Simulation of a calcium looping CO<sub>2</sub> capture process for pressurized fluidized bed combustion
Bibliographic record
Abstract
The Canadian regulations on carbon dioxide emissions from power plants aim to lower the emissions from coal‐fired units down to those of natural gas combined cycle (NGCC) units. Since coal is significantly more carbon intensive than natural gas, coal‐fired plants must operate at higher net efficiencies and implement carbon capture to meet the new regulations. Calcium looping (CaL) is a promising post‐combustion carbon capture (PCC) technology that, unlike other capture processes, generates additional power. By capturing carbon dioxide at elevated temperatures, the energy penalty that carbon capture technologies inherently impose on power plant efficiencies is significantly reduced. In this work, the CO2 capture performance of a calcium‐based sorbent is determined via thermogravimetric analysis under relatively high carbonation and low calcination temperatures. The results are used in an aspenONE™ simulation of a CaL process applied to a pressurized fluidized bed combustion (PFBC) system at thermodynamic equilibrium. The combustion of both natural gas and coal are considered for sorbent calcination in the CaL process. A sensitivity analysis on several process parameters, including sorbent feed rate and carbonator operating pressure, is undertaken. The energy penalty associated with the capture process ranges from 6.8–11.8 percentage points depending on fuel selection and operating conditions. The use of natural gas results in lower energy penalties and solids circulation rates, while operating the carbonator at 202 kPa(a) results in the lowest penalties and drops the solids circulations rates to below 1000 kg/s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".