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

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 nd 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 ltering 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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

HD gas purification for polarized HDice targets production at Jefferson Lab

Solid, frozen-spin targets of molecular HD were rst developed for nuclear physics by a collaboration between Syracuse University and Brookhaven National Lab. They have been successfully used in measurements with photon beams, rst at the Laser-Electron-Gamma-Source [1] and most recently at Je erson Lab during the running of the E06-101 (g14) experiment [2]. Preparations are underway to utilize the targets in future electron experiments after the completion of the 12 GeV JLab upgrade [3]. HD is an attractive target since all of the material is polarizable, of low Z, and requires only modest holding elds. At the same time, the small contributions from the target cell can be subtracted from direct measurements. Reaching the frozen-spin state with both high polarization and a signi cant spin relaxation time requires careful control of H2 and D2 impurities. Commercially available HD contains 0.5 - 2% concentrations of H2 and D2. Low-temperature distillation is required to reduce these concentrations to the 104 level to enable useful target production. This distillation is done using a column lled with heli-pack C [4] to give good separation e ciency. Approximately 12 moles of commercial HD is condensed into the mechanically refrigerated system at the base temperature of 11K. The system is then isolated and the temperature stabilized at 18K producing liquid HD, which is boiled by a resistive heater. The circulation established by the boil-o condensing throughout the column then ltering back down produces a steady-state isotopic separation permitting the extraction of HD gas with very low H2 and D2 content. A residual gas analyzer initially monitors distillation. Once the H2 concentration falls below its useful operating range, samples are periodically collected for analysis using gas chromatography [5] and Raman scattering. Where the measurement techniques overlap, good agreement is obtained. The operation of the distillery and results of gas analysis will be discussed. References [1] Phy. Rev. Lett. 101 (2009) 172002. [2] www.jlab.org/exp_prog/proposals/06/PR-06-101.pdf [3] www.jlab.org/exp_prog/proposals/12/PR12-12-009.pdf, www.jlab.org/exp_prog/proposals/12/PR12-12-010.pdf, and www.jlab.org/exp_prog/proposals/11/PR12-11-111.pdf [4] Nucl. Inst. Meth. 664 (2012) 347, www.wilmad-labglass.com/Products/LG-6730-104/ [5] Rev. Sci. Instrum. 82, 024101 (2011).

Whisnant, Charles↗

Multilevel ensemble Kalman filtering

This study embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. Finally, the resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

ltering↗