Production of High Quality Water for Oil Sands Application
Bibliographic record
Abstract
Abstract Canadian oil sands in the province of Alberta are a hydrocarbon source for North America. By the year 2015, the oil sands will be producing in excess of 3 million barrels/day of crude oil. A number of companies operate Upgraders that convert the bitumen that is extracted from the oil sands into light sweet crude oil. Steam is required to heat utilities at the Upgrader facility. In one major oil sands extraction site, well water is being used as feed water for the boilers producing this steam. Reverse Osmosis (RO) systems were designed and installed to produce high quality water required for this application. The pretreatment system was designed with conventional multimedia technology. The RO system required feed water with silt density index (SDI) of 3 or less. Due to ineffectiveness of the conventional pretreatment system, the SDI of the RO feed water was in the range of 12-20. This resulted in severe fouling of the RO membranes and production losses. In order to optimize the performance of the RO membrane system, a pressurized microfiltration membrane system was delivered and commissioned within 5 days to replace the existing pre-treatment system. The new unit contained an automated PVDF hollow fiber microfiltration membrane system mounted in a trailer. SDI values in the range of 1.0-2.5 were immediately observed in the feed water to the RO system. The end user has enjoyed significant cost savings and ease of operation as a result of this innovative technology. This paper describes the details of the installation and the superior performance data gathered at the end user site.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".