Woven-Fiber Microfiltration (WFMF) and Ultraviolet Light Emitting Diodes (UV LEDs) for Treating Wastewater and Septic Tank Effluent
Bibliographic record
Abstract
Decentralized wastewater treatment systems enable wastewater to be treated at the source for cleaner discharge into the environment, protecting public health while allowing for reuse for agricultural and other purposes. This study, conducted in Thailand, investigated a decentralized wastewater treatment system incorporating a physical and photochemical process. Domestic wastewater from a university campus and conventional septic tank effluent from a small community were filtered through a woven-fiber microfiltration (WFMF) membrane as pretreatment for ultraviolet (UV) disinfection. In domestic wastewater, WFMF reduced TSS (by 79.8%), turbidity (76.5%), COD (38.5%), and NO3 (41.4%), meeting Thailand irrigation standards for every parameter except BOD. In septic tank effluent, it did not meet Thailand irrigation standards, but reduced TSS (by 77.9%), COD (37.6%), and TKN (13.5%). Bacteria (total coliform and Escherichia coli) and viruses (MS2 bacteriophage) passing through the membrane were disinfected by flow-through UV reactors containing either a low-pressure mercury lamp or light-emitting diodes (LEDs) emitting an average peak wavelength of 276 nm. Despite challenging and variable water quality conditions (2% < UVT < 88%), disinfection was predictable across water types and flow rates for both UV sources using combined variable modeling, which enabled us to estimate log inactivation of other microorganisms. Following UV disinfection, wastewater quality met the WHO standards for unrestricted irrigation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".