Transport of Ultradispersed Catalytic Particles Through Bitumen at Upgrading Temperatures
Bibliographic record
Abstract
Mass transfer and deposition of fine particles in cylindrical channels has received considerable attention for a long time due to its practical significance and direct application in industry. For example, this knowledge is helpful in aerosol classification and its deposition under electrical fields, formation of deposits in heat exchangers and pipelines, hydrodynamic field chromatography, thrombus formation in organs and, many other areas Recently, this phenomenon has gained particular importance on the dispersion of ultradispersed catalysts for heavy crude oil and bitumen hydroprocessing due to its practical significance and direct application Ultradispersed catalysts have been studied for heavy oil and bitumen hydroprocessing as an alternative for typical supported catalysts. An advantage when comparing ultradispersed catalysts to supported ones, in the case of heavy oil and bitumen hydroprocessing, is that the former could be easily incorporated into the reaction media to flow together with the feedstock to be treated, in this manner residence times can be longer than those conventionally used for hydroprocessing Recent publications (Loria et al., 2009b(Loria et al., , 2009c Time-dependent, two and three-dimensional convective-dispersive models, which simulated the transient deposition and suspension of ultradispersed particles immersed in viscous media inside a horizontal cylinder, were developed, solved and experimentally validated. In addition, a study on the effect of the fluid medium properties over the dispersion coefficient was performed. The dispersion coefficient is a proportionality constant that serves to quantify the particle concentration due to convection and dispersion and should be expressed as a function of the properties of the fluid medium The solution of the previously mentioned models provides a particle concentration profile along the horizontal channel, as well as information regarding the critical particle size that allows particles to remain suspended in the fluid medium enclosed in this geometry. This knowledge can be applied in the previously referred ultradispersed catalysis of heavy crude oils and bitumen. In these systems, it is important to ensure that catalytic particles remain suspended in the fluid medium in order to make use of their catalytic activity and also, to www.intechopen.com
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) | 0.001 | 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".