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气田集输管网是气田建设过程中一个投资巨大的复杂工程,如果能够对其进行整体优化,将取得良好的经济效益和社会效益。气田集输管网的优化设计,即寻求站址、管网布局以及管径、壁厚等工艺参数的合理分配,属于NP难点问题。文章通过分级优化的方法,在采用kruskal算法确定管网最优布局的基础上,提出结合遗传蚁群算法的优化参数方案,以集输管网干线的最小造价为目标函数,管径和壁厚作为优化变量,建立符合实际工程的数学模型。该模型根据集输管网所处的复杂环境,确定了流量连续性,管道规格,节点压力等一系列约束方程。根据模型的结构特点,在遗传蚁群算法的求解过程中,给出了符合实际数据的染色体选择、交叉、变异方式,并且在最佳时刻通过遗传算法与蚁群算法的衔接,将两种算法进行融合,形成了一种时间效率和求解效率都比较好的启发式算法。仿真计算表明,应用遗传蚁群算法的设计方案在求解速度和求解精度上都明显优于单一的遗传算法或蚁群算法,更加节省管网的投资费用。
Gas gathering and transportation network is a huge investment in the construction of gas fields in complex projects, if the overall optimization can achieve good economic and social benefits. The optimization design of gas gathering and transporting pipe network, that is to seek site, pipe layout and reasonable distribution of process parameters such as pipe diameter and wall thickness, is an NP difficult problem. Based on the kruskal algorithm to determine the optimal layout of pipe network, the article puts forward the optimization parameter scheme based on genetic ant colony algorithm through the hierarchical optimization method. Taking the minimum cost of the pipeline network as the objective function, the pipe diameter and wall thickness As an optimization variable, a mathematical model that matches the actual project is established. According to the complex environment in which the pipeline network is located, the model determines a series of constraint equations such as flow continuity, pipeline specifications and node pressure. According to the structural characteristics of the model, the genetic selection, crossover and mutation of chromosomes are given in the process of solving genetic ant colony algorithm. At the optimal time, genetic algorithm and ant colony algorithm are used to connect the two algorithms To form a heuristic algorithm with good time efficiency and solution efficiency. The simulation results show that the design scheme of genetic ant colony algorithm is better than single genetic algorithm or ant colony algorithm in terms of solving speed and solving precision, which saves more investment cost of pipe network.