Rapid estimation of biosludge polymer demand for dewatering via image classification
Bibliographic record
Abstract
Applying the optimal polymer dose to biosludge prior to dewatering can help minimize the economic and environmental costs of disposal. However, measuring the polymer demand is a time-intensive process for wastewater treatment plants, and biosludge characteristics vary over time. A novel, rapid image-based polymer demand estimation tool was developed by designing a simple and accessible image capture station and applying transfer learning of an open-source image classifier. Images were captured with a smartphone fixed in an enclosed structure above a biosludge sample dosed with polymer in a 150 mm petri dish. To facilitate collecting the required image database, the entire structure was fastened to an orbital shaker to intermittently apply shake-steps between image capturing that simulate replicates. Using TensorFlow in Python, image databases were constructed and used to retrain EfficientNetV2 to classify images of dosed biosludge as either underdosed, well-dosed, and overdosed. Two image-based models were constructed, one for pulp and paper mill biosludge, and the other for municipal biosludge, which achieved 86 % and 83 % accuracy against an unseen set of images.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".