Corrosion Evaluation for Absorption - Based CO2 Capture Process Using Single and Blended Amines
Bibliographic record
Abstract
One of the major problems associated with the amine-based carbon dioxide (CO2) capture process is corrosion of process components, which results in unexpected downtime, production loss, and even major fatalities. Most of the published corrosion literature is on conventional monoethanolamine (MEA) solvent, and there have been very few corrosion studies conducted on other single amines like methyldiethanolamine (MDEA), diethanolamine (DEA), 2-amino-2-methyl-1-propanol (AMP), and some blended amines. Although there has been extensive research conducted on the kinetics of concentrated piperazine (PZ) as an attractive solvent for the CO2 absorption process, no corrosion studies have been conducted for this solvent. This work investigated the corrosion of construction materials including carbon steel (CS1018) and stainless steels (SS304 and SS316) in the CO2 capture process, using various types of CO2 absorption solvents. The tested solvents included MEA, DEA, MDEA, AMP, PZ, and their blends. A series of laboratory corrosion tests was carried out using electrochemical techniques (DC-cyclic potentiodynamic polarization and ACimpedance measurement) and weight loss technique to establish an engineering corrosion database for the CO2 capture process. Experimental conditions were chosen to be CO2 saturation and 80°C for most experiments. The electrochemical results show that the corrosivity order of CS1018 for the single amine systems was MEA > AMP > DEA > PZ > MDEA. The corrosion rates in MEA and AMP systems were almost double those of the PZ and MDEA systems. The passivation of carbon steel in the DEA system was more compact and less porous than those in the MDEA, PZ, MEA, and AMP systems. The corrosive effects of process contaminants, i.e., thiosulfate, oxalate, sulfite, and chloride, on corrosion rate were observed in all amine systems. The presence of thiosulfate reduced the corrosion rate of carbon steel in the MEA system, whereas the presence of oxalate increased the corrosion rate in all tested single amines. Two corrosivity behaviours were found in the presence of sulfite and chloride. In the presence of sulfite, the corrosion rate of carbon steel was increased in the MEA, DEA, MDEA, and PZ systems, but decreased in the AMP system. In the presence of chloride, the corrosion rate increased only in the MDEA system, but decreased in the MEA, DEA, AMP, and PZ systems. In addition to single amines, five different blended amines were also tested for their corrosiveness. The results show that the corrosivity trend of CS1018 in blended amine systems was MEA-PZ ≥ MEA-AMP ≥ MEA-MDEA > MDEA-PZ > AMP-PZ. The stainless steel materials (SS316 and/or SS304) offered great resistance to corrosion in all amine systems. For example, the corrosion rates were very low, in the range of 0.006 - 0.036 mmpy, which is well below the standard acceptable corrosion rate (0.07 mmpy). Conductivity of the solution was found to correlate well with corrosion rate in both single and blended amine systems. The weight loss results show that after 28 days, the corrosivity order of CS1018 in single amine systems was MEA > DEA > PZ > AMP ≈ MDEA. The corrosion products deposited over carbon steel were found to be iron carbonate (FeCO3) and iron oxide (Fe3O4).
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.
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) | 0.000 | 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 | 0.000 |
| 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".