Enhanced superstructure optimization for heat exchanger network synthesis using deterministic approach

Abstract

Heat Exchanger Network (HEN) synthesis is primarily formulated as a mixed integer non-linear programming (MINLP) problem based on method of stage-wise superstructure (SWS). Approaches to obtain an optimal HEN configuration can adopt deterministic algorithms. But, as a large scale problem, it is difficult to solve due to the complexities arisen from nonlinearities of SWS. To overcome the model nonlinearities, stochastic algorithms and meta-heuristic approaches have been proposed in the literature to tackle the problem. However, it reaches a near-optimal HEN configuration from a series of stochastic solutions, which are obtained by the execution of many computational procedures with extensive time resource, and a global optimum will not be guaranteed from only randomly generated results. In this paper, an enhanced SWS approach is proposed, in which new temperature and heat duty constraints are updated to reduce the redundant combinations and avoid conflicted calculation of non-isothermal mixing energy balance. The present model is also extended to allow flexible stream splitting for practical applications. Then, a deterministic-based global solver (GAMS/BARON) is applied in solving three case studies. The results show that the proposed approach can provide cost-efficient HEN solutions with lower TAC than using existing stochastic and deterministic algorithms.