Dynamic Surrogate Modeling Using Latent Variable Methods and Neural Networks for Market-Driven Operation of an Air Separation Unit

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 …