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

TL;DR

This paper develops linear scale-bridging models for demand response in industrial air separation units, using time-series analysis and Kalman filtering for online updates, enabling more accurate and feasible scheduling.

Contribution

It introduces a novel linear SBM approach for demand response, with an online updating strategy using Kalman filtering, improving model accuracy over time.

Findings

Linear SBMs are suitable over typical scheduling horizons.

Online updating significantly improves SBM accuracy.

Kalman filtering effectively estimates model parameters in real-time.

Abstract

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller 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.