%0 Journal Article
%T 星状集输管网拓扑结构的整体优化
%A 刘刚
%A 许继凯
%A 国志刚
%A 陈雷
%A 卢兴国
%A 滕厚兴
%A 徐睿妤
%J 中国石油大学学报(自然科学版)
%D 2016
%R 10.3969/j.issn.1673-5005.2016.04.018
%X 针对星状集输管网结构特点,建立以管网建设总投资为目标函数,以系统中节点连接关系、站点位置、管线参数为优化变量的星状油气集输管网拓扑结构优化模型。将蚁群算法与遗传算法相结合对模型进行整体优化求解。在蚁群算法中,将节点连接关系的确定转化为路径选择问题,将启发因子表示为管段建设成本的函数,用路径方案对应的管网建设总成本计算信息素的积累量。在遗传算法中,以格雷码形式将站址信息储存于染色体上,用蚁群算法求得每种站址分布方案下最优井组和管径,并用其计算各染色体的适应度,从而同步求得最佳站址、最佳井组划分和管线参数。结果表明,所设计算法优化质量高于分级优化,且鲁棒性强,不受计算初始值影响。</br>The topological structure optimization model of radial patter oil and gas gathering pipe network was built according to its structural characteristics, with the total construction cost of the pipe network as the objective function, and the connection relation of nodes, pipeline parameters and location of stations as the optimization variables. To avoid the deficiency of multilevel optimization, ant colony algorithm and genetic algorithm were combined to solve the optimization model globally. In ant colony algorithm, the determination of connection relation was converted to the routing problem, heuristic factor was expressed as the function of pipe construction cost, and the total construction cost of pipe network corresponding to the routing scheme was used to calculate the pheromone accumulation. In genetic algorithm, the information of station location was stored in chromosomes using gray code, and the well-group scheme and pipe diameters were obtained by ant colony algorithm and were used to calculate the fitness of each chromosome. Meanwhile, the optimal station location, optimal well-group and pipeline parameters were also obtained. The above algorithm was applied to the optimum calculation of the specific gathering pipeline networks in some oil fields. The results show that the global optimization algorithm has better optimum quality and stronger robustness than multilevel optimization, and the optimum results are not affected by initial value
%K 集输管网 拓扑结构 分步优化 整体优化 蚁群算法 遗传算法&lt
%K /br&gt
%K gathering pipe network topological structure multilevel optimization global optimization ant colony algorithm genetic algorithm
%U http://zkjournal.upc.edu.cn/zgsydxxb/ch/reader/view_abstract.aspx?file_no=20160418&flag=1