Artificial Neural Network Prediction of Tribological Properties of Nano Lubricants with Eco-Friendly Refrigerant
Bibliographic record
Abstract
With the emergence of the Montreal and Kyoto Protocols, there's been a prohibition on refrigerants in vapor compression systems that harm the ozone layer and contribute to global warming. While alternative refrigerants have been suggested, they tend to be more energy-intensive than the conventional ones they replace. A promising approach to mitigate this increased energy consumption lies in the use of innovative lubricants that can enhance system efficiency. Nano-lubricants have recently gained attention for their superior performance compared to traditional lubricants in refrigeration systems. However, formulating nano-lubricants and determining their composition with the desired thermophysical and tribological properties pose significant challenges. The development of nano-lubricants requires extensive experimentation, and predicting the properties of these lubricants, especially those that offer optimal performance, demands even more rigorous experimentation. Recently, Artificial Neural Networks (ANNs) have been employed to forecast system performance based on existing experimental data. In this study, we leverage literature data on nano-lubricants of varying compositions to design an ANN architecture. The model is then trained, assessed, and validated for predicting the properties of nano-lubricants with unknown compositions. The ANN's predictions are subsequently compared with actual results to gauge its accuracy and reliability.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".