Optimizing the Performance of Type U Ultrafiltration Membrane with Variations in Flow Rate and Filtration Time
Bibliographic record
Abstract
Ultrafiltration is one of the membrane technologies widely applied to remove nanoparticles (NPs) and colloids, producing drinking water from raw water such as river water, rainwater, etc.In this research, a Type U ultrafiltration membrane was applied.The research parameters, including filtration time and flow rate are varied at 80-720 mins and 1-2 L/s, respectively and optimized to determine the optimum condition of membrane performance.The raw water originates from the Sei Harapan Batam reservoir.The operating parameters, including turbidity and pH, are monitored.After being processed using a U-type ultrafiltration system, the pH and turbidity parameters decreased from 6-8.5 to 6-7 and 6-12 NTU to 0.1-0.5 NTU, respectively at a filtration time of 720 minutes and a flow rate of 2 L/s.Under these conditions, the efficiency of turbidity reduction is relatively high, ranging from 95-99% with an average of 96%.It can be concluded that the turbidity of the effluent water from the membrane system reached the Indonesian drinking water standard of below 3.0 NTU.The U-type ultrafiltration is a low cost and environmentally friendly drinking water production process due to the absence of any chemical.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".