Grey water treatment using a solar powered electro-coagulator and vacuum membrane distillation system
Bibliographic record
Abstract
ABSTRACT Grey water reuse has been identified as a sustainable solution to reduce the pressure on freshwater storages. Membrane distillation techniques provide high quality permeate from this insanitary source. However, grey water contains surfactants present in the form of linear alkylbenzene sulphonate (LAS) that reduces the contact angle between the feed solution and the membrane surface leads to the wetting phenomenon. Electro-coagulation (EC) with aluminium electrodes has been demonstrated as an effective technology that removes LAS significantly. The aim of this paper is to investigate the effect of the current density and circulation rate of EC unit on the permeate water quality. For this purpose, synthetic grey water was treated at different operating conditions. It has been shown that, only after 12 min of EC, the turbidity, total suspended solids, chemical oxygen demand, total organic carbon, total nitrogen, total phosphorous, electrical conductivity and faecal coliforms were reduced by an average 94.4%, 89.9%, 83.8%, 71.0%, 73.1%, 96.1%, 30.2% and 1.32log, respectively. The EC permeate was sent to the solar powered vacuum membrane distillation (VMD) to produce pure water. Photovoltaic panels and a thermal collector supplied electricity and heat, respectively, for the combination of EC and VMD in order to use renewable energy.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".