Study Of Design And Optimal Operation Of Heat Exchanger Network

Heat exchanger network (HEN) is an important part of energy recovery in petrochemical industry. A proper design of HEN has great impact on reducing energy consumption and controlling capital cost. During the past 60 years, heat exchanger network synthesis (HENS) has been the subject of a considerable amount of research. However, most of them are in the design of HEN for fixed parameters. Actually there are significant changes in the environment of a plant. A good HEN should be optimal for a set of nominal conditions as well as provide the flexibility to handle a range of operating conditions. Therefore, the design of flexible HEN considering variable operating conditions has been attractive subject for more and more researchers.In the first section, a novel approach based on stage-wise superstructure representation is proposed for single period and multi-period HENS without isothermal mixing assumption. The HENS problem can be formulated as mixed integer non-linear programming (MINLP) model, which is solved by particle swarm optimization (PSO). Three benchmark examples are studied to prove the effectiveness of the presented method.During this section, the flexibility index of the given network is evaluated to determine whether the network satisfy flexibility target. The index model can be defined as a MINLP model, which can be simplified through slack and solved with PSO. The examples are investigated using flexibility index and downstream paths method. The optimal network should be evaluated by the trade-off between cost and flexibility demanding.A general model is proposed for optimal operation of HEN, which is formulated as non-linear programming (NLP). The presented model consists of exchanger model, splitter model, mixer model, by-pass model and relation model between unit models. The HEN structure is represented with stage-wise superstructure presented by Yee and Grossmann (1990). A solution strategy based on particle swarm optimization algorithm is developed for NLP problem, which promises simple, fast and robust searching procedure. The proposed method is applied to three examples taken from previous research about HENS, and the results prove that the method is more effective to optimal operation problem of HEN.