Electrodialysis Processes an Answer to Industrial Sustainability: Toward the Concept of Eco-Circular Economy?—A Review
Bibliographic record
Abstract
Wastewater and by-product treatments are substantial issues with consequences for our society, both in terms of environmental impacts and economic losses. With an overall global objective of sustainable development, it is essential to offer eco-efficient and circular solutions. Indeed, one of the major solutions to limit the use of new raw materials and the production of wastes is the transition toward a circular economy. Industries must find ways to close their production loops. Electrodialysis (ED) processes such as conventional ED, selective ED, ED with bipolar membranes, and ED with filtration membranes are processes that have demonstrated, in the past decades and recently, their potential and eco-efficiency. This review presents the most recent valorization opportunities among different industrial sectors (water, food, mining, chemistry, etc.) to manage waste or by-product resources through electrodialysis processes and to improve global industrial sustainability by moving toward circular processes. The limitations of existing studies are raised, especially concerning eco-efficiency. Indeed, electrodialysis processes can be optimized to decrease energy consumption and costs, and to increase efficiency; however, eco-efficiency scores should be determined to compare electrodialysis with conventional processes and support their advantages. The review shows the high potential of the different types of electrodialysis processes to treat wastewaters and liquid by-products in order to add value or to generate new raw materials. It also highlights the strong interest in using eco-efficient processes within a circular economy. The ideal scenario for sustainable development would be to make a transition toward an eco-circular economy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".