Optimisation for enhancing sludge dewaterability using different conditioners
Bibliographic record
Abstract
Dewatering of alum sludge from drinking-water plants is proving to be a major challenge because of the large amounts of residual sludges produced annually. In the last few years, most studies have focused on improving the dewatering process to reduce costs of alum sludge management and transport. In the present study, three different types of conditioners were tested. Lime was used as an example of an inorganic conditioner, ferric chloride (FeCl3) was used as an example of a chemical conditioner and chitosan was used as an example of a biopolymer. The performance of a conditioner was evaluated with respect to its effect in reducing the resistance of the conditioned sludge to filtration, namely the specific resistance to filtration (SRF). Tests are run using different concentrations of the conditioners, different speeds of rotation and different pH values to investigate the maximum value of percentage reduction in SRF. The response surface methodology was chosen from the Design-Expert program (version 12), and the Box–Behnken design was employed to find factor settings that optimise the output response – that was, percentage reduction in SRF (Red. %). The model obtained proved to be significant enough but with varying degrees. Chitosan showed to be the most favourable conditioner with a maximum percentage reduction in SRF of 98.57%. This was followed by ferric chloride, which gave a value of 89.2% for percentage reduction in SRF, and lastly came lime with a percentage reduction of 79.81%. Besides, a lower concentration of the conditioner and a lower speed of rotation are required when using chitosan as a conditioner.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".