Correlation of the Transport Properties for the Ethanol-Water System Using Neural Networks
Bibliographic record
Abstract
Process design and simulation rely heavily on the accuracy and availability of transport property correlations. General models that combine the properties of pure components often lack the necessary accuracy. In this investigation, neural networks were used to model some important transport properties for the ethanol-water binary system. Specifically, a three-layer feed-forward neural network with six neurons in the hidden layer was used to model viscosity, thermal conductivity, surface tension and the Fick diffusion coefficient based on an array of experimental data. These neural network models were then compared to some conventional models that are commonly used to predict the aforementioned transport properties. The results showed that the neural network models were able to represent the experimental data very well for the system studied. One advantage in using neural network models to represent these properties is their ability to predict complex and interrelated behaviors without a priori information about the underlying model structure. Further, since all the models retain the same simple matrix structure, their integration into computer codes becomes straightforward and non-repetitive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".