a comprehensive analysis and representation of the overall resource efficiency. The framework structures REIs into three categories describing (1) the efficiency of key unit operations, (2) the efficiency of the production of an individual batch, and (3) the overall system performance. Indicators from the last two categories can be propagated along the production process and can be aggregated vertically from units to sections and complete production sites. Chemical production processes often involve batch blending and splitting, separation processes, and continuous production steps. The framework allocates the contributions of the continuous steps to the batches and considers merging and splitting of batches. Thus, it yields a reliable and commensurate set of indicators that can be used to monitor and to optimize the resource efficiency of batch processing plants in real‐time in order to support the decision making process in daily operations. To demonstrate the approach, it is applied to a sugar production process that integrates batch and continuously operated unit operations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".