Design of industrial wastewater demulsifier by HLD-NAC model
Bibliographic record
Abstract
Abstract The chemical method is one of the treatment techniques for the separation of oil–water emulsion systems. The selection of appropriate demulsifiers for each emulsion system is the most challenging issue. Hydrophilic-lipophilic-deviation (HLD) is a powerful semi-empirical model, providing predictive tools to formulate the emulsion and microemulsion systems. This work aims to apply HLD to obtain an optimal condition for demulsification of oil-in-water emulsion system—real industrial wastewater—with different water in oil ratios (WOR). Therefore, the oil parameter of the contaminant oil and surfactant parameter for three types of commercial surfactants were calculated by performing salinity scans. Furthermore, the net-average-curvature (NAC) framework coupled with HLD was used to predict the phase behavior of the synthetic microemulsion systems, incorporating solubilization properties, the shape of droplets, and quality of optimum formulation. The geometrical sizes of non-spherical droplets ( L d , R d )—as an indicator of how droplet sizes are changing with HLD—were consistent with the separation results. Correlating L d /R d at phase transition points with bottle test results validates the hypothesis that NAC-predicted geometries and demulsification behavior are interconnected. Finally, the effect of sec-butanol was examined on both synthetic and real systems, providing reliable insights in terms of the effect of alcohol for WOR ≠ 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".