Dynamic Surrogate Modeling Using Latent Variable Methods and Neural Networks for Market-Driven Operation of an Air Separation Unit
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".