An optimization study for preventing silica gelation and improving filtration effectiveness during <scp>pH</scp> reduction of high concentration silica solutions
Bibliographic record
Abstract
Abstract The steam generation processes at the steam‐assisted gravity drainage facilities result in huge quantities of wastewater streams, which are characterized by high pH and high silica levels. These concentrated streams need to be neutralized before their disposal via down‐hole injection. The neutralization of these high‐pH brines results in the formation of a gel‐like substance, which makes it difficult to filter the amorphous silica gel. The wastewater used in this study was synthetically prepared using sodium metasilicate to mimic high‐concentration silica solutions. Our experiments did not show any advantage of a two‐step pH‐neutralization process over the single‐step process for suppressing silica gelation. A systematic experimental campaign was undertaken to investigate the effects of SiO2 concentration, NaCl:SiO2 ratio, and pH on the residual silica concentration, percent silica removal, filtration rate, and filtration effectiveness. For NaCl:SiO2 ratios higher than 4.5, silica precipitation during pH reduction did not lead to the formation of gel or sol. The response surface methodology (RSM), based on the Doehlert design of experiments, was implemented to optimize the responses and provide high efficacy with fewer experiments. The results from the analysis of variance (ANOVA) analyses of the experimental data were used to evaluate the significance of each term in the quadratic model. 3D response surfaces and 2D contour plots were generated for determining the optimal ranges of independent factors for achieving the maximum silica removal, the highest filtration rate, the best filtration effectiveness, and the minimum residual silica concentration. An optimum operating region was established from the RSM analysis and overlay plot.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".