PENGOLAHAN LINDI DENGAN OZON DAN PROSES OKSIDASI LANJUT BERBASIS OZON
Bibliographic record
Abstract
Limbah industri tekstil di area pinggir kota Surabaya mempunyai karakteristik perbandingan COD dan BOD = 5.57. Limbah jenis ini sulit untuk dibiodegradasi. Studi ini mempelajari tekonologi elektrokoagulasi untuk mengolah limbah tekstil dengan menurunkan intensitas warna, Total Suspended Solid (TSS) dan Chemical Oxygen Demand (COD). Percobaan batch pada suhu kamar dilakukan untuk mempelajari pengaruh pH, jarak elektroda terhadap penurunan warna,TSS dan COD dan membandingkan biaya operasinya jika menggunakan pengolahan kimia.Effisiensi penurunan tertinggi untuk warna (91.96%), TSS (49.17%), dan COD (29.67%) terjadi pada pH awal 4.0 dan jarak elektroda 2 cm dengan elektroda Al/Al. Waktu optimum penurunan intensitas warna dalah 10 menit. Laju penurunan COD adalah : -dC/dt = 0.0053 C +0.056 , dengan C adalah konsentrasi COD. Jumlah sludge yang dihasilkan daripengolahan elektrokoagulasi 3.4 % lebih kecil dibandingkan menggunakan bahan kimia. Biaya yang digunakan untuk pengolahan dengan elektrokoagulasi 52.35 % lebih murah dibandingkan jika menggunakan koagulasi dengan bahan kimia ( tawas). Kata kunci : elektrokoagulasi, penurunan warna, penurunan TSS, laju degradasi COD, imbah tekstil Abstract Waste water from textile industry which is located in one suburb of Surabaya city as characteristic which the ratio of COD to BOD was 5.57. This type of waste water is difficult to be biodegraded. This study investigated elektrokoagulasi technology to treat textile waste water by removing color, total suspended solid, and Chemical Oxygen Demand. Batch experiment at room temperature was carried out to study the effect of pH, electrode distance for color, TSS and COD removal. This study also tried to compare the operation cost between elektrokoagulasi and chemical processes. The best removal efficiencies by Al electrodes was 91.96 % for color, 49.17 % for TSS and 29.67 % for COD which were under initial pH 4.0 and electrodes distance 2 cm. The optimum operation time for color removal was found 10 minutes.The COD degradation rate was - dC/dt = 0.0053 C +0.056, with C= COD concentration. Sludge result from elektrokoagulasi was 3.4 % less than that by chemical treatment.The operation cost for elektrokoagulasi is 52.35 % less than that for chemical coagulation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".