Operator-In-The-Loop Bayesian Optimization Toward Optimal Process Operation
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
Optimal process operation is crucial for maintaining system resilience, playing a key role in ensuring operations to continue safely and without interruption even during system failures. This process involves identifying, diagnosing, and fixing causes within a system to restore its function and prevent further issues. However, many current methods rely heavily on machines and computers, which can encounter errors or become trapped in less-than-optimal conditions. They often overlook the valuable insights gained from operators’ years of experience. To address this gap, this chapter presents a novel approach using operator-in-the-loop Bayesian optimization, which combines Bayesian optimization techniques with operator expertise. The proposed method is demonstrated through a case study of a polyvinyl chloride (PVC) production plant, modeled in Aspen HYSYS, and further validated for its practical use with an experimental continuous stirred tank reactor (CSTR) setup.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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