Reactivity Study and Kinetic Modeling of Deasphalted Oil Upgrading via Thermal and Catalytic Steam Cracking
Bibliographic record
Abstract
Solvent deasphalting (SDA) is a well-known process where asphaltenes are removed from the oil using a paraffinic solvent to produce a lighter and better quality deasphalted oil (DAO). SDA has uses in refineries and bitumen upgraders. In the latter use, the DAO still needs further upgrading to make it a transportable oil that meets pipeline specifications. This doctoral thesis covers the use of thermal cracking as a DAO upgrading technology, as well as catalytic steam cracking using innovative Ni/K and Ni/Ce ultradispersed (UD) and Ni/Ce fixed-bed (FB) catalyst formulations as an alternative path to further improve upon the thermal cracking performance. A detailed reactivity study and comprehensive kinetic modeling for these DAO processing methods is conducted and discussed throughout the work. The experimental data for the reactivity experiments is obtained in a research-scale pilot plant equipped with an up-flow open tubular reactor, which was procured, designed and constructed as part of this doctoral project. A complete set of characterization techniques is used for detailed evaluation of the performance of the processes and the analysis of relevant properties related to the quality of the liquid and gas products. The effect of operating conditions on DAO thermal cracking including Liquid Hourly Space Velocity (LHSV), reaction temperature, total pressure and steam partial pressure was assessed and was found to be consistent with results in the literature regarding thermal cracking of residual hydrocarbons. Considerable generation of asphaltenes during thermal cracking was found to be the main drawback of this process and their production was analyzed from a kinetic point of view. Catalytic steam cracking was found to have the same relevant kinetic and rate controlling steps as that of thermal cracking in terms of conversion and the effect of operating variables, however having a catalytic effect to promote water dissociation to induce hydrogenation and enhancement of the overall product quality. Operating conditions, as well as the state of oxidation of the catalyst active phase were found to play a critical role for the appropriate performance of the catalytic process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".