Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units often involves frequent changes in process setpoints (e.g., production rate), and therefore dynamics should be considered scheduling calculations to ensure feasibility. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant a closed-loop low-order representation. In contrast previous works that have employed nonlinear SBMs, paper proposes linear developed using time-series analysis, facilitate online computations. Using year-long industrial dataset, we find compact SBMs are suitable approximations over typical horizons, but their accuracies unpredictable time. We introduce strategy for updating based on Kalman filtering schemes parameter estimation. The approach greatly improves accuracy SBM predictions will enable use SBM-based demand-response future.