基于群体智能算法的换热网络同步最优综合

A hybrid swarm intelligence algorithm for simultaneous synthesis of heat exchanger network

换热网络同步综合方法一般需要建立复杂的混合整数非线性数学规划模型,该模型具有非凸、非线、不连续的特点,属于最难求解的一类NP-hard问题,应用传统的优化算法很难确定其全局最优解,尤其是对大规模换热网络综合问题,甚至无法在合理时间内接近全局最优的局部最优解。针对换热网络同步综合问题,提出基于群体智能算法的分层优化策略,外层采用离散粒子群算法与遗传算法相结合的混合群体智能算法优化换热网络结构,内层在结构变量给定条件下利用改进粒子群算法优化冷热物流分流比与换热负荷。两个典型算例研究证明了该方法能以较高的效率和稳定性得到较好的优化结果。

Heat exchanger network is an important energy recovery system in chemical processes. Recently, simultaneous synthesis methods have been frequently applied to designing a costoptimal heat exchanger network. Simultaneous synthesis problem is usually formulated as a mixed-integer non-linear programming model, which is nonconvex, nonlinearity, non-continuous, and belongs to one of the toughest nondeterministic polynomial-time hard （NP-hard） problems. Medium or largescale simultaneous synthesis problems in many cases cannot be solved in a reasonable time. A two-level approach was proposed for solving HENS problem. A hybrid methodology consisting of binary particle swarm optimization and genetic algorithm was utilized to generate the network structure in the upper level, while in the lower level, the heat load of exchangers and splitstream fractions were optimized by an improved particle swarm optimization. Two benchmark problems were solved to prove the efficiency of the proposed method.