Knowledge-Informed Semi-Supervised Robust Modeling for Mixed Oil Length Prediction in Multi-Product Pipelines Based on Student’s-T Mixture Model
Bibliographic record
Abstract
Abstract Multi-product pipelines usually transport various petroleum products in certain batch, leading to inevitable mixed oil segment. Current soft sensor modeling methods for predicting mixed oil length face challenges characterized by nonlinearity, scarcity of labeled samples and interference from outliers. We propose a semi-supervised robust soft sensor called the KISSVBSMR. It employs the Semi-supervised Variational Bayesian Student’s-t mixture regression to empower the usage of unlabeled data, and it strategically selects the key variable based on physical significance to enhance mode identification. Additionally, by transforming the relationship between input and output variables derived from theoretical equation and incorporating a Bayesian approach, the KISSVBSMR can explore the prior knowledge regarding the regression coefficient and efficiently against outliers. Evaluation through numerical and real-life industrial datasets reveals the shortcomings of traditional methods and the superiority of KISSVBSMR in model performance. This novel approach improves the predictive accuracy of mixed oil lengths, optimizing multi-product pipeline operations and advancing knowledge-data hybrid modeling techniques.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".