Desulfurization of Heavy Oil–Oxidative Desulfurization (ODS) As Potential Upgrading Pathway for Oil Sands Derived Bitumen
Bibliographic record
Abstract
Heavy oil usually contains percentage levels of sulfur. Most of the sulfur in heavy oil is found in bulky thiophenic structures. Thiophenic sulfur is difficult to remove by catalytic hydrodesulfurization, but it can readily be oxidized. The sulfoxides and sulfones produced from sulfur oxidation can be solvent extracted from the heavy oil as a result of their increased polarity. Oxidative desulfurization of heavy oil was studied using Canadian Cold Lake bitumen (5% S, 1100 kg/m 3 ), with air as oxidant. At the conditions investigated, namely, autoxidation at 145–175 °C followed by water washing, 46–47% of the sulfur in the bitumen could be removed. This is equivalent to >20 kg sulfur per ton oil. Part of the sulfur was removed as SO 2 and part as water extracted sulfur-containing compounds. Lower autoxidation temperatures led to better desulfurization. The main challenge was to prevent free radical addition reactions that cause a viscosity increase and bitumen hardening. Autoxidation of undiluted bitumen and bitumen–water mixtures resulted in hardening. Hardening was prevented when bitumen was diluted with naphtha ( n -heptane). However, the oxidized sulfur compounds could not be extracted with water from the bitumen–heptane phase, and some material was precipitated as a result of solvent deasphalting. Oxidation selectivity was studied using a model dibenzothiophene and n -heptane mixture. Some precipitation was also observed, and the chemistry was analogous to the precipitation chemistry (gum formation) that undermines storage stability of transportation fuels.
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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".