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Optimal heat exchanger network synthesis using particle swarm optimization

机译：基于粒子群算法的最优换热网络综合

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Heat exchanger network (HEN) synthesis has been a well-studied subject over the past decades. Many studies and methodologies were proposed to make possible the energy recovery, minimizing the utilities consumption and the number of heat transfer equipment.rnMost of papers published in this subject are based on Pinch Analysis and mathematical programming. Some recent papers use meta-heuristic techniques like Genetic Algorithms or Simulated Annealing to solve the HEN synthesis problem and good results are found but with large computational effort.rnIn this paper an optimization model for the synthesis of HEN is proposed. The approach is based on the use of Particle Swarm Optimization to determine the HEN that minimizes the total annualized cost, accounting for capital costs of heat exchangers and the energy costs for utilities and pumping duties. The algorithm is based on a superstructure simultaneous optimization model for the HEN synthesis considering stream splitting. Some examples from the literature were used to show the application of the proposed algorithm, and the results confirm the achievement of the optimum HEN configuration with little computational effort.
机译：在过去的几十年中，热交换器网络（HEN）的合成一直是一个经过深入研究的主题。提出了许多研究和方法以实现能量回收，最大程度地减少公用事业消耗和传热设备的数量。该主题中发表的大多数论文都基于捏分析和数学程序设计。最近的一些论文使用遗传算法或模拟退火等元启发式技术解决了HEN合成问题，虽然获得了很好的结果，但计算量却很大。本文提出了一种HEN合成的优化模型。该方法基于粒子群优化的使用，确定了将总年度成本降至最低的HEN，并考虑了热交换器的资本成本以及公用事业和抽水税的能源成本。该算法基于考虑流分割的HEN合成的上层结构同时优化模型。文献中的一些例子被用来说明所提出的算法的应用，并且结果证实了最佳的HEN配置的实现，而很少的计算工作。

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