Graphene Based Membranes for High Salinity, Produced Water Treatment by Pervaporation Separation
Bibliographic record
Abstract
Petroleum industries generate huge volumes of wastewater that is associated with oil and gas during extraction, known as produced water. It accounts for 98% of the amount extracted, and comprises diverse pollutants of salts, suspended solids, dissolved organic solutes, and dispersed oils; that require to be safely treated before being disposed to the environment, or reused for various beneficial applications. Nowadays, graphene-based membranes have shown potential as a membrane material due to their high performance and stability features. This research demonstrated the use of graphene oxide membranes supported on polyethersulfone films (GO/PES) for high salinity water, simulated produced water model (PWM), and PWM with simulated foulants treatment via the pervaporation separation technology. The membranes showed the highest water flux of 47.8 L m-2 h-1 for NaCl solutions in pervaporation testing operated at 60 oC, and salt and organic rejections of 99.9% and 56%, respectively. In addition, the membranes were tested for long-term pervaporation for 72 hours and showed a decline of 50–60% from the initial flux in the worst-case-scenario. Moreover, in-depth investigation of the Zn2+ crosslinker showed a hydrolysis reaction to Zn(OH)2, with the progress of the long-term pervaporation, in which much of it is being leached out. Consequently, since GO membranes are not stable in water, it remains challenging to be utilized in the industry. A more stable GO membrane in aqueous phase was proposed. The membrane’s stability was enhanced by divalent and trivalent metal cations of Zn2+ and Fe3+ crosslinkers, respectively, and partial reduction under vacuum. Two orders of fabrications were investigated of either crosslinking rGO (method I) or reducing \nM+–rGO (method II). The prepared membranes were examined for their characterization and performance. Fe3+–rGO prepared by method II showed the best organic solute rejection of 69%. Moreover, long-term pervaporation experiment was performed for 12 hours for Zn2+–rGO membranes, and revealed a drop in flux of 6% only, while Zn2+–GO membrane had a drop in flux of 24%. Additionally, the stability of the membranes was tested via an abrasion method using a rotary wheel abrader. The conducted experiments revealed that Fe3+–rGO membranes had the maximum mechanical integrity with an abrasion resistance of 95% compared to the initial control (non-reduced and non-crosslinked) GO/PES membrane.
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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 | 0.000 | 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 | 0.001 |
| 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".