Wear Modeling of Dense Slurry Flow in Oil Sands Coarse Tailings (CT) Pipelines
Bibliographic record
Abstract
Abstract Dense slurry flow is a feature of the oil sands operation. Modelling of erosion in dense slurry flow under oil sands process conditions is challenging. Although several erosion models are currently used for upstream produced sand application under very dilute sand conditions, extension of those models to dense slurry flow such as conditions relevant to oil sands is highly uncertain. The objective of this study is to develop predictive wear model for dense slurry flow to narrow the gap. An integrated approach was developed to model the wear in oil sands Coarse Tailings (CT) slurry pipeline. Three techniques, including pilot-scale flow loop experiments, computational fluid dynamics (CFD) simulations and field trial, were jointly used to aid the development of a reliable predictive tool. By collaborating with vendors, a high-resolution, non-intrusive, erosion monitoring system based on ultrasonic technology (UT) was developed and implemented in the flow loop experiments. A data mining analysis based on random forest algorithm was applied to the field trial data to develop a predictive wear model for CT pipelines. Both the Eulerian-Granular and Eulerian-Lagrangian methods were explored in CFD simulations for dense slurry flow in long and large horizontal pipes. The CFD erosion model was calibrated based on the field trial data and validated by the flow loop tests. The effects of critical variables affecting the wear were investigated, and a predictive tool was developed. The modelling tool is capable of predicting erosion rates due to changes in the piping design and operating conditions. The model can help the operator adjust process conditions to minimize wear and optimize inspection and maintenance schedule. This paper summarizes the findings from the various techniques adopted in this study and their limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".