Data-Driven Approach for Predicting Gasoline Yield in an FCC Unit Charged with Light and Heavy Feedstocks: A PCA-Guided Grouping for Enhanced Modeling Experience
Bibliographic record
Abstract
This work introduces a feed-aware modeling framework that integrates ensemble anomaly detection, PCA-based segmentation integrated with feed chemistry, and Bayesian-optimized regression to improve the model performance of a Fluid Catalytic Cracking (FCC) unit using pilot-plant experimental data. Three regression models were trained using 5-fold cross-validation and optimized using Bayesian Optimization: Gaussian Process Regression (GPR), Boosted Trees (BT), and Robust Linear Regression (RLR). Modeling on the full unsegmented data set showed that conversion was a highly predictable target. Both GPR and BT achieved strong performance (mean R 2 values of approximately 0.72 to 0.73 after optimization), while RLR remained unstable due to its linear assumptions. For the gasoline yield, BT with optimization performed best (mean R 2 of approximately 0.67), while GPR and RLR with optimization showed limited predictive skill ( R 2 values of approximately 0.25 and −0.51, respectively). These results reflect the nonlinear and distributed nature of yield formation. PCA revealed a clear latent structure that aligned strongly with feedstock type, and K-means clustering confirmed this separation by distinguishing naphtha from VGO samples in a chemically meaningful way. Thus, the data set was segmented into feedstock groups, which yielded substantial improvements, especially for GPR. In the naphtha subset, GPR with optimization achieved mean R 2 values of 0.956 for conversion and 0.902 for gasoline yield, with very low associated error values. Performance also improved for VGO, although to a lesser extent, while RLR continued to underperform. Overall, segmentation based on PCA and clustering was essential for improving model reliability in FCC prediction tasks. Among all algorithms evaluated, GPR consistently delivered the highest accuracy and generalizability across feed types and targets, supporting its suitability for data-driven optimization of catalytic cracking performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".