Can osmotic membrane bioreactor be a realistic solution for water reuse?
Bibliographic record
Abstract
Abstract A growing emphasis on water recycling resulted in intense research activity, aiming to develop and validate reliable and high-quality water treatment processes at lowest cost. In parallel, significant advances in the field of osmotically driven processes have been obtained in the past decade. While the combination of membrane bioreactor (MBR) and reverse osmosis (RO) has become the preferred choice for water reuse, the osmotic membrane bioreactor (OMBR) has begun to be considered as a promising alternative. Based on the current state of knowledge, this paper critically asses the potential for OMBR to be implemented for water reuse application and highlights challenges to reach full scale operation. The initial vision of an energy-free osmotic gradient process is not realistic and its low fouling behaviour is still to be properly assessed. However, OMBR demonstrated unique features such as high rejection of contaminants and an absence of RO brine stream that can support its implementation, especially in the context of high end (potable, industrial) water reuse. However, to become a viable and effective technology for water reuse, significant research and development is still required. Tackling the salinity build-up, developing membranes and modules adapted to OMBR, evaluating long term performance and economics, validating removal of contaminants and developing design, maintenance and automatic control systems constitute critical topics to be considered in future research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".