An NLP Approach for Evolution of Heat Exchanger Networks Designed by Pinch Technology

Common methods to design heat exchanger networks (HENs) by pinch technology usually need an evolutionary step to reduce the number of heat transfer units. This step is called loop breaking and is based on the removal of exchangers that impose minimum increase on utility consumption. Loops identification and breaking is a tedious task and becomes more complicated in large networks. This paper presents a rapid nonlinear programming (NLP) formulation for the evolution of HENs in which loop identification is not required. The objective of the NLP is the minimization of HENs annual cost, which is not considered in current methods. In this method a search is done to find the best units elimination of which improves HENs annual cost. The search continues until the minimum number of units (MNU) is achieved and the exchangers that must be removed from the network are specified. The method was applied to some networks reported in the literature and better results were obtained. Also, the convergence of the presented method is very fast and it can be applied to different networks designed by pinch technology.