Comments On “Neural Network Modeling of Structured Packing Height Equivalent to a Theoretical Plate” and“HETP and Pressure Drop Prediction for Structured Packing Distillation Columns Using a Neural Network”
Bibliographic record
Abstract
Sir: The two research notes 1,2 recently published in Ind. Eng.Chem.Res. by Eldridge and co-workers describe a currently fashionable approach for correlating a number of macroscopic hydraulic and transport parameters in multiphase reactors.Hence, on the basis of a wide hydrodynamic data set, these authors propose a set of general perceptron-like artificial neural network (ANN) correlations for the prediction of the height equivalent to a theoretical plate (HETP) and the twophase pressure gradient (∆P/H) in counter-current gasliquid structured packing towers.The proposed tools are shown to outperform, in terms of prediction capability, the well-trodden empirical correlations or phenomenological models existing in the field.Although these authors are successful in demonstrating their concept, the impact of their contribution, can, to our opinion, be further reinforced.The procurement in a publication of the full expression and parameters of a correlation is the sole guarantee that such a tool can be useful to readers from industry and academia.In this work, the authors neglect to provide for both the HETP and ∆P/H in their derived correlation equations together with the numerical values of the weights.This unfortunately makes it impossible for users to tangibly take advantage of these two papers.Basically, the most valuable aspect of this work is more the correlation equations rather than the methodology implemented to extract the correlations.For the benefit of the readers of Ind. Eng.Chem.Res., it is highly suggested that the authors provide the complete set of equations allowing for the computation by their tools of the hydrodynamic parameters in the chosen configuration.Neural network computing is becoming increasingly fashionable among the chemical engineering circle.It is a powerful "black-box" approach used to map complex
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".