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Demand-response operation of air separation units requires frequent changesin production rate(s), and scheduling calculations must explicitly considerprocess dynamics to ensure feasibility of the solutions. To this end,scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of aprocess and its controller in a low-order representation. In contrast toprevious works that have employed nonlinear SBMs, this paper proposes linearSBMs, developed using time-series analysis, to facilitate online schedulingcomputations. Using a year-long industrial dataset, we find that compact linearSBMs are suitable approximations over typical scheduling horizons, but thattheir accuracies are unpredictable over time. We introduce a strategy foronline updating of the SBMs, based on Kalman filtering schemes for onlineparameter estimation. The approach greatly improves the accuracy of SBMpredictions and will enable the use of linear SBM-based demand-responsescheduling in the future.

Identification and online updating of dynamic models for demand response of an industrial air separation unit