Supercritical water deasphalting and desulphurization of heavy fuel oil with comprehensive molecular‐level analysis and techno‐economic analysis
Bibliographic record
Abstract
Abstract The removal of sulphur from heavy fuel oil (HFO) is essential to address environmental concerns and comply with the stringent regulations imposed by the International Maritime Organization (IMO) in 2020. Previously, the acceptable limit for sulphur was 3.5 wt.%, but it was recently changed to 0.5 wt.%. Hence, upgrading HFO into low‐sulphur marine fuel can be achieved by removing its heptane‐insoluble asphaltene fraction. Solvent deasphalting, typically used in the petroleum industry, can be applied for deasphalting HFO, but this study investigates the supercritical water deasphalting (SCWDA) process, to develop a scalable deasphalting process for upgrading HFO into low‐sulphur marine and power‐generation fuel. Multiple variations of supercritical water deasphalting experiments were carried out to evaluate the effects of process parameters to optimize upgrading conditions. Characterization of the deasphalted oil (DAO) and the precipitated solid material with nitrogen and sulphur (NS) analyzer, and thermogravimetric analysis (TGA) confirmed the complete removal of asphaltene along with a significant amount of resins from HFO. The HFO, DAO, and the asphaltene fraction were further analyzed by Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) and nuclear magnetic resonance (NMR). SCWDA reduced the sulphur content of HFO from 34,270 ppm to 6690 ppm. Techno‐economic analysis (TEA) shows significant economic viability, resulting in lower production cost for deasphalted oil (DAO) at 647 USD per tonne (capacity: −40,000 barrel/day feed; yield of DAO: −98%; Discount rate of return: −8%; Reference year: −2023–24; Location: Saskatoon, Canada).
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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) | 0.000 | 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".