Determining controller benefits via probabilistic optimization
Bibliographic record
Abstract
Abstract For the most part, process control research has focussed on the synthesis and tuning of controllers, which has provided a plethora of techniques that can address virtually any application. With each new control technique, a steady stream of ‘successful’ application results are generated and reported. Recently, a considerable number of control researchers have turned their attention to assessing the performance of installed control systems and to the diagnosis of controller performance problems. Despite successes in the areas of controller synthesis, tuning and performance analysis, almost no research has addressed the fundamental issue of determining whether the economic performance gains that are expected accrue from a proposed process control project are sufficient to justify its execution. The work presented here proposes an optimization‐based technique for calculating the expected economic performance of a given control system; a method, which is analogous to analysis of variance, for determining the expected economic benefit that will arise from a particular controller improvement effort; and a sensitivity analysis approach for determining the effect of specific assumptions on control system improvement decisions. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".