Toward a Circular Economy in Water Treatment: Upcycling Aluminum Salt-Based Water Treatment Residual into An Effective Adsorbent–Ceramsite
Bibliographic record
Abstract
Coagulation is widely used in water treatment, generating large volumes of water treatment residual (WTR), most of which is aluminum salt-based water treatment residual (Al-WTR). This waste is environmentally and financially costly to manage. Al-WTR, however, can be upcycled into value-added products such as ceramsite, a porous material that can be used for adsorption or other beneficial purposes. Here, we review the fabrication processes for transforming Al-WTR into ceramsite, the strategies for enhancing its performance, and its potential environmental applications. Ceramsite has exhibited potential as an adsorbent in removing pollutants such as phosphorus and heavy metals as well as being a biofilm-supporting medium. Moreover, ceramsite has shown the effective removal of emerging pollutants from water matrices. Therefore, ceramsite represents a promising strategy for valorizing Al-WTR. Further investigations are required to improve the ceramsite performance and assess its applicability in environmental engineering. Furthermore, we also discuss the current challenges and barriers associated with the application of the Al-WTR-derived ceramsite and possible mitigation strategies. This Review aims to stimulate further research and development in sustainable WTR management, thereby contributing to the development of a circular economy in the water treatment sector.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".