Comparison of a Pseudo-homogeneous Nonequilibrium Dynamic Model and a Three-phase Nonequilibrium Dynamic Model for Catalytic Distillation
Bibliographic record
Abstract
A comparison of a pseudo-homogeneous nonequilibrium (NEQ) dynamic model and a three-phase NEQ dynamic model was studied for the simulation of both a batch catalytic distillation (CD) process and a continuous CD process for the aldol condensation of acetone. The models were implemented in gPROMS and C++. The simulation results show that most of the dynamic responses of both the batch and continuous CD columns are either close to zero order or first order; however, the responses of temperatures in and below the reaction zone of the batch CD column predicted by the pseudo-homogeneous NEQ dynamic model are highly nonlinear. The formation rate of diacetone alcohol (DAA) and the liquid phase temperatures predicted by the pseudo-homogeneous NEQ dynamic model were found to be much higher than those predicted by the three-phase NEQ dynamic model for both the CD columns. Through a comparison with the experimental data, it was found that the three-phase NEQ dynamic model can adequately describe this CD process, while the simpler pseudo-homogeneous NEQ dynamic model overly predicts the formation rate of DAA and the liquid phase temperatures because the mass- and heat-transfer resistances between the liquid and solid phases are ignored. Since the computation time for the two NEQ dynamic models is very similar, it is recommended that the three-phase NEQ dynamic model should be used in the simulation of the CD process unless it is known a priori that the CD process is kinetically controlled.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".