Pilot capacity iron electrocoagulation scale-up for natural organic matter removal for drinking water treatment
Bibliographic record
Abstract
Canadian remote communities are most often those who are affected by poor water quality and boil water advisories. A major issue is the applicability of traditional water treatment technologies to unconventional applications (small-scale and inaccessible communities). Their inaccessibility presents difficulties for supplying needed chemicals involved in traditional treatment processes such as coagulations and flocculation. Electrocoagulation (EC), an electrochemical process producing coagulant chemicals on-site and on-demand, may be an alternative technology to traditional coagulation suitable for small and remote communities. The following work investigated a continuous iron EC process for natural organic matter (NOM) removal. EC experiments were undertaken in the laboratory at 1.35 and 5 LPM, using synthetic surface water, monitoring the effect of flocculation, metal loading (ML), current density and inter-electrode gap. At both flow rates, flocculation was found to have no effect on the reduction of DOC or UV-abs-254. ML was found to have the greatest effect on both DOC and UV-abs-254 reductions, where the highest ML tested yielded reductions >90% and >60%, respectively. Increases in UV-abs-254 at low ML were found to be due to dissolved residual iron. It was determined that humic acid and chloride functioned as ligands and increased the solubility of iron. Operations were scaled-up to 10 LPM and integrated into a water treatment plant in the community of Van Anda, using raw surface water. Average DOC and UV-abs-254 reductions at the greatest ML were 37.2±4.2% and 54.7±0.9%, respectively. EC was found to have low energy requirements at a pilot-scale, whereby 0.480-0.621 kWh per cubic meter of water treated was required to operate at the conditions that yielded the greatest NOM reductions. Finally, an investigation to determine the current density distribution was undertaken. Current distribution results yielded increased current uniformity with the increase of the inter-electrode gap. This increased uniformity can be attributed to the water velocity profiles in the reactor. Through computational fluid dynamic (CFD) models, it was demonstrated that fluid flow uniformity also increased with an increasing inter-electrode gap. Regions of the electrode that were observed to be occupied by high fluid velocity were also areas yielding greater current density.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".