基于鱼骨图-博弈云模型的天然气管道风险评价

Risk evaluation method for natural gas pipeline based on fishbone diagram-game cloud modeling

  • 摘要: 天然气管道风险评价具有模糊性与不确定性,现有方法尚未实现定性概念与定量描述的有效融合,在风险指标选择与权重确定方面缺乏统一标准,亟须一种系统性的评价方法。以国内外天然气管道事故统计数据为基础,运用鱼骨图法识别并构建了第三方破坏、腐蚀、环境灾害、误操作、管道缺陷、运行故障6方面共30项影响因素作为评价指标。基于博弈论赋权法,结合G1法与改进CRITIC法计算的主客观权重进行综合赋权;利用Matlab确定云模型的特征参数并生成云图,通过正向云发生器确定不同风险等级下各指标的隶属度与综合隶属度,并进行等级评判。本文所评价支线4处管段分别为中等、低、低、较低风险,与现场实际情况相吻合,为天然气管道风险管理提供理论指导。同时,该模型有效降低了决策者的主观不确定性,并消除了数据间的客观误差,具有较强的实用性和可靠性。

     

    Abstract: Risk evaluation of natural gas pipelines is ambiguous and uncertain; however, the existing methods couldn't realize the effective integration of qualitative concepts and quantitative descriptions, lacking uniform standards in the selection of risk indicators and the determination of weights, which urgently requires a systematic evaluation method. Based on the domestic and international natural gas pipeline accident statistics, 30 influencing factors in 6 aspects, including third-party damage, corrosion, environmental disasters, dis-operation, pipeline defects, and operational failures, were identified and constructed as evaluation indexes using a fishbone diagram method. Employing the game theory assignment method, the subjective and objective weights calculated by combining the G1 and improved CRITIC methods were comprehensively assigned. Matlab was used to determine the characteristic parameters of the cloud model and generate the cloud diagrams, and the affiliation degree and the comprehensive affiliation degree of each index under different risk levels were determined by the forward cloud generator and the level judgment was conducted. The four pipeline sections of the evaluated branch line were respectively medium, low, low, and lower risk, which coincide with the actual situation in the field and provide theoretical guidance for natural gas pipeline risk assessment. Results show that the proposed model effectively reduces the subjective uncertainty of the decision maker and eliminates the objective error between the data, performing highly practical and reliable.

     

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