Optimal Startup of Cryogenic Air Separation units: Modeling, Simulation, Optimization, and Control

Abstract

Cryogenic air separation units (ASUs) are the most widely used technology for industrialscale production of large amounts of high-purity air components. These are highly energyintensive processes, which have motivated the development of demand response strategies to adapt their operation in response to the increased volatility of the energy market. The startup of ASUs warrants particular consideration within this context. ASUs are tightly integrated, thermally and materially, and have slow dynamics. These result in startup times on the order of hours to a day, during which electricity is consumed with limited revenue generation. In the current environment of electricity price deregulation, it may be economically advantageous for ASUs to shut down during periods of high electricity pricing, increasing the occurrences of startups. This presents a promising research opportunity, especially because ASU startup has received relatively little attention in the literature. This thesis investigates the optimal startup of ASUs using dynamic optimization. First, this thesis focuses on startup model development for the multiproduct ASU. Startup model development requires accounting for discontinuities present at startup. Four main discontinuities are considered: stage liquid flow discontinuity, stage vapor flow discontinuities, flow liquid out of sumps and reboilers, and opening and closing valves. Other types of discontinuities accounted for include the change in the number of phases of streams. These discontinuities are approximated with smoothing formulations, using mostly hyperbolic tangent functions, to allow application of gradient-based optimization. The modeling approach was assessed through three case studies: dynamic simulation of a successful startup, dynamic simulation of a failed startup, and dynamic optimization using a least-squares minimization formulation. Following startup model development, this thesis investigates the development of a framework for optimizing ASU startups using readily interpretable metrics of time and economics. For economics, cumulative profit over the startup horizon is considered. Two events are tracked for the definition of time metrics: time taken to obtain product purities and time to obtain steady-state product flows. Novel approaches are proposed for quantifying these time metrics, which are used as objective functions and in formulating constraints. The