Dynamic Modelling and Controllability Studies of a Commercial-scale MEA Absorption Processes for CO2 Capture from Coal-fired Power Plants
Bibliographic record
Abstract
This paper presents a mechanistic dynamic model of an industrial-scale carbon dioxide (CO2) capture plant using Monoethanolamine (MEA) as an absorbent. In order to remove 87% of CO2 from the flue gas stream generated from a 750 MW supercritical coal-fired power plant and produce a CO2 concentration of 95% in the CO2 product stream, a post-combustion CO2 capture plant with three absorbers and two strippers are needed,. A decentralized control structure composed of 11 proportional- integral (PI) controllers was proposed to maintain the dynamic operability of this commercial-scale CO2 capture plant. The evaluation of the plant's performance in closed-loop were conducted using multiple scenarios, i.e., the loss of CO2 recovery (%CC) control loop during variation of flue gas flow rate, a positive ramp change in the flue gas flow rate under a maximum withdrawal constraint on the reboilers’ heat duty, and the disturbance in the flue gas composition resulting from the variation in coal composition and air flow rate. The controllability analysis performed on the proposed industrial-scale MEA absorption plant using the control system designed in this study shows that the plant is able to recover fast from most of the disturbances considered in the analysis. The insight provided from the present study can then be used to address the integration of the present CO2 capture plant to a coal-based power plant and evaluate the dynamic feasibility of this integration under various scenarios.
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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) | 0.001 | 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".