用改进的遗传算法进行多流股换热器网络综合

建立了有分流情况下换热器网络综合的教学模型，该模型改进了文献中等温混合的不合理假设，有分流情况下的换热器网络综合问题本质上是一个混合整数非线性规划(MINLP)问题。这类问题的非凸非线性特性使得目标函数存在多个局部最优解，传统的基于梯度的搜索方法在处理这类问题时由于计算规模庞大且极易陷于局部最优解而不再适用，因此对遗传算法进行多流股换热器网络综合的方法进行了研究，最后通过例题证明了方法的有效性。

Multi-stream heat exchanger networks synthesis with improved genetic algorithm

The multi-stream heat exchanger networks synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming (MINLP) model according to Yee, et al.. Its nonconvexity nature leads to the existence of more than one optimum and the computation difficulty for traditional algorithms to find the global optimum. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee, et al., non-isothermal mixing is supported in this paper. As a consequence, nonlinear constraints are approached and nonconvexity of the objective function is also added. Compared with other deterministic algorithms, genetic algorithms (GAs) can provide a promising approach to solution of this problem. A novel method for the synthesis of multi-stream heat exchanger networks based on GA using the stage-wise superstructure is presented. The performance of the proposed GA approach is demonstrated by two examples and better solutions obtained are compared with literatures.