Reactivity and Comprehensive Kinetic Modeling of Deasphalted Vacuum Residue Thermal Cracking
Bibliographic record
Abstract
Upgrading and refining of heavy oil and bitumen has become a costly practice in the last decades, creating a need for new processes to appropriately convert these heavy feedstocks into lighter and more valuable materials. Solvent deasphalting (SDA) is a carbon rejection process where asphaltenes are removed from the oil using a paraffinic solvent, producing a lighter deasphalted oil (DAO) stream that can be further upgraded without the limitations of asphaltene instabilities. In this work, upgrading via thermal cracking of deasphalted vacuum residue was assessed in a bench scale pilot plant equipped with an up-flow open tubular reactor. Two different feedstocks were evaluated: recycled and virgin DAO. Reactivity experiments were carried out at temperatures within the interval 380–423 °C and liquid hourly space velocities (LHSV) of 0.25–3 h –1 . The effects of operating pressure and steam partial pressure on the thermal crackability of DAO were also evaluated as not having a significant effect within the 150–300 psig range. Experimental results showed increased reactivity of virgin DAO compared to that of recycled DAO as well as better quality and higher selectivity to more valuable products. A lumped kinetic modeling including asphaltene generation was evaluated. Different scenarios were assessed to incorporate chemical considerations within the complex lumped kinetic model developed, which have a significant effect on the kinetic parameters as well as the possible interpretation of the results. Overall, typical behavior was observed for thermal cracking with global activation energies of 241 and 226 kJ/mol for recycled and virgin DAO (560 °C+), respectively. Good fitting of the model with experimental data with relative errors around 7% and clear kinetic relevance of the asphaltenes generation reaction during thermal cracking was highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".