Improving the accuracy of the Eyring equation by pseudo‐ideal solution model to predict the viscosity of the mono‐ethanol amine‐[Bmim] <scp>PF6</scp> ionic liquid blends in a <scp>CO<sub>2</sub></scp> capturing pilot plant
Bibliographic record
Abstract
Abstract The purpose of the present study was to propose a new simulation model for calculation of the viscosity of the mono‐ethanol amine‐ionic liquid (MEA‐IL) solvent in the CO2‐capturing pilot plant. To do so, a combination of the pseudo‐ideal solution model (PISM) and Eyring's mixture viscosity equation was implemented in MATLAB. Moreover, the various thermodynamic models were used to calculate viscosity, using solvent containing MEA‐IL in the high CO2 feed gas. The present study determined the viscosities of the MEA‐IL blends at different concentrations and various temperatures in the CO2 capturing pilot plant. The results were comparable to those obtained from the Eyring‐PISM model, Eyring‐Wilson method, Cheng and Meisen a previous study equation, and the Aspen Plus and Promax with electrolyte non‐random two‐liquid model that presented the actual viscosity values of the liquid solutions. The calculated solvent viscosity values with the Eyring‐PISM model at different temperature profiles were achieved using an average absolute deviation (AAD) of about 1.164% and 1.422% in absorber and desorber for solvent (MEA = 27 wt% and IL = 34.2 wt%) and 1.578% and 1.868% in absorber and desorber for solvent (MEA = 29 wt% and IL = 37.12 wt%), respectively. The consideration of a suitable model for the determination of viscosity has a significant role in energy consumption in the CO2‐capturing pilot plant. The estimated energy consumption with the Eyring‐PISM model was achieved using an AAD of about 0.652% for solvent (MEA = 27 wt% and IL = 34.2 wt%) and 0.502% for solvent (MEA = 29 wt% and IL = 37.12 wt%), respectively. The results obtained from the Eyring‐PISM simulation model using the experimental data revealed a high degree of accuracy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".