An alternative strategy for global optimization of heat exchanger networks

• Published in: Applied thermal engineering Vol. 43; pp. 75 - 90
• Main Authors: Bogataj, Miloš, Kravanja, Zdravko
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2012; Elsevier
• Subjects: Applied sciences; Combinatorial analysis; Computation; Devices using thermal energy; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Global optimization; Heat exchanger network; Heat exchangers; Heat exchangers (included heat transformers, condensers, cooling towers); Heat transfer; Mathematical models; MINLP; Multilevel domain partitioning; Nonlinearity; Optimization; Piecewise underestimators; Strategy; Synthesis; Theoretical studies. Data and constants. Metering; MINLP; Piecewise underestimators; Global optimization; Multilevel domain partitioning; Heat exchanger network; Engineering; Heat exchanger; Algorithm; Optimization
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2011.12.015

The HEN synthesis problem is one amongst many engineering problems which can be characterized as highly combinatorial, nonlinear and nonconvex, all contributing to computational difficulties shown either in a form of long computational times and/or in identifying poor locally optimal solutions. In this work, a new strategy for global optimization of heat exchanger networks (HENs) is presented. We first introduce a concept of stage-wise superstructure augmented by an aggregated substructure. On this basis, the HEN synthesis problem is formulated as a mixed integer nonlinear program (MINLP). The strategy for providing globally optimal solutions relies on solving a single convex MINLP which incorporates piecewise linear and nonlinear convex underestimators of the nonconvex linear fractional terms present in the nonconvex MINLP. It is shown that the optimal solution of the convex MINLP can provide a lower bound tight enough that the gap between the upper and lower bound falls below 1%. In addition, an algorithm for identifying good locally optimal solutions is presented. The approach was tested on two examples, showing that currently we are able to solve small HEN synthesis problems to global optimality with reasonable computational effort, while good locally optimal solutions can be identified for larger problems.