Simultaneous Modeling of Water Purification Process by Direct Osmosis Membrane and Recovery of Osmotic Solution by Ultrafiltration Membrane
Bibliographic record
Abstract
In this study, first, the water purification process was modeled by the hybrid of direct osmosis membrane and ultrafiltration, then the current system was compared with experimental results in terms of quality control and costs. In the direct osmosis process, a highly concentrated sodium polyacrylate solution was used as the draw solution. When the FO side of the hybrid membrane met wastewater, seawater, or saltwater, clean water was drawn through the FO membrane into the SPA solution. Then, the clean water was removed from the SPA solution through the UF membrane by applying pressure, which can be hydraulic or mechanical, less than 1 bar. Modeling was done to prove the validity of the design concept. Some model equations were extracted to simulate the performance of the hybrid membrane, and the experimental data were analyzed based on the model equations. It is believed that this method allows the production of RO quality water at a UF pressure much lower than the RO pressure and thus leads to a significant reduction in energy consumption for water production. It was noticed that more water (than the calculated value) could be drawn to the SPA solution when the CSPA,0 was<15.75 wt% while less water was drawn when CSPA,0 was>15.75 wt%.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".