Advances in MINLP to Identify Energy-Efficient Distillation Configurations

In this paper, we describe the first mixed-integer nonlinear programming (MINLP)-based solution approach that successfully identifies the most energy-efficient distillation configuration sequence for a given separation. Current sequence design strategies are largely heuristic. The rigorous approach presented here can help reduce the significant energy consumption and consequent greenhouse gas emissions by separation processes. First, we model discrete choices using a formulation that is provably tighter than previous formulations. Second, we highlight the use of partial fraction decomposition alongside reformulation-linearization technique (RLT). Third, we obtain convex hull results for various special structures. Fourth, we develop new ways to discretize the MINLP. Finally, we provide computational evidence to demonstrate that our approach significantly outperforms the state-of-the-art techniques.

Funding: This work was supported by the U.S. Department of Energy [Grant DE-EE0005768], the Bilsland Dissertation Fellowship, and the National Science Foundation [Grant EEC-1647722].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2340.