RO Membrane to Remove Sulfate: an Inland Brackish Water Desalination Pilot Study
Bibliographic record
Abstract
The City of Fargo completed a Facility Plan of their Water Treatment Plant (WTP) in 2011 to address two main issues: increasing water demands and high sulfate concentrations within a raw water source (Sheyenne River) primarily due to Devils Lake flooding. Reverse Osmosis (RO) was recognized as the most appropriate technology for sulfate reduction, and recommended for use in the WTP expansion. An RO pilot study was performed to evaluate its feasibility for two operational scenarios. RO membranes experienced rapid fouling in the Polishing Scenario, which used RO to further treat filtered water from the existing WTP (pretreatment, lime softening, ozone, and granular filtration). RO membranes exhibited superior performance in the Parallel Scenario, which was a separate treatment process (coagulation/flocculation/sedimentation + microfiltration/ultrafiltration +RO) parallel to the existing WTP. RO membrane autopsies indicated that membrane fouling was organic and biological for the Polishing Scenario while organic and scaling for the Parallel Scenario. Optimization studies were performed in the Parallel Scenario to determine optimal coagulation conditions for pretreatment as well as flux, recovery, and membrane cleaning regimes for both the MF/UF and the RO. Uniquely, an RO membrane selection pilot was conducted for both scenarios to evaluate RO membranes from four different manufacturers. The slight difference surface chemistry among various RO membrane can cause substantial different performance. It was found that one RO membrane could not be cleaned adequately, although it has many successful applications elsewhere. This one year pilot study proved that RO technology is feasible to reduce sulfate concentrations to acceptable levels in the City’s finished water.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".