A Neural Network Approach for Identification and Modeling of Delayed Coking Plant
Bibliographic record
Abstract
In this study, an artificial neural network (ANN) modeling of a delayed coking unit (DCU) is proposed. Different data from various DCU have been collected. Feed API and Cat Cracker (CCR) weight percent have been considered as network inputs. Coke, output CCR, light gases, gasoline, gas-oil and C5+ weight percents are the network outputs. 70 percent of the data have been used for training of ANN. Among the Multi Layer Perceptron (MLP) architectures a network with 31 hidden neurons has been found as best MLP predictor. Radial Basis Function (RBF) also has been implemented for identification of the plant. An RBF network with 20 spread was found as best estimator of the DCU. Best RBF network and best MLP network performance in prediction of 30 percent of unseen data were compared. It was found that RBF method has the best generalization capability and was used in DCU modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".