Today's fast-changing markets often require the granularity of production schedules to be refined to time scales comparable to the time constants of a chemical process. Consequently, the process dynamics must be considered explicitly in production scheduling. High dimensionality, nonlinearity, and the associated computational complexity make incorporating dynamic models in scheduling calculations challenging. We propose a novel scheduling approach based on scheduling-oriented low-order dynamic models identified from historical process operating data. We introduce a methodology for selecting scheduling-relevant variables and identify empirical models that capture their dynamic response to production target changes imposed at the scheduling level. The optimal scheduling calculation is then formulated as a dynamic optimization aimed at minimizing operating cost. We apply these concepts to an industrial-size model of an air separation unit operating under time-sensitive electricity prices. Our approach reduces computational effort considerably while preserving essential information required for the optimal schedule to be feasible from a dynamic point of view. Extensive simulations show that significant savings can be derived from operating in a transient regime, where the production rate is increased when energy prices are low, and reduced during peak price periods, while taking advantage of available storage capacity.

The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017

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In this paper, we introduce a singular perturbations framework for the dynamic analysis and model reduction of building models. Working with a prototype building, we present a theoretical justification of the empirically acknowledged multiple time scale dynamic response of buildings, and develop a mathematically rigorous methodology for deriving reduced-order models for the dynamics in each time scale. Our analysis accounts for the potential use of Heat Recovery Ventilators (HRVs), and we show that their presence leads to the emergence of a dynamic behavior with three time scales, including an overall, system-wide component which involves both the building and the HVAC system. The second part of the paper presents a simulation case study, where we demonstrate the use of the derived reduced-order models in the synthesis of a nonlinear predictive model-based optimal energy management strategy for a single-zone test building situated on the University of Texas campus. The proposed controller exhibits excellent performance, can easily be executed in realtime and its application results in significant energy savings compared to setpoint tracking strategies.