The Impact of Process Parameters on the Deposition of Fines Present in Bitumen-Derived Gas Oil on Hydrotreating Catalyst
Bibliographic record
Abstract
The gradual increase in the amount of fines (<20 μm) deposited in a hydrotreating reactor results in a sudden pressure drop, leading to premature reactor shutdown. In addition to the nature of the fine particles, it is believed that the particle deposition is a function of the process parameters of the reactor. Among the process conditions that influence the hydrotreating process, temperature and pressure have been identified as key variables; therefore their impact on fines deposition on NiMo/γAl 2 O 3 catalyst was studied. Model fine particles were suspended in light gas oil (LGO) feed, and the feed was hydrotreated in a batch reactor. The catalyst was characterized to understand its interaction with the model fine particles. Mass balance results and SEM images were used to quantitatively analyze the deposition of fine particles on the catalyst bed. The statistical analyses were performed using central composite design (CCD) to optimize the hydrotreating conditions for bed deposition and sulfur conversion as a function of process parameters such as temperature (355–375 °C), pressure (1200–1400 psig), and particle loading (1–1.5 g of fines) in 200 mL of oil. High fines concentration in the feed (particle loading) and high temperature led to higher bed deposition. The results obtained helped in understanding the impact of process parameters on particle deposition in a batch reactor and not in a packed bed reactor. The optimum temperature to have a significant sulfur conversion with least fines deposition for LGO feed with process conditions within the boundary of this research for a batch reactor was found to be in range of 360 to 364 °C.
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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 | 0.000 |
| 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".