DEVELOPING INTEGRATED MEMBRANE PROCESSES FOR INDUSTRIAL WASTEWATERS
Bibliographic record
Abstract
Wastewaters that are produced by industrial processes are more challenging to treat than municipal wastewaters, primarily due to two reasons. Firstly, industrial wastewaters contain high concentrations of several different contaminants (e.g. metals, nutrients and organics etc.), which can be challenging for a single process to treat. Secondly, the compositional properties of the wastewaters can vary significantly as it is dependent on several upstream processes. Commercial membrane technologies have shown significant adoption in desalination and municipal wastewater treatment applications. Their favourable selectivity and tunable properties have garnered interest from both academia and industry to push these technologies into industrial wastewater treatment. Despite showing promising contaminant removal results, current studies have shown that fouling due to high contaminant loadings, and variable treatment efficacies due to feed property variations, limit the adoption of commercial membranes into these applications. Current research addresses these challenges through the new material development or surface modifications, however, there is a need to approach these challenges at a process level by integrating existing membrane technology into adaptive processes. This thesis aims to advance the adoption of commercial membrane technology into ‘tough-to-treat’ industrial wastewater applications. Firstly, the effects of high contaminant concentrations and variable feed properties on membrane treatment is studied by using advanced techniques, such as gas chromatography – mass spectrometry, to resolve the composition of feed and permeate streams from membrane processes treating real wastewaters. It was determined that fast and efficient screening tools are required to optimize and adapt membrane processes to respond to this variability. This thesis then introduces high-throughput and miniaturized screening platform that combines analytical centrifugation with filter plate technology to rapidly optimize two-stage coagulation-filtration processes with an extremely low material and time requirement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".