Utilization of Ultraviolet-Visible Spectroscopy and Rheology for Sludge Characterization and Monitoring
Bibliographic record
Abstract
Operation of sludge treatment processes mainly relies on manual control, which is far from ideal.There is a need for new approaches to optimize the operation of sludge treatment processes and wastewater plants.This research aims to identify new tools and methods that can be used for inline and real-time characterization and monitoring of sludge.Two methods that were examined in this thesis that have potential to be used as monitoring technologies were ultraviolet/visible spectrophotometry and torque rheology.Effluent and filtrate absorbance measurements in the ultraviolet/visible range were successful in monitoring the progress of aerobic digestion.Torque rheology was not found to be sensitive enough for monitoring aerobic digestion of sludge, however it was able to detect changes in the total solids content of anaerobically digested sludge.Torque rheology detected significant changes in anaerobically digested sludge when trivalent cations were added, but not when divalent cations were added.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".