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High purity distillation columns and multi-stream heat exchangers (MSHXs) are critical units in cryogenic air separation plants. This article focuses on modeling approaches for the primary section of a super-staged argon plant. A full-order stage-wise model for distillation columns in air separation units (ASUs) that considers key process phenomena is presented, followed by a reduced-order model using a collocation approach. The extent of model reduction that can be achieved without losing significant prediction accuracy is demonstrated. A novel moving boundary model is proposed to handle MSHXs with phase change. Simulation results demonstrate the capability of the proposed model for tracking the phase change occurrence along the length of the heat exchanger. Dynamic simulation studies of the integrated plant show that the thermal integration between the feed and product streams captured in the primary heat exchanger is critical to accurately capture the behavior of ASUs. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1602-1615, 2016 [ABSTRACT FROM AUTHOR]

• A data-driven modeling and optimization framework for integration of scheduling and control. • Application to optimal demand response (DR) of industrial air separation unit. • Extensive case study reveals the economic benefits of DR engagement in the real-time and day-ahead electricity markets. • Monte Carlo sensitivity analysis examines the effect of uncertainty in electricity price forecasts. Managing electricity demand has become a key consideration in power grid operations. Industrial demand response (DR) is an important component of demand-side management, and electricity-intensive chemical processes can both support power grid operations and derive economic benefits from electricity price fluctuations. For air separation units (ASUs), DR participation calls for frequent production rate changes, over time scales that overlap with the dominant dynamics of the plant. Production scheduling calculations must therefore explicitly consider process dynamics. We introduce a data-driven approach for learning the DR scheduling-relevant dynamics of an industrial ASU from its operational history, and present a dynamic optimization-based DR scheduling framework. We show that a class of low-order Hammerstein-Wiener models can accurately represent the dynamics of the industrial ASU and its model predictive control system. We evaluate the economic benefits of the proposed scheduling framework, and analyze their sensitivity to electricity price uncertainty. [ABSTRACT FROM AUTHOR]

In the air separation industry, distillation-based cryogenic separation is the dominant technology for large-scale production of high-purity nitrogen, oxygen, and argon products. The use of rigorous dynamic models in the design and operation of air separation units can provide insights into the plant operation to inform the development of economically beneficial designs and operating practices. In this study, we provide a comprehensive analysis on a liquid storage and vaporization strategy for air separation units following a two-tiered multiperiod formulation with a collocation-based dynamic model. Economic incentives for collecting liquid, either directly as liquid product or by liquefaction of overproduced gas product, and then redistribution for meeting gas product demand or for use as additional reflux are explored in a transient market environment. This includes different electricity price and/or demand profiles, operation costs, as well as product specifications. Operation bottlenecks due to process dynamics constrain the operation practice and hence influence the potential profitability. [ABSTRACT FROM AUTHOR]