基于最小路径覆盖算法的化工过程重要参数的识别
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摘要
将复杂网络的目标控制理论应用到化工过程系统重要参数的识别中,以SDG(signed directed graph)模型和复杂网络理论为基础构建化工过程的网络模型,然后利用LeaderRank和节点相似度算法(SRank算法)对网络节点重要性进行排序并基于此对网络进行鲁棒性分析选取目标节点,通过最小路径覆盖算法对网络进行目标控制分析,确定驱动节点并对它们进行重点监控。案例分析结果表明,该方法可行,对化工过程系统中重要参数的监测和安全控制具有一定的指导意义。
This paper proposes the application of complex network target control theory in the identification of important parameters in chemical process system.The network model of chemical process is built on the basis of SDG(signed directed graph) model and complex network theory,then the importance of network nodes is sorted by LeaderRank and node similarity algorithm(SRank algorithm).Based on this,the robustness of the network is analyzed and the target node is selected.The target control analysis is carried out by the minimum path coverage algorithm and the driving nodes are confirmed and monitored.Case studies show that this method is feasible and has some guiding significance for monitoring and safety control of important parameters in chemical process system.
This paper proposes the application of complex network target control theory in the identification of important parameters in chemical process system.The network model of chemical process is built on the basis of SDG(signed directed graph) model and complex network theory,then the importance of network nodes is sorted by LeaderRank and node similarity algorithm(SRank algorithm).Based on this,the robustness of the network is analyzed and the target node is selected.The target control analysis is carried out by the minimum path coverage algorithm and the driving nodes are confirmed and monitored.Case studies show that this method is feasible and has some guiding significance for monitoring and safety control of important parameters in chemical process system.
引文
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[13]罗筱如.基于复杂网络理论的电力网络鲁棒性及脆弱性分析[D].成都:西南交通大学,2012.
[14]徐翔斌,李恒.基于供需能力的供应链网络鲁棒性[J].系统工程,2015,(8):17-23.
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[17]姜英.基于复杂网络的化工过程层次SDG故障传播特性的研究[D].青岛:青岛科技大学,2018.
[2]Cai E,Dan L,Ling L,et al. Monitoring of chemical industrial processes using integrated complex network theory with PCA[J].Chemometrics&Intelligent Laboratory Systems,2015,140(27):22-35.
[3]姜英,王政,秦艳,等.基于复杂网络的化工过程层次符号有向图模型建立及关键节点识别[J].化工进展,2018,37(2):444-451.
[4]Gao J,Liu Y Y,D'souza R M.Target control of complex networks[J].Nature Communications,2014,5:5415-5423.
[5]Zhang X,Wang H,Lv T.Efficient target control of complex networks based on preferential matching[J]. Plos One,2017,12(4):e0175375.
[6]薛等长.基于最小路径覆盖的复杂网络目标控制的研究[D].西安:西安电子科技大学,2015.
[7]Sharma A,Cinti C,Capobinaco E.Multitype network-guided target controllability in phenotypically characterized osteosarcoma:Role of tumor microenvironment[J]. Frontiers in Immunology,2017,8:918-936.
[8]Zhang X F,Ou-Yang L,Zhu Y,et al.Determining minimum set of driver nodes in protein-protein interaction networks[J].Bmc Bioinformatics,2015,16(1):146-159.
[9]Kanhaiya K,Czeizler E,Gratie C,et al.Controlling directed protein interaction networks in cancer[J]. Sci Rep,2017,7(1):1155-1167.
[10]Galbiati,Marco,Delpini,et al. The power to control[J]. Nature Physics,2013,9(3):126-128.
[11]陆靖桥,傅秀芬,蒙在桥.复杂网络的鲁棒性与中心性指标的研究[J].计算机应用与软件,2016,33(4):302-305.
[12]鲍登,高超,张自力.基于复杂网络的公交-地铁复合网络鲁棒性分析[J].西南师范大学学报,2017,42(5):22-27.
[13]罗筱如.基于复杂网络理论的电力网络鲁棒性及脆弱性分析[D].成都:西南交通大学,2012.
[14]徐翔斌,李恒.基于供需能力的供应链网络鲁棒性[J].系统工程,2015,(8):17-23.
[15]张贝克,许欣,高东,等.基于定性趋势与符号有向图的模型校核方法[J].化工学报,2013,64(12):4536-4543.
[16]顾亦然,朱梓嫣.基于LeaderRank和节点相似度的复杂网络重要节点排序算法[J].电子科技大学学报,2017,46(2):441-448.
[17]姜英.基于复杂网络的化工过程层次SDG故障传播特性的研究[D].青岛:青岛科技大学,2018.