Artificial Neural Network Modelling of Heat Transfer to Canned Particulate Fluids under Axial Rotation Processing
Bibliographic record
Abstract
Artificial neural network models were developed for the overall heat transfer coefficient (U) and the fluid to particle heat transfer coefficient hfp in canned Newtonian fluids with and without particles, and the model performances were compared with the dimensionless correlations for both free and fixed axial modes of agitation. Part of the experimental data were used for training and testing, and a portion was used for cross validation. The average errors (RMS), associated with predicted hfp and U values in fixed and free axial mode were a function of the ANN variables: number of hidden layers, number of neurons in each hidden layer, learning rule, transfer function and number of learning runs. RMS values not significantly different with number of hidden layers between one and three, and the associated RMS was minimal with a high R2 value with one hidden layer and 8 neurons. The combination of the Delta-rule and TanH transfer function also gave the lowest RMS and the highest R2. The highest R2 was achieved for the data set with 85% used for training and testing and 15 % for the cross validation in both modes of rotation, and therefore this combination was used for the development of neural network models. Mean relative errors (MRE) for ANN models were much lower compared with MRE associated with dimensionless correlations; 75-78% lower for hfp and 66% lower for U in fixed and free axial mode with particulate in liquid. Without particulates, in comparison with dimensionless correlations, the MRE for ANN models were 37% lower in end-over-end mode and 76% lower for free axial mode. Overall, ANN models yielded much higher R2 values than dimensionless correlations. The ANN coefficient matrix is included so that the models can be implemented in a spreadsheet.
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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 | 0.001 | 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) | 0.001 | 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".