Development of a vision‐based online soft sensor for oil sands flotation using support vector regression and its application in the dynamic monitoring of bitumen extraction
Bibliographic record
Abstract
Abstract Extraction from oil sands is a crucial step in the industrial recovery of bitumen. It is challenging to obtain online measurements of process outputs such as bitumen grade and recovery. Online measurements are a prerequisite for innovating better process control solutions for process efficiency and cost reduction. We have developed a soft sensor to provide online measurements of bitumen grade and recovery in a flotation‐based oil sand extraction process. Continuous froth images were captured using a VisioFroth camera system on a batch flotation unit. A support vector regression (SVR) model with a Gaussian kernel was constructed to develop a soft sensor for bitumen grade and recovery using froth image features as the inputs. The model was trained and validated for batch flotation of different grades of oil sands ore at industry‐relevant process conditions. A Dean‐Stark analyzer was used to obtain offline grade and recovery measurements that were used to calibrate the soft sensor. Mean squared errors (MSE) of 62 and 74 were achieved for grade (%) and recovery (%), respectively, and this was obtained using 5‐fold cross validation. The developed soft sensor model has been applied successfully in the real‐time dynamic monitoring of flotation grade and recovery for different grades of ore and operating conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".