Rate‐based modelling of reactive absorption of acid gases in an aqueous methyldiethanolamine (MDEA) solution
Bibliographic record
Abstract
Abstract In this work different tools for accurate prediction of acid gas absorption are used. At first, simulation of reactive absorption is carried out using the RATEFRAC module of Aspen Plus, which is tested against pilot plant data. The limitations and disadvantages of this module are presented. In order to present a more predictive approach a rate‐based model for the gas scrubbing process is developed. In this model the assumption of thermodynamic equilibrium is considered only at the gas–liquid interphase. Chemical equilibrium among the reacting species in the liquid phase is assumed just for the bulk phase. Mass transfer is modelled using mass transfer coefficients calculated from available correlations which are then improved using an enhancement factor to account for the chemical reactions. The validity of the suggested model is established by comparison of model results with published pilot plant data. The prediction results using the proposed model are improved by around 17% AAD for CO2 and around 7.5% AAD for H2S compared to simulation results using Aspen Plus.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".