Using Crushed Glass with Sand as a Single and Dual Filter Media for Removal of Turbidity from Drinking Water
Bibliographic record
Abstract
The aim of this research is trying to find that environmentally and economically efficient way for reuse industrial solid wastes of glass as an alternative filter media to sand to remove turbidity from drinking water.It is required to set a pilot filtration unit which included mainly three transparent columns.It was used to remove the turbidity of synthetic turbid water that consisted of three filter media.The first and second filters represent single media filters of glass and sand, respectively.The third filter represents a filter of dual media of sand at the bottom layer and glass at the top layer.The single media of glass filter and the dual media of glass-sand filter have a maximum removal efficiency of water turbidity in comparison with the single media of sand filter.The maximum removal efficiencies of glass filter, glass-sand filter, and sand filter are 94% and 95%, and 87%, respectively, at influent turbidity of 25 NTU and a filtration rate of 5 m/h.Statistical analysis using stepwise multiple linear regression models had been carried out by utilizing (DataFit, version 9. 1. 32) program models give a good matching between the measured and the predicted values for simulated drinking water for sand, glass, and glass-sand media with the determination coefficient (R² ) equal to 1.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".