Kinetic Modeling and Assessment of a CO2 Nanobubble-Enhanced Hydrate-Based Sustainable Water Recovery from Industrial Effluents
Bibliographic record
Abstract
Abstract This study evaluates the effectiveness of CO2 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 CO2 as the gas hydrate former at 274.15 K and 3.58 MPa. CO2 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 Na2SO4 and MgSO4 (0.1 and 0.5 M). The results showed that CO2 NBs significantly enhanced hydrate formation in both Na2SO4 and MgSO4 solutions, with CO2 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 CO2 NBs increased CO2 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 CO2 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 CO2 NBs, respectively. This study represents the successful demonstration of the efficient application of the CO2 NBs-boosted HBD method to treat high-salinity effluents and recover clean water for reuse. Graphical Abstract
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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 | 0.001 |
| 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".