Technoeconomic and Dynamical Analysis of a CO<sub>2</sub> Capture Pilot-Scale Plant Using Ionic Liquids
Bibliographic record
Abstract
Carbon capture has been recognized as an attractive alternative to reduce CO 2 emissions. The most feasible technology that can be developed at a commercial-scale in a short-term period is CO 2 capture by absorption since it is an end-pipe technology that can be installed in existing coal-based power plants and will not require retrofit of the power plant. The most studied CO 2 capture process is absorption using monoethanolamine (MEA) and represents the benchmark solvent because of the favorable properties it has shown such as fast kinetics, high absorption capacity, good solubility in water, and low price. On the other hand, this solvent is susceptible to thermal and chemical degradation, and it is also corrosive. Nevertheless, the main drawback of this solvent is the energy consumption needed for solvent recovery (almost >90% of the plant’s operating cost). Ionic liquids (IL) are new alternative solvents for CO 2 capture. Experimental results have shown that IL feature chemical and thermal stability, and good CO 2 absorption capacity. In this work, a theoretical IL is used as physical solvent for developing a new flow-sheet of a CO 2 capture plant. A techno-economic analysis was carried out to evaluate the feasibility of the proposed design. The results show that the IL-based plant features lower energy demand compared to a traditional MEA-based plant. Moreover, the dynamic analysis performed in this study provides insight on the degree of nonlinearity and the dynamics of the process, which are essential tools to design suitable control schemes. The results show that the plant can accommodate perturbations in the flue gas flow rate up to ±10% while meeting CO 2 recovery and purity targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".