Penurunan Zat Organik dan Kekeruhan Menggunakan Teknologi Membran Ultrafiltrasi dengan Sistem Aliran Dead-End (Studi Kasus : Waduk Saguling, Padalarang)
Bibliographic record
Abstract
Abstrak.Telah dilakukan percobaan skala laboratorium untuk mengkaji kemungkinan aplikasi teknologi membran ultrafiltrasi untuk mengolah air Waduk Saguling.Parameter utama dalam penelitian ini adalah fluks dan kandungan zat organik serta kekeruhan.Membran yang digunakan adalah membran selulosa asetat (CA) yang dibuat sendiri dengan komposisi selulosa asetat 11% (CA-11), 13%(CA-13), 15%(CA-15).Dari hasil analisis karakteristik air baku Waduk Saguling terlihat bahwa Warna, Kekeruhan, dan Zat Organik merupakan parameter yang harus diolah agar memenuhi standar baku mutu yang diperbolehkan.Dilakukan juga pengolahan pendahuluan dengan menggunakan Poly Alummunium Chloride (PAC) sebagai koagulan.Komposisi membran yang memiliki rejeksi zat organik dan Total Suspended Solid (TSS) paling besar dan memenuhi standar baku mutu air minum akan dipergunakan dalam pengolahan air minum yaitu CA-15.Dari hasil penelitian, permeabilitas air Waduk Saguling dari membran CA-15 mencapai 5-12 L/m 2 .jam.bar.Rejeksi zat organik pada air baku waduk saguling mencapai 90 % , TSS mencapai 90-100 %, TDS 80 % dan warna rejeksi 88 %.Membran Ultrafiltrasi dapat digunakan karena mempunyai prospek yang baik, sedangkan pengaruh pretreatment adalah untuk meningkatkan kualitas
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".