Simulation of hydrodesulfurization using artificial neural network
Bibliographic record
Abstract
Abstract Artificial neural network (ANN) is applied to investigate the hydrodesulfurization (HDS) process with light‐cycle oil as feed and NiMo/Al 2 O 3 as catalyst. ANN models frequently work as a “black box” which makes the model invisible to users and always need significant data for training. In this work, a new ANN is proposed. The Langmuir–Hinshelwood kinetic mechanism is incorporated into the model so that the proposed ANN model is forced to follow the given reaction mechanisms. Both advantages of self‐learning ability of ANN and the existing knowledge of HDS were taken into account. Lengthy training process is minimised. Effects of operating temperature, pressure, and LHSV on the sulfur removal rate are studied. The inhibition of nitrogen compounds is also investigated. It is shown that the presence of nitrogen can significantly reduce the conversion rate of sulfur components, in particularly, hard sulfur such as 4,6‐DMDBT.
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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".