Document Type : Full article

Abstract

Synthesis of heat exchanger networks (HENs) is inherently a mixed integer and nonlinear programming (MINLP) problem. Solving such problems leads to difficulties in the optimization of continuous and binary variables. This paper presents a new efficient and robust method in which structural parameters are optimized by genetic algorithm (G.A.) and continuous variables are handled due to a modified objective function for maximum energy recovery (MER). Node representation is used for addressing the exchangers and networks are considered as a sequence of genes. Each gene consists of nodes for generating different structures within a network. Results show that this method may find new or near optimal solutions with a less than 2% increase in Hen annual costs.

Keywords

• Heat exchanger networks (HENs)
• optimization
• Genetic Algorithm (G.A.)
• NLP formulation

Main Subjects

• Modeling and Simulation
• Transport Phenomena,