Development of new correlation for the prediction of power number for closed clearance impellers using machine learning methods trained on literature data
Bibliographic record
Abstract
Abstract The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state‐of‐the‐art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number . In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R 2 value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state‐of‐the‐art correlations available in literature.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".