Abstract
Cryogenic air separation units (ASUs) are the primary industrial technology for producing high purity oxygen, nitrogen, and argon gases at commercial scale. Cryogenic ASUs are large consumers of electricity, making them ideal candidates for market-driven operation research in today's volatile and uncertain manufacturing environments. To maximize profitability, ASU operation must dynamically adapt to changing market conditions as they evolve. This work explores the implementation of a rolling-horizon scheduling (RHS) strategy for the real-time market-driven operation of a high-dimensional ASU model with inventory, responding to uncertainty in future plant demand and electricity price forecasts by periodically rescheduling in response to updated market information. A dynamic latent variable-based surrogate model (LV-SM) is used within the scheduling framework as a computationally efficient substitute for an existing first-principles-based ASU model. Results show that RHS and plant inventory are effective strategies for handling uncertainties in the future market forecasts, while the LV-SM shows computational performance suitable for real-time implementation. With this scheduling framework in place, extensions involving expanded case studies and uncertainty-aware optimization are planned for future work.