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

摘要

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