PENGOLAHAN LIMBAH CAIR LABORATORIUM DENGAN ADSORPSI SERTA PRETREATMENT NETRALISASI DAN KOAGULASI
Bibliographic record
Abstract
Limbah cair laboratorium Teknik Lingkungan UNIPA Surabaya belum memenuhi baku mutu Peraturan Menteri Lingkungan Hidup Nomor 5 Tahun 2014, sehingga perlu diolah supaya tidak mencemari lingkungan. Penelitian ini bertujuan mengkaji pengaruh dosis koagulan Poly Alum Chloride (PAC) terhadap penurunan Pb, Cr, dan TDS, mengkaji kualitas air limbah setelah dinetralisasi, dikoagulasi dan diadsobsi terutama untuk parameter Pb, Cr, TDS, dan pH. Variabel penelitian ini adalah dosis PAC yaitu 150 mg/L, 225 mg/L dan 300 mg/L. Penelitian dilakukan dalam skala laboratorium dengan sistem kontinyu dengan aliran down flow. Media adsorpsi yang digunakan ijuk, sabut kelapa, karbon aktif ampas tebu dan zeolit yang disusun bertingkat dalam reaktor dari pipa PVC. Proses adsorpsi dilakukan selama 2 jam dan pengambilan sampel setiap 15 menit. Hasil dari penelitian ini menunjukan bahwa PAC 300 mg/L menghasilkan efisiensi penurunan tertinggi, yaitu TDS 13,7% Cr 97%, Pb 93,5%, dan kualitas limbah setelah dinetralisasi, dikoagulasi dan diadsorpsi pada menit ke-15 mempunyai kadar TDS 1.810 ppm, Cr total 0,36 ppm, Pb 0,66 ppm sehingga air limbah sudah memenuhi baku mutu sesuai dengan Peraturan Menteri Lingkungan Hidup No. 5 Tahun 2014 sedangkan pH sebesar 5,42 belum memenuhi baku mutu.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".