<i>In Situ</i> Upgrading of Athabasca Bitumen Using Multimetallic Ultradispersed Nanocatalysts in an Oil Sands Packed-Bed Column: Part 2. Solid Analysis and Gaseous Product Distribution
Bibliographic record
Abstract
Thermal cracking of Athabasca bitumen was carried out in an oilsand packed-bed column, in the presence and absence of in situ prepared trimetallic nanocatalysts at a pressure of 3.5 MPa, residence time of 36 h, and temperatures of 320 and 340 °C. In this part of the study, the effects of reaction severity (time and temperature) as well as the presence of nanocatalysts in packed media on solid and gaseous products were investigated. Results showed that the presence of trimetallic nanocatalysts enhanced the hydrogenation reactions and, consequently, led to significant reduction of coke formation (51.3%) and CO 2 emission reduction. Further, the analysis of the gaseous products and deposited solids confirmed the previous findings reported in part 1 ( 10.1021/ef401716h ) of this study. The accumulative volume of coke precursor gases, such as ethylene and propylene, increased with the reaction severity. However, reaction severity has no significant effect on the atomic metallic ratios (metal/total metal) of the employed trimetallic nanocatalysts, which clearly demonstrates the stability of injected ultradispersed (UD) nanocatalysts (metal/total metal: Mo, 0.6267; Ni, 0.1808; and W, 0.1924) in the porous media at high pressure and temperature. Nonetheless, aggregation of nanocatalysts inside the porous media was observed and graphically demonstrated by environmental scanning electron microscopy (ESEM) images. Overall, the presence of trimetallic nanocatalysts in porous media not just enhanced bitumen upgrading but also improved the produced liquid quality and reduced the coke content as well as CO 2 emission by 50%.
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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 | 0.001 |
| 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) | 0.000 | 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".