Comprehensive dynamic modelling and CFD simulation of heat and mass transfer in wetted wall absorption reactors
Bibliographic record
Abstract
Abstract A comprehensive mathematical model has been developed to analyze transport phenomena in wetted wall absorption reactors. In this regard, a wetted wall reactor was modelled considering the entrance regions of momentum, heat and mass transfer, compressibility of the gas phase as a function of gas temperature, pressure, and composition, and also non‐Newtonian behaviour of the liquid phase. In the developed model, a gas mixture of two components, A and B, was considered as the gas phase (A as the diffusing component), while a binary liquid mixture of components C and D was taken as the liquid phase (C as the reactant component). CFD methods were applied to solve nonlinear governing equations simultaneously and the simulation results were validated against experimental data reported in the literature and excellent agreement was found. Moreover, distributions of velocity, temperature, and species concentrations of both phases were obtained and the effects of various operating conditions and physicochemical properties on these distributions were carefully studied. Furthermore, the open‐loop response of the reactor to some typical step and pulse inputs was examined. Moreover, the closed‐loop response of the reactor was also examined; in this regard, a PI controller was designed to control the reactor's output properly.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".