Model-based advanced process control of coagulation
Why this work is in the frame
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Bibliographic record
Abstract
The drinking water treatment industry has seen a recent increase in the use of artificial neural networks (ANNs) for process modelling and offline process control tools and applications. While conceptual frameworks for integrating the ANN technology into the real-time control of complex treatment processes have been proposed, actual working systems have yet to be developed. This paper presents development and application of an ANN model-based advanced process control system for the coagulation process at a pilot-scale water treatment facility in Edmonton, Alberta, Canada. The system was successfully used to maintain a user-defined set point for effluent quality, by automatically varying operating conditions in response to changes in influent water quality. This new technology has the potential to realize significant operational cost saving for utilities when applied in full-scale applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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