Evaluation of Glass Reinforced Plastic for Post Combustion CO2 Capture Amine Service
Bibliographic record
Abstract
Abstract Carbon capture, sequestration, and storage (CCS) is considered as an immediate possible measure to address global warming by reducing CO2 emission to the environment. Amines are used widely to capture CO2 gas from industrial exhaust gas streams. Introduction of cost-effective construction materials in the amine services can offer significant cost benefit to the users. Therefore, an extensive laboratory study was conducted to evaluate glass reinforced plastic (GRP) as a construction material for some large equipment used in the process. Very limited published record was found studying GRP’s interaction with amine solvents, especially for longer time exposure. Information received from GRP and resin suppliers supported the same. Apart from cost reduction, GRP can help reducing iron contamination of amines, which is well known to catalyze amine degradation in a post combustion environment. Impact of exposure of GRP to an amine formulation at temperatures up to 100°C on its properties were measured. Changes in glass transition temperature (Tg), tensile properties, Barcol hardness, weight and visual appearance were recorded. The Plastic Design Library (PDL) guided chemical resistance of the GRP material was calculated. No significant impact was observed during this study, indicating suitability of the GRP material for application in amine-based processes up to a certain temperature limit.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".