Advances in MINLP to Identify Energy-efficient Distillation Configurations

TL;DR

This paper introduces a novel MINLP-based method for designing energy-efficient distillation sequences, significantly reducing energy consumption and greenhouse emissions compared to heuristic strategies.

Contribution

The paper presents the first rigorous MINLP approach for distillation configuration design, incorporating tighter formulations, partial fraction decomposition, convex hull results, and advanced discretization techniques.

Findings

Outperforms existing methods in energy efficiency.

Reduces computational complexity for configuration selection.

Provides a scalable solution for large configuration spaces.

Abstract

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, where crude distillation alone is estimated to consume 6.9 quads of energy per year globally. The challenge in solving this problem arises from the large number of feasible configuration sequences and because the governing equations contain non-convex fractional terms. We make several advances to enable solution of these problems. 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.