Generalized optimization framework for synthesis of thermally coupled distillation columns

In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation-oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero-flow issues. The synthesis problem is formulated as a Mixed Integer Nonlinear Programming problem, which is optimized using our previously developed Feasible Path-Based Branch and Bound method, coupled with an improved Sequential Quadratic Programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%.