Bootstrapped Gross Error Detection for Efficient and Fault-Tolerant Real-Time Optimization
Bibliographic record
Abstract
Real-time optimization (RTO) is a model-based approach for generating economically optimal steady-state process set points. The process model used in RTO requires reconciliation with the plant through parameter estimation, which uses online measurements. In the presence of sensor faults causing measurement bias, the estimation layer can result in suboptimal set points that may violate safety, environmental, or operational constraints. Herein, a gross error detection approach is proposed to determine measurement sets that exclude faults, thus avoiding estimation errors propagating to the set points and ensuring safe operation. This is achieved by computing parameter estimate samples offline using varying measurement combinations and bootstrapping available plant data. The resulting parameter estimates are subjected to single-sample t-tests to determine which estimates are significantly different; these correspond to the measurement that have the highest probability of being faulty. The computational complexity of the algorithm is discussed, whereby it is shown to be related to the observability criteria and number of measurements. A continuously stirred tank reactor with an upper bound on heat generation is used to exemplify the proposed approach in a process safety setting. The incidence of constraint-violating operation is observed to decrease in both frequency and severity when using the proposed framework; thus, the resulting set points are economical while ensuring safe heating limits are respected during operation.
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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 | 0.009 |
| 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 | 0.001 |
| 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".