Development of a bench-scale immersed ultrafiltration apparatus for coagulation pretreatment experiments
Bibliographic record
Abstract
The purpose of this paper is to present results of a project that focused on developing a standardized bench-scale apparatus and operating procedures for immersed ultrafiltration (UF) membrane systems to assess integrated process designs (e.g., coagulation-UF) under controlled laboratory conditions. The integrated test apparatus, termed Immersed Ultrafiltration Enhanced Coagulation (IUEC), was designed using a hollow-fiber, outside-in UF module immersed in a single compartment water preparation and filtration tank equipped with aeration mixing capabilities for coagulation and flocculation process evaluations. Bench-scale experiments were conducted with alum on a low turbidity surface water source to evaluate system performance of the integrated IUEC apparatus compared to a standard jar test unit. The experiments were evaluated by measuring the removal of natural organic matter and zeta-potential analysis from water collected from a conventional mechanically-mixed process with a manual transfer to a UF membrane system and comparing these results to the IUEC system. The results of this study demonstrated that using the single-compartment IUEC apparatus can provide water quality data that is congruent with those obtained through conventional methods that rely on use of standard jar tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".