Conversion of tailings solvent recovery unit ( <scp>TSRU</scp> ) by‐products into activated carbon‐zeolite composites: Impact of fusion pre‐treatment on porosity and <scp> CO <sub>2</sub> </scp> capture
Bibliographic record
Abstract
Abstract Currently, the oil sands industry is producing millions of tons of tailings by‐products from the tailings solvent recovery unit (TSRU) into the tailings ponds. TSRU tailings (TTs) consist of water, asphaltene, and minerals, including silica and alumina mixtures. The oil sands sector has expanded efforts to discover solutions to remove the tailings ponds due to growing public concern about the environmental effects of these ponds and stiffer government rules on their disposal. Therefore, this report studied the effect of the different methods for the conversion of the TTs into the activated carbon‐zeolite composite. The TTs were treated with activation followed by hydrothermal, either with or without fusion with NaOH at 800°C for 1 h as a pre‐treatment. Zeolite Na‐P, zeolite A, and zeolite X were identified during the different characterizations, depending on the pre‐treatment of the fusion. The result showed that the fusion with NaOH before the hydrothermal reaction was effective as it increased the porosity and adsorption of the composite. The CO 2 capture capacity of the product before fusion was 0.19 mmol/g, and after fusion, it was improved to 0.486 mmol/g.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".