Dynamic Surrogate Modeling Using Latent Variable Methods and Neural Networks for Market-Driven Operation of an Air Separation Unit
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Bibliographic record
Abstract
This work presents a dynamic surrogate modeling framework that combines latent variable methods and neural networks for accurate and computationally efficient market-driven dynamic optimization of an air separation plant. The high-dimensional full-order model (FOM) consisting of ≈ 3800 states is projected onto a 10-dimensional latent subspace using principal component analysis (PCA). Following order reduction, a rectified linear unit (ReLU)-activated multilayer perceptron (MLP) neural network is trained to compute step-ahead predictions of the latent states in addition to the squared prediction error (SPE) statistic of the step-ahead prediction. The ReLU network is embedded directly into a discrete time reformulation of the optimization problem using complementarity conditions, and a trust region is enforced during optimization by constraining the SPE along the prediction horizon to be within specified confidence limits. The latent variable-based surrogate model (LV-SM) is validated through multistep-ahead simulation case studies, demonstrating high prediction accuracy for restoration of not only the states directly relevant to optimization but also the entire original state-space. The LV-SM’s performance in dynamic optimization is studied using a market-driven optimization case study, where it achieves a solution nearly identical to the FOM with nearly 3 orders of magnitude reduction in computation time using a two-tiered optimization approach. The results of this work highlight the potential of the LV-SM as a substitute for high-dimensional and complex first-principles-based industrial process models, particularly for use in real-time operations applications.
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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 | 0.001 | 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) | 0.000 | 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