Study of the Aggregation Behavior of Silica and Dissolved Organic Matter in Oil Sands Produced Water Using Taguchi Experimental Design
Bibliographic record
Abstract
Plant equipment fouling is one of the major problems affecting the performance of steam assisted gravity drainage (SAGD) bitumen extraction processes. The produced water that is treated and reused as boiler feedwater contains high concentration of silica and dissolved organic matter (DOM), and silica and carbon have been found to be principle components of steam generator and heat exchanger foulants. The interactions between silica and SAGD DOM were studied in this research to provide insight into possible fouling mechanisms and mitigation methods. The effects of physicochemical process parameters such as different types of organics, salts, and colloids in the silica–DOM co-precipitation are studied at different concentrations and pHs. In order to study the effects of all physicochemical process parameters at three different levels with a minimum number of experiments, Taguchi experimental design is employed. Analysis of variance is performed to evaluate the contribution of each parameter in the silica–DOM aggregation process. The study revealed that the rate of silica organic co-precipitation varies mainly with the nature of the organics. Further, at higher ionic strength and lower pH of the solution, an enhanced silica–organic aggregation is observed. Multivalent cationic salt is found to be a good coagulating agent to remove humic-like DOM fraction from SAGD produced water.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".