Real‐time resource efficiency indicators for monitoring and optimization of batch‐processing plants
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
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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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 | 0.000 | 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