Modeling the Hydrocracking Process with Deep Neural Networks
Bibliographic record
Abstract
In the refinery process, a vast amount of data is generated in daily production. How to make full use of these data to improve the simulation’s accuracy is crucial to enhancing the refinery operating level. In this paper, a novel deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) is developed for modeling the industrial hydrocracking process. The SOM is used to map input variables into two-dimensional maps to extract process features. Then, these maps are fed into the CNN to predict the outputs of the hydrocracking process. The SOM adopted is free of training, which reduces the computational complexity, simplifies the application, and improves the prediction accuracy. Practical guidance on the application of the proposed framework is provided by comparing and analyzing different structures and parameters. Finally, an online modeling scheme is developed and applied in an actual hydrocracking process. Experimental results demonstrate that the proposed framework has great performance in modeling the hydrocracking process and provides a good reference for process optimization.
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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) | 0.000 | 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 | 0.001 |
| 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".