Treatment of Produced Water from Unconventional Resources by Membrane Distillation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Unconventional resources (Shale gas/oil) use significant volumes of water for hydraulic fracturing (fracking). While some of the water used is fresh groundwater, there are more environmental pressures to use brackish water sources for fracking. This brackish water may need to be treated to lower the saturation levels and to allow mixing of field chemicals. Unconventional resources also produced high volume of flow-back water (produced water). This produced water (PW) contains high levels of total dissolved solids (TDS) and desalination may be needed to allow recycling or reuse of this water source. Membrane Distillation (MD) is an innovative process that can desalinate highly saline waters (30,000–100,000 mg/L TDS) more effectively than reverse osmosis. As a proof of concept, bench-scale MD testing were performed on brackish and produced water samples (30,000 mg/L-60,000 mg/L TDS) obtained from Texas. Results have shown excellent TDS rejection (99.9 %) on all the water samples that were tested without impacting membrane's flux performance. To evaluate the O&M and scale up issues, two one m3/day MD pilot units are currently operating side by side at a local desalination plant in Doha. Brine from the thermal desalination plant was used as representative high salinity water (70,000 mg/L), similar salinity levels could be found in brackish groundwater and/or flow-back water. It was assumed that all other contaminants that could cause membrane fouling (such as suspended oil, solids, organics, microorganisms) will be removed in a pretreatment step prior to MD. Preliminary results showed that the pilot units were successful in completely removing salt. Flux was very stable for more than 2 weeks. However, it was concluded that pretreatment is critical for stable performance of the MD units. This presentation will provide up to date data on MD bench and pilot-scale performance with O&M issues and projected cost estimates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it