Pioneering GRP Use in Amine-Based CO2 Capture: Insights from Pilot Plant Exposure Studies
Bibliographic record
Abstract
Abstract Carbon capture, sequestration, and storage (CCS) is crucial for mitigating global warming by reducing industrial CO2 emissions. To enhance the commercial viability of CCS, this study focuses on reducing capital (CAPEX) and operational (OPEX) expenditures by evaluating the use of glass reinforced plastic (GRP) in amine-based CO2 capture technologies. Previous studies on chemical compatibility (C2023-18832) and mechanical feasibility (C2024-20710) were presented at the 2023/2024 AMPP conference. This investigation involved exposing GRP materials to solvent blends in a carbon capture pilot plant over seven months. The conditions included both rich and lean amines, with temperatures ranging from 30 to 143°C. Analysis of the exposed samples indicated that GRP is technically suitable for cold amine applications (30 to 63°C). However, for hot amine application, previous lab studies indicated suitability up to 100°C though no exposure data is available from the pilot plant study in that temperature range. These findings align with prior laboratory results, affirming the suitability of GRP for cold amine applications and highlighting the risks associated with high-temperature exposures. This research supports the development of more cost-effective and durable CCS systems by identifying materials that balance performance and economic feasibility.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".