Multi-objective optimization of heat exchanger network with disturbances based on graph theory and decoupling

Abstract

Disturbance poses a significant challenge to highly coupled chemical systems. In this work, a multi-objective optimization framework is developed for reducing the impact of fluctuations on heat exchange networks (HENs) based on graph theory and decoupling. The framework bridges the gap between algebra and structure of HEN through a directed graph (Digraph) and defines the Excitation as a fluctuation impact indicator. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is improved for generating feasible solutions and global optimality, and the quasi-gradient is applied to select an equilibrium solution from the Pareto Front. Two case studies are conducted, and the equilibrium solution is selected based on quasi-gradient. For the literature case, the Excitation of the equilibrium solution and the total annual cost is reduced by 56.15% and 2.12%, respectively. For the coal-to-methanol system, the utility consumption and Excitation are 29.6% and 264.50% lower than the results of the Aspen Energy Analyzer, respectively.