关键词: 性能模拟, 换热网络, 神经网络, 遗传算法, 优化

Abstract:

Finding cost effective retrofit for heat exchanger networks remains a challenge. Traditional retrofit methods rely heavily on topological modifications, which often increase retrofit time and investment costs. Therefore, this paper focuses on the heat exchanger network retrofit with a fixed network structure, and a data-driven efficient optimization method was proposed. A performance simulation model was established to simulate the utility consumption and temperature distribution of a heat exchanger under different performance parameters, so as to obtain a large number of data for the optimization of the heat exchanger network. Then back propagation neural network prediction model and genetic algorithm were used to solve the retrofit problem with the goal of maximum energy savings. Case studies showed that the retrofitted heat exchanger network can obtain greater energy-saving benefits through smaller investment. Compared with the literature, the energy savings per unit cost increased by 51.4%. A modification scheme that cannot be obtained by the empirical rule method can be quickly obtained compared with the sensitivity analysis of the same structure. Moreover, the effectiveness and economic practicability of the proposed method were demonstrated.

Key words: performance simulation, heat exchanger network, neural network, genetic algorithm, optimization