Modeling and economic analysis of block absorber (RadFrac) performance with stage variations for post-combustion CO2 capture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents a comprehensive modeling and economic evaluation of a Monoethanolamine (MEA)-based post-combustion carbon capture (PCC) absorber unit. The simulation provides detailed insights into the absorber's internal profiles, including temperature gradients and the percentage of CO 2 captured, with a particular focus on the effects of varying the number of stages and absorber diameters. Validation against experimental data demonstrated close alignment between simulated and observed values, with an average Root Mean Square Deviation (RMSD) of 0.018 and 4.0129, respectively, confirming the reliability of the simulation in capturing the complex dynamics of the absorber. The economic analysis, conducted using the Aspen Process Economic Analyzer (APEA), simplified the complex relationship between absorber segmentation, capital and operational costs, and absorber diameter. The study revealed that increasing the absorber diameter and the number of stages leads to a significant rise in Total Capital Cost (TCC), Total Operating Cost (TOC), Equipment Cost, and Total Installed Cost (TIC), with the highest costs observed at a stage number of 90 and a diameter of 1.0 m. However, the analysis identified an optimal configuration at an absorber diameter of 0.45 m with either 10 or 20 stages. This setup effectively balances cost and CO 2 capture efficiency, offering a more economical solution compared to configurations with larger diameters and higher stage numbers, which substantially increase expenses. The findings highlight the critical trade-offs between operational efficiency and capital expenditure, stressing on the crucial role of absorber diameter in determining the economic feasibility of the absorber block in PCC systems.
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.
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 it