Optimisation for enhancing sludge dewaterability using different conditioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (FeCl 3 ) 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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