Effect of Metal Ions on Light Gas Oil Upgrading over Nano Dispersed MoS<sub>x</sub>Catalysts Using<i>in Situ</i>H<sub>2</sub>
Bibliographic record
Abstract
Hot water extraction and steam injection techniques are used to recover the bitumen from the oil sands or from deep underground deposits generating bitumen emulsions. A novel one-step bitumen upgrading process was developed in our laboratory where the water in the bitumen emulsion was activated through the water gas shift reaction (WGSR) to provide in situ H 2 for hydrodesulfurization (HDS) and upgrading of the bitumen. The catalyst precursor, phosphomolybdic acid, was transformed into a nano dispersed Mo sulfide catalyst in situ during the upgrading reaction. This nano dispersed Mo catalyst was found to be effective for upgrading light gas oil (LGO) derived from Alberta oil sands with in situ H 2 . The effect of Ni, Co, Fe, V and K on the nano dispersed Mo sulfide catalyst for the upgrading of LGO was investigated. Ni, Co promoted both WGSR and HDS. V and K inhibited HDS although they promoted WGSR. Fe showed no significant effect on either WGSR or HDS. Ni was found to be the best promoter for HDS. Atthough K was the best promoter for WGSR, however, K apparently inhibited HDS completely but did not affect the boiling point distribution of the oil product. The effects of water content, syn-gas composition and reaction temperature were also discussed. Extra water inhibited HDS when usingeither in situ H 2 or molecular H 2 . Syn-gas could be used for providing in situ H 2 for LGO upgrading. Higher reaction temperature favoured both HDS and hydrocracking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".