Treatment of Produced Water from Unconventional Resources by Membrane Distillation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".