Characterization of transport through polymers for fracking fluid treatment and organic acid concentration in extractive membrane bioreactors
Bibliographic record
Abstract
Abstract BACKGROUND The water‐intensive practice of hydraulic fracturing produces wastewater containing a variable matrix of organic and inorganic compounds, including ions and ionizable organic compounds. Extractive membrane bioreactors (EMBs) operating with tailored polymer membranes can selectively sequester and/or transport these solutes for biological treatment. Four grades of Hytrel™ tubing were compared for their suitability in EMB systems, based on the polymers' thermodynamic affinity for solutes and their ability to transport or speciate ionic wastewater constituents. RESULTS Of the four Hytrel™ grades compared, high water content types (30% and 54% equilibrated water content) facilitated the transport of ionic species through the tubing, with all monovalent species being transported through both high‐water content grades, and lack of transport through the low‐water content tubing (3% and 5%). Undissociated organic acids were transported through all tubing types and dissociated acids were able to permeate only high‐water content grades. Using this differential ability to transport/not transport an organic acid depending on its dissociation state, a low‐water content Hytrel™ grade of tubing was able to concentrate a dilute butyric acid solution by 220% after 48 h. CONCLUSION The use of polymeric tubing for EMB applications for the treatment of hydraulic fracturing wastewater requires knowledge of both solute affinity and water content for complex waste streams, both of which affect the capability to transport organic and ionic species. © 2018 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".