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Heat exchanger network optimization has an important role in high-efficiency energy utilization and energy conservation. The thermal resistance of a heat exchanger network is defined based on its entransy dissipation. In two-stream heat exchanger networks, only heat exchanges between hot and cold fluids are considered. Thermal resistance analysis indicates that the maximum heat transfer rate between two fluids corresponds to the minimum entransy-dissipation-based thermal resistance; i.e. the minimum thermal resistance principle can be exploited in optimizing heat exchanger networks.heat exchanger network, entransy-dissipation-based thermal resistance, optimization, minimum thermal resistance principle Citation:Qian X D, Li Z, Li Z X. Entransy-dissipation-based thermal resistance analysis of heat exchanger networks. Chinese Sci Bull, 2011Bull, , 56: 3289-3295, doi: 10.1007 Heat exchanger networks (HENs) are widely used in energy transport and utilization. Two-stream HENs are a type of HENs in which hot and cold fluids do not exchange thermal energy directly through one heat exchanger due to environmental constraints or practical demands. Trivedi et al. [9] improved on this approach and introduced the dual temperature and pseudo-pinch method in HEN optimization. Analyzing by a mathematical programming method, a HEN is described by an objective function and constraint conditions. Cerda et al. [10] and Floudas et al.[11] solved this HEN optimization problem using linear programming (LP) and mixed-integer nonlinear programming (MINLP) methods respectively.In optimization designs of two-stream HENs, the heat transfer rate is mainly of concern to designers. To maximize the heat recovery or heat transfer rate, much research has been performed. In the research of run-around heat recovery system, Fan et al. [12] found that the system had the highest efficiency when the heat capacity ratio of air and the medial fluid was in the range 0.8-1.2. Zhou et al. [13] found that a ground-coupled liquid loop heat recovery ventilation system had an optimal allocation ratio between heat exchangers and an optimal brine flow rate that provided maximum heat recovery efficiency. These authors found the optimum working condition for the networks but did not explain the phys-