Generalized modular framework for the representation and synthesis of complex distillation column sequences

In this paper, the complex distillation column sequencing problem for the separation of mixtures into their constituent components is addressed through the Generalized Modular Framework (GMF), based on formal superstructure optimization techniques. The proposed method overcomes structural complexities through the use of systematically composed structural models, incorporating all the feasible sequencing alternatives. The generated sequences are evaluated with respect to their energy efficiency, based on aggregated physical models, generating compact optimization problems while avoiding the use of potentially limiting simplifying assumptions. The synthesis method coupled to a formal mixed integer nonlinear programming (MINLP) solution algorithm was applied to illustrating case studies generating substantial energy savings by finding systematically and validly the most energy-efficient distillation column sequence.