A New and Efficient NLP Formulation for Synthesizing Large Scale Multi-Stream Heat Exchanger Networks

Abstract

The heat exchanger network (HEN) synthesis problem is generally formulated as a mixed integer nonlinear programming (MINLP) by using the concept of HEN superstructures. Although these MINLP formulations can be used to solve the HEN synthesis problem, the size of the problem of their application is restrained because of their complexity. In this study, a new nonlinear programming (NLP) formulation of the simultaneous model for multi-stream heat exchanger network (MSHEN) synthesis is proposed based on stream pseudo-temperature. This formulation can be used to solve MSHEN synthesis problem with temperature enthalpy (T-H) diagram method based on optimal stream heat transfer temperature difference contributions using genetic/simulated annealing algorithm (GA/SA). A significant reduction on the size and complexity of the problem is achieved by the NLP formulation. Large scale MSHEN synthesis problem with unequal heat transfer film coefficients and different construction materials can be solved using the NLP formulation. The performance and efficiency of the NLP formulation and algorithm are demonstrated through two examples.