ABSTRACT
ABST R AC T Cryogenic air separation, while widely used in industry, is an energy intensive process. Effective design can improve efciency and reduce energy consumption, however, uncertainties can make determination of the optimal design difcult. This paper addresses the conceptual design of air separation considering two types of unknown information: uncertain physical properties and variable product demands. A rigorous, highly nonlinear model including three columns with recycle is built to capture the coupled nature of air separation systems. Using a multi-scenario approach to discretize the uncertainty space gives rise to a large-scale, structured nonlinear programming formulation. IPOPT, a rigorous interior-point implementation, is used to efciently solve this difcult nonlinear optimization problem. The optimal value of the design variables found with and without considering uncertainties are compared in detail.
