Advancements in the treatment of amine-rich wastewater from amine-based post-combustion carbon capture: a review
Bibliographic record
Abstract
Carbon capture and storage (CCS) plants play a pivotal role in reducing greenhouse gas emissions from carbon-intensive industries while enabling the continued use of fossil fuels. Among CCS methods, amine-based post-combustion capture is widely used for its efficiency and cost-effectiveness. However, the process generates substantial amine-rich wastewater containing harmful compounds like amines, ammonia, nitramines, sulfate, and nitrosamines, posing significant environmental and health challenges. This review examines recent developments in treating amine-rich wastewater, with a focus on economically viable and environmentally sustainable solutions. It discusses amine degradation pathways, byproduct toxicity, and the environmental impacts of untreated wastewater. By examining the physical, chemical, and biological technologies, biological processes, such as the pre-denitrification-nitrification process, stand out as effective and eco-friendly solutions for treating amine-rich wastewater. This study also proposes anaerobic ammonium oxidation (ANAMMOX) as a promising approach due to the low carbon-to-nitrogen ratio of CCS wastewater. A combined denitrification-anammox process is recommended to improve nitrogen removal efficiency by producing an ammonium- and bicarbonate-rich effluent that favors anammox bacterial growth. However, its effectiveness has not yet been evaluated, highlighting the need for further research. The conducted literature review also reveals that most existing research has focused on the removal of individual wastewater components rather than treating actual CCS wastewater, highlighting the need for integrated, scalable treatment approaches tailored to real CCS effluents.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".