Liquid‐phase chemical reactors: Development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system
Bibliographic record
Abstract
Abstract The Euler‐Euler method plus an intelligent algorithm was used to predict bubbly flow in a reactor as a function of column height. The combination of computational fluid dynamics (CFD) and the adaptive network‐based fuzzy inference system (ANFIS) method was used for a chemical bubble column reactor to understand the complex behaviour of fluids in a multiphase reactor. Air fraction as one of the main factors in the scale‐up of reactors was selected as an output parameter for the prediction tool (ANFIS method) at different positions in the reactor. To train and test the prediction ability of this method, a 3D position of one CFD element was selected and, based on that position, a training algorithm was started. After an appropriate learning step, the method was used to simulate gas at different locations of the reactor. The different structures of the ANFIS algorithm were designed to obtain a correct predictive tool for fluid behaviour inside the bubble column. The ANFIS approach shows that it can simulate the liquid behaviour and the CFD results and ANFIS output correlate with one another.
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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 | 0.000 |
| 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".