化工进展 ›› 2018, Vol. 37 ›› Issue (03): 867-874.DOI: 10.16085/j.issn.1000-6613.2017-1122

• 化工过程与装备 • 上一篇    下一篇

失效情景下气体探测器多目标布置优化

章博1,2, 赵日彬1   

  1. 1 中国石油大学(华东)机电工程学院, 山东 青岛 266580;
    2 中国石油大学(华东)海洋油气装备与安全技术 研究中心, 山东 青岛 266580
  • 收稿日期:2017-06-08 修回日期:2017-09-12 出版日期:2018-03-05 发布日期:2018-03-05
  • 通讯作者: 章博(1980-),男,博士,副教授,硕士生导师,研究方向为油气安全工程。
  • 基金资助:
    山东省自然科学基金(ZR2016EEM27)及中央高校基本科研业务费专项资金(18CX05028A)资助。

Multi-objective optimization for placement of gas detectors considering failure scenario

ZHANG Bo1,2, ZHAO Ribin1   

  1. 1 College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, Shandong, China;
    2 Center for Offshore Equipment and Safety Technology, China University of Petroleum, Qingdao 266580, Shandong, China
  • Received:2017-06-08 Revised:2017-09-12 Online:2018-03-05 Published:2018-03-05

摘要: 目前有关气体探测器布置优化研究较少考虑探测器的失效情景,本文以某柴油加氢装置的硫化氢气体探测器布置优化为例,提出一种失效情景下气体探测器多目标布置优化方法。首先对待检测区域的潜在泄漏源进行辨识,构建泄漏场景集并进行场景缩减,通过计算流体力学方法预测泄漏实时浓度场。其次,以时效性和鲁棒性作为评价指标,以考虑泄漏场景概率和探测器失效概率的检测时间最小化、探测器网络鲁棒性最大化作为优化目标函数,并结合逻辑约束条件建立数学模型。在此基础上,采用基于模拟退火的多目标粒子群算法对模型进行求解,得到Pareto非劣解集,采用理想点逼近法(TOPSIS)对Pareto解集进行排序得到最优方案,最后决策者可根据不同需求确定最终布置方案。

关键词: 失效情景, 探测器, 多目标, 优化设计, 计算流体力学

Abstract: At present,the study of optimization for gas detector placement is less concerned with the detector failure scenarios. In this paper,the optimization of hydrogen sulfide gas detector in a diesel hydrogenation unit was taken as an example. An optimization method for the placement of gas detectors under failure scenario was proposed. The preparation for the optimization includes identification of the potential release sources in the target area,establishment of the leakage scene set,reduction of the leakage scene used by clustering algorithm,and prediction of the dispersion real-time concentration field by computational fluid dynamics simulation. Both timeliness and robustness were taken into account as the evaluation index during the optimization. The minimization of detection time considering leakage scene probability,the detector failure probability,and the maximization of robustness of detector network were chosen as optimization goals, which combined logical constraints to establish mathematical programming model. A Pareto solution can be obtained by SA-PSO(particle swarm optimization algorithm based on simulated annealing) and TOPSIS(technique for order preference by similarity to an ideal solution) method. Finally,the final placement scheme can be determined by the different needs of the decision maker.

Key words: failure scenario, detectors, multi-objectives, optimization design, computational fluid dynamics

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