Scientific machine learning for modeling industrial-scale Primary Separation Vessel
Bibliographic record
Abstract
A scientific machine learning (SciML) method integrates masked neural networks with fundamental physical laws to model the Primary Separation Vessel (PSV) in oil sands processing. This framework addresses limitations in conventional models by discovering a critical underlying relation, namely optimized hindered settling functions, through embedded neural networks, subsequently translated into interpretable mathematical expressions via symbolic regression. The methodology significantly enhances computational efficiency through parallel processing and pseudo-transient solver techniques while eliminating the need for scenario-specific parameter tuning. The re-discovered hindered-settling function successfully captures the fundamental behavior of hindered settling across varied conditions, although parameter estimation relied on a limited dataset. Validation against industrial range benchmarks demonstrates the approach’s effectiveness in reproducing characteristic physical behaviors across different ore grades. This advancement represents a significant step in bridging theoretical and data-driven approaches for complex multiphase systems, with promising potential for extension to integrated systems and improved operational optimization in oil sands processing facilities.
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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 | 0.000 | 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) | 0.000 | 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".