Kinetic Modeling and Assessment of a CO2 Nanobubble-Enhanced Hydrate-Based Sustainable Water Recovery from Industrial Effluents
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study evaluates the effectiveness of CO 2 nanobubble-enhanced hydrate-based desalination (HBD) to treat industrial effluents from the mining and metals industry. Testing was conducted in a high-pressure reactor apparatus that employed CO 2 as the gas hydrate former at 274.15 K and 3.58 MPa. CO 2 nanobubbles (NBs) were used to promote hydrate formation, aiming to streamline an HBD process without separation steps for the additives/chemicals used. Due to the limited studies on hydrate formation in sulfate-containing aqueous solutions, this research focused on the kinetics of hydrate formation in varying concentrations of Na 2 SO 4 and MgSO 4 (0.1 and 0.5 M). The results showed that CO 2 NBs significantly enhanced hydrate formation in both Na 2 SO 4 and MgSO 4 solutions, with CO 2 consumption increasing by up to approximately 51% and 35%, respectively. Additionally, a kinetics study on a real effluent from the mining and metals industry showed that the presence of CO 2 NBs increased CO 2 consumption by around 20% after 180 min. This research also evaluated water recovery and desalination efficiency in a 3-stage HBD process applied to the effluent, the concentration of which exceeded the operating range of reverse osmosis. The results indicated an improvement in water recovery from 25.13 ± 2.04% to 40.16 ± 1.43% with CO 2 NBs, underscoring their effectiveness in treating highly saline water. Moreover, desalination efficiencies of 49.54 ± 2.39% and 42.03 ± 3.43% were achieved without and with CO 2 NBs, respectively. This study represents the successful demonstration of the efficient application of the CO 2 NBs-boosted HBD method to treat high-salinity effluents and recover clean water for reuse. Graphical Abstract
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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