Estimation of bitumen froth quality using Bayesian information synthesis: An application to froth transportation process
Bibliographic record
Abstract
Abstract This study presents the design of soft sensors for estimation of bitumen froth quality in an oil sands natural froth lubricated (NFL) transportation process. One of the most important quality indexes for bitumen froth is the water content. Due to the variation in oil sands composition and the nature of multi‐phase process conditions, existing hardware sensors are not reliable enough to provide on‐line accurate water content measurement. Laboratory analysis result is obtained off‐line with large sampling interval and irregular time delay. Therefore, it is not sufficient for real‐time monitoring and control. To overcome these limitations, Bayesian information synthesis approach is proposed to fuse all the existing information to produce more reliable and more accurate real‐time froth quality information. This technique has been applied in Syncrude Canada Extraction operations; both monitoring and control performance illustrate the promising perspectives of the proposed approach. © 2012 Canadian Society for Chemical Engineering
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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 | 0.001 |
| 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".