Artificial Neural Networks for Accurate Prediction of Physical Properties of Aqueous Quaternary Systems of Carbon Dioxide (CO<sub>2</sub>)-Loaded 4-(Diethylamino)-2-butanol and Methyldiethanolamine Blended with Monoethanolamine
Bibliographic record
Abstract
Physical and heat transport properties such as density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity of aqueous carbon dioxide (CO 2 )-loaded and unloaded 4-(diethyl amino)-2-buthanol (DEAB) and methyldiethanolamine (MDEA) as single amines and each blended with a primary amine (MEA) were measured at different ranges of temperature (25–60 °C), amine concentrations (0.5–2 M for tertiary amine and 5 M for primary amine), and CO 2 loading (0–0.6 mol/mol amine). Results showed an increasing trend of CO 2 loading on density, viscosity, and refractive index and a decreasing trend on the heat transport properties. Two artificial neural network techniques, back propagation neural network (BPNN) and radial basis neural network (RBFNN) as well as some well-known semi-empirical correlations from literature, were applied to correlate and predict these physical properties for two quaternary systems: MEA+DEAB+water+CO 2 and MEA+MDEA+water+CO 2 . Results from the correlation showed that artificial neural network techniques gave the least deviation for the prediction of all physical properties of both amine systems with less than 1% AAD. The correlation coefficient between the experimental and predicted values in terms of R 2 value was in the range of 0.98–0.99.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".