Hybrid Model for Optimization Of Crude Distillation Units
Why this work is in the frame
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Bibliographic record
Abstract
Planning, scheduling and real time optimization (RTO) are currently implemented by using different types of models, which causes discrepancies between their results. This work presents a single model of a crude distillation unit (preflash, atmospheric, and vacuum towers) suitable for all of these applications, thereby eliminating discrepancies between models used in these decision processes. Hybrid model consists of volumetric and energy balances and partial least squares model for predicting product properties. Product TBP curves are predicted from feed TBP curve, operating conditions (flows, pumparound heat duties, furnace coil outlet temperatures). Simulated plant data and model testing have been based on a rigorous distillation model, with 0.5% RMSE over a wide range of conditions. Unlike previous works, we do not assume that (i) midpoint of a product TBP curve lies on the crude distillation curve, and (ii) midpoint between the back-end and front-end of the adjacent products lies on the crude distillation curves, since these assumptions do not hold in practice. Associated properties (e.g. gravity, sulfur) are computed for each product based on its distillation curve. Model structure makes it particularly amenable for development from plant data. High model accuracy and its linearity make it suitable for optimization of production plans or schedules.
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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) | 0.002 | 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 it