Parametric Process Design and Economic Analysis of Post-Combustion CO2 Capture and Compression for Coal- and Natural Gas-Fired Power Plants
Bibliographic record
Abstract
For the envisaged large number of commercial-scale carbon capture and storage (CCS) projects that are to be implemented in the near future, a number of issues still need to be resolved, the most prominent being the large capital and operational costs incurred for the CO2 capture and compression process. An economic assessment of the capture and compression system based on optimal design data is important for CCS deployment. In this paper, the parametric process design approach is used to optimally design coal and natural gas monoethanolamine (MEA)-based post-combustion CO2 absorption–desorption capture (PCC) and compression plants that can be integrated into large-scale 550 MW coal-fired and 555 MW natural gas combined cycle (NGCC) power plants, respectively, for capturing CO2 from their flue gases. The study then comparatively assesses the energy performance and economic viabilities of both plants to ascertain their operational feasibilities and relative costs. The parametric processes are presented and discussed. The results indicate that, at 90% CO2 capture efficiency, for the coal PCC plant, with 13.5 mol.% CO2 in the inlet flue gas, at an optimum liquid/gas ratio of 2.87 kg/kg and CO2 lean loading of 0.2082 mol CO2/mol MEA, the CO2 avoidance cost is about $72/tCO2, and, for the NGCC PCC plant, with 4.04 mol.% CO2 in the inlet flue gas, at an optimum liquid/gas ratio of 0.98 kg/kg and CO2 lean loading of 0.2307 mol CO2/mol MEA, the CO2 avoidance cost is about $94/tCO2.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".