Heat exchanger networks retrofit by coupling genetic algorithm with NLP and ILP methods

Abstract

This paper addresses the revamping of heat exchanger networks (HENs) using genetic algorithm (GA) coupled with nonlinear programming (NLP) and integer linear programming (ILP) methods. Structural modifications are carried out by the GA in which node representation is used for the addressing of exchanger locations. Continuous variables are handled using a modified NLP formulation for maximum energy recovery (MER). Simultaneous optimization of the NLP is replaced by a search loop to find the best minimum approach temperature and split ratios. In this way the NLP is converted to an LP procedure which is easier to solve. After each LP, an ILP problem is solved to determine the minimum investment cost of modifications. The ILP determines the elimination or reuse of current exchangers and/or introducing new ones to the network. Results show that the proposed method usually finds better solutions than those reported in the literature.