Handling of Amine-Based Wastewater Produced During Carbon Capture
Bibliographic record
Abstract
The large-scale implementation of amine-based carbon capture processes requires the development of sustainable handling technology of the waste effluents. The generated wastewater contains significant amounts of ammonia and toxic degradation products, nitroamines and nitrosamines. They both pose great threats to the ecological environment and human health. Monoethanolamine (MEA) is one of the most commonly used absorption solvents in the post-combustion carbon capture process. In order to make a better management strategy, the waste components and the pathways of MEA degradation are demonstrated based on different reference papers and case studies. Moreover, the toxicity and environmental impact of the degradation products are evaluated. The goal of this review is to elucidate potential technologies that can either eliminate the hazardous nature of the amine waste or convert it into marketable products. We categorize these technologies as waste disposal, recycle, reuse, and chemical/biological treatment method. Several applications with a focus on biodegradation technique are examined according to their amine removal performance. The results reveal that bioconversion is a promising technique for handling amine-based wastewater at large-scale.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".