A framework for efficient large scale equation-oriented flowsheet optimization

Despite the economic benefits of flowsheet optimization, many commercial tools suffer from long computational times, limited problem formulation flexibility and numerical instabilities. In this study, we address these challenges and present a framework for efficient large scale flowsheet optimization. This framework couples advanced process optimization formulations with state-of-the-art algorithms, and includes several notable features such as (1) an optimization-friendly formulation of cubic equation of state thermodynamic models; (2) a new model for distillation column optimization based on rigorous mass, equilibrium, summation and heat (MESH) equations with a variable number of trays that avoids integer variables; (3) improvements on the Duran–Grossmann formulation for simultaneous heat integration and flowsheet optimization; and (4) a systematic initialization procedure based on model refinements and a tailored multi-start algorithm to improve feasibility and identify high quality local solutions.Capabilities of the framework are demonstrated on a cryogenic air separation unit synthesis study, including two thermally coupled distillation columns and accompanying multistream heat exchangers. A superstructure is formulated that includes several common ASU configurations in literature. As part of the optimization problem the solver selects the best topology in addition to operating conditions (temperatures, flowrates, etc.) for coal oxycombustion applications. The optimization problem includes up to 16,000 variables and 500 degrees of freedom, and predicts specific energy requirement of 0.18 to 0.25 kWh/kg of O₂ depending on design assumptions. These results are compared to literature and plans to extend the framework to an entire coal oxycombustion power plant optimization study are discussed.