State estimation of wastewater treatment processes using distributed extended Kalman filters
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Bibliographic record
Abstract
In this work, we develop a distributed state estimation scheme for wastewater treatment processes in the context of extended Kalman filtering. Specifically, we consider a wastewater treatment process that includes a five-compartment reactor and an ideal splitter. First, we present a method to design the sensor network for the process and then discuss how the process may be decomposed into subsystems for distributed state estimation. We present a detailed design of the distributed filters and a detailed distributed state estimation algorithm to coordinate the actions of the different filters. The distributed scheme is compared with a centralized extended Kalman filtering scheme under dry weather conditions. Simulation results show that the distributed scheme can give comparable estimation performance to the centralized scheme or even better performance than the centralized scheme. Also, the distributed estimation scheme is shown to have more stable performance under different noise conditions.
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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