Optimized CO2 Capture Using Water-Based Absorbent under Circulated Water Conditions for Carbon Capture and Storage (CCS) Applications
Bibliographic record
Abstract
Capturing CO2 emissions using a recirculating water-based system offers a simple and eco-friendly alternative to conventional Carbon Capture and Storage (CCS) methods for reducing emissions and mitigating climate change. To optimize CO2 solubility in water, it is essential to integrate and evaluate the key variables that influence the capture process, enabling the identification of ideal operating conditions. This study aims to analyze the phenomena and examine the effects of variables such as CO2 gas flow rate, water flow rate, water temperature, circulation time, and differential pressure on the effectiveness of the CO2 capture process, as indicated by the mass of CO2 dissolved in water within the system. The experiment was carried out using a transparent absorber column filled with packing material. CO2 gas with 99.9% purity was introduced at flow rates of 2 and 4 dm3/min, while water was recirculated at flow rates of 0.25 and 0.5 dm3/min. The system operated at water temperatures between 24 and 30 °C, with circulation durations of 5, 10, and 15 min, and differential pressures of 30 and 50 mmHg. Samples were collected at the end of each circulation period and CO2 absorption was determined through titration using 0.1 M NaOH and phenolphthalein as the indicator. The results demonstrated that higher water flow rates and increased pressure differentials enhanced the capture performance, as evidenced by greater CO2 absorption. Conversely, elevated water temperatures and higher CO2 gas flow rates led to decreased absorption efficiency. The most favorable conditions were achieved at 24 °C, a water flow rate of 0.5 dm3/min, a circulation time of 15 min, and a differential pressure of 50 mmHg, resulting in 1.672 g of CO2 absorbed. The results of this study indicate that water has the potential to serve as an environmentally friendly alternative technology for CCS applications. However, further research is needed to develop a process that is effective, efficient, and more cost-effective. HIGHLIGHTS Higher water flow rates and pressure differentials significantly improved CO2 capture efficiency. Increased water temperature and CO2 gas flow rate reduced absorption performance. Optimal conditions yielded 1.672 g CO2 absorbed at 24 °C, 0.5 dm3/min water flow, circulation time of 15 min, and differential pressure (Dp) of 50 mmHg. GRAPHICAL ABSTRACT
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".