Development of a solar electrocoagulation technology for decentralised water treatment
Bibliographic record
Abstract
The goal of this research was to investigate the feasibility of using electrocoagulation (EC) as a primary surface water treatment process that could be used with solar panels in decentralised locations. In this study, the removal of turbidity, ultraviolet light at 254 nm (UV 254) and dissolved organic carbon (DOC) was studied. Operating parameters such as current density (0·91–10 mA/cm2) and charge loading (0·083–1·250 A h/l) in bipolar and monopolar configuration were studied using a direct current (DC) power supply. Experimental results showed that a current density of 4·55 mA/cm2and a treatment time of 60 min in the bipolar configuration allowed the best removal of turbidity, UV 254 and DOC (97·0, 93·0 and 95·2%, respectively). For the second part of the study, the DC power supply was replaced by two photovoltaic panels without the use of a set of batteries and a charge controller. According to the results, the turbidity removal is directly proportional to the solar radiation intensity. This suggests that it is possible to control the charge loading by adjusting the treatment time as a function of solar intensity. Such outcomes suggest that solar-powered EC would be a suitable technology for areas not connected to centralised water treatment networks.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".