The analysis of solubility, absorption kinetics of CO<sub>2</sub> absorption into aqueous 1‐diethylamino‐2‐propanol solution
Bibliographic record
Abstract
In this present work, the CO 2 absorption performance of aqueous 1‐diethylamino‐2‐propanol (1DEA2P) solution was studied with respect to CO 2 equilibrium solubility, absorption kinetics, and absorption heat. The equilibrium solubility of CO 2 in 2M 1DEA2P solution was measured over the temperature range from 298 to 333 K and CO 2 partial pressure range from 8 to101 kPa. The absorption kinetics data were developed and analyzed using the base‐catalyzed hydration mechanism and artificial neural network models (radial basis function neural network [RBFNN] and back‐propagation neural network [BPNN] models) with an acceptable absolute average deviation of 10% for base‐catalyzed hydration mechanism, 2.6% for RBFNN model and 1.77% for BPNN model, respectively. The CO 2 absorption heat of 1DEA2P was estimated to be −43.6 kJ/mol. In addition, the ions (1DEA2P, 1DEA2PH + , , CO 3 2− ) speciation plots of the 1DEA2P‐CO 2 ‐H 2 O system were developed to further understand the reaction process of 1DEA2P with CO 2 . Based on a comparison with conventional amines (e.g., MEA, DEA, MDEA) and alternative amines (i.e., 1DMA2P and 4‐(diethylamino)−2‐butanol [DEAB]), 1DEA2P exhibited good performance with respect to CO 2 equilibrium solubility, reaction kinetics, and CO 2 absorption heat. Meanwhile, the overall evaluation of 1DEA2P for application in CCS in terms of absorption and desorption is presented, giving helpful information for the screening of these novel amines. © 2017 American Institute of Chemical Engineers AIChE J , 63: 2694–2704, 2017
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".