Research based on double coverage rate and reliability of gas detector layout optimization

The gas detector layout should be highly attuned to combustible gas leakage and attain a good reliability in avoiding detector malfunction, which is an important guarantee for the normal production of the chemical industry and other related enterprises. Herein, a gas detector layout optimization method based on double coverage and reliability is proposed. The key gas leakage monitoring area is determined through layout scene field investigation. To improve the detection probability and detection system reliability, the dual coverage target and voting mechanism are set, and the gas detector layout is determined with the ray-casting algorithm according to the coverage target. Combined with FLACS software to simulate a variety of typical leakage conditions under different layout scenarios, the relationship between the leaked gas concentration detected by gas detectors in each layout scheme and time is obtained, and the gas leakage detection probability in each layout scheme, number of detectors that can trigger the alarm, shortest time to trigger the alarm and reliability are comprehensively evaluated. The decision-maker selects the final gas detector layout plan according to the evaluation results and actual site needs. The study shows that the detection probability of each layout scheme set according to the double coverage is high, and multiple detectors can trigger the alarm (up to 100%), which ensures that the alarm can be triggered within 10 s under all applicable conditions. According to the evaluation results, the decision-maker can obtain a layout scheme that not only agrees with the actual site conditions but also attains a high detection probability, short detection time and strong reliability.     

Optimal gas detector placement under uncertainty considering Conditional-Value-at-Risk

A stochastic programming formulation considering Conditional-Value-at-Risk (CVaR) is developed for the optimal placement of gas detectors in petrochemical process facilities. A rigorous gas dispersion simulator, FLACS, is used to generate release scenario data for a real process geometry. We consider two problem formulations: minimization of expected detection time and minimization of expected detection time subject to a restriction on CVaR across the scenario set. The extensive form of each stochastic program is formulated in Pyomo and solved using CPLEX. Considering all scenarios, we compare key values and histograms of detection times for both formulations. Minimizing the mean detection time only can lead to optimal detector placements with a good expected behavior, but unacceptable worst-case behavior. The formulations that minimize or constraint CVaR produce sensor placements with significantly better worst-case behavior and fewer scenarios having high detection times. Considering these results, a strong case for the use of optimal sensor placement using stochastic programming considering CVaR is made for improving safety systems.     

高硫炼油装置硫化氢泄漏场景集定量分析

随机规划方法已成为解决不确定条件下高硫炼油装置气体检测报警仪布置优化问题的重要途径,而构建接近真实情况的泄漏场景集则是实现随机规划的基础。目前,有毒气体泄漏场景集构建方面的研究鲜有报道,且传统以典型泄漏场景为代表的做法未能体现真实风险。为此给出一种融合泄漏概率和风场联合分布概率的定量构建硫化氢泄漏场景集的方法,使场景集包括重要泄漏场景并运用DNV的LEAK软件和历史气象数据定量预测场景的实现概率,为实现后续的气体泄漏检测报警仪布置随机规划提供技术支持。并以某柴油加氢装置为例,定量构建其硫化氢泄漏场景集,为后续的气体检测报警仪布置优化及其它基于定量风险分析的控制决策提供支持。     

A dynamic approach for evaluating the consequences of toxic gas dispersion in the chemical plants using CFD and evacuation modelling

Accidental releases of toxic gas in the chemical plants have caused significant harm to the exposed occupants. To evaluate the consequences of these accidents, a dynamic approach considering the gas dispersion and behavior evacuation modelling has been proposed in this paper. This approach is applied to a hypothetical scenario including an accidental chlorine release in a chemical plant. CFD technique is utilized to calculate the time-varying concentration filed and evacuation modelling is used to obtain the evacuation routes. The exposure concentrations in the evacuation routes are calculated by using the code of data query. The integrated concentration toxic load model and probit model are used to calculate the probability of mortality of each occupant by using the exposure concentrations. Based on this dynamic approach, a new concept of average probability of mortality (APM) has been proposed to quantify the consequences of different accidental scenarios. The results show that APM decreases when the required detection time decreases or emergency evacuation mode is implemented. The impact of the detection time on APM becomes small as the wind speed increases. The effect of emergency evacuation mode is more obvious when the release occurs in an outdoor space.     

考虑人因可靠性的安全仪表功能SIL验证方法研究

油气站场一般设置有紧急停车系统（ESD）等存在操作员介入的非常规安全仪表功能（SIF），为解决已有的安全完整性等级（SIL）评估方法不能针对此类SIF进行功能安全评价的情况。对存在操作员介入的非常规SIF进行研究，将其中的人为因素细分为观察、决策和执行3个阶段；根据各类人因可靠性分析方法优缺点，筛选CREAM和HCR方法分别分析紧急情景环境和应急响应时间对非常规SIF人因失效概率的影响，建立考虑人因可靠性的SIL验证模型；基于此模型选取某输油站典型SIF开展SIL评估，分析人因失效对SIF整体可靠性的影响水平，并提出改善措施。结果表明:将操作员应急响应过程中的人因失效概率引入传统的SIL验证模型中，可实现对非常规SIF的功能安全评价；人因失效对非常规SIF具有显著影响，筛选的人因可靠性模型可准确计算人因失效概率。     

Integration of accident scenario generation and multiobjective optimization for safety-cost decision making in chemical processes

Investment in Chemical Process Industries for improving their safety requires considering risk level, environmental effect, cost and many other aspects simultaneously. This paper focuses on a new systematic method of finding the most cost–risk–effective investment scenario set. The method uses the automatic accident scenario generation technique first to find a set of the most dangerous scenarios. Then it uses the multiobjective optimization method to decide the priority of the investment. These computations includes considering many constraints such as limited budget, environmental requirements and social demands. The Styrene Monomer plant case study proves the practical use of this integration method of accident scenario generation and multiobjective optimization.     

A new approach for facility siting using mapping risks on a plant grid area and optimization

The present work is focused on developing a methodology to find the optimal placement of a hazardous process unit and other facilities using optimization theory while considering a risk map in the plant area. Incidents can have possible consequences resulting from flammable gas releases, which can be evaluated by using consequence modeling programs. The probability of each incident can be derived from initial leak hole size estimation through event tree analysis. In this methodology the plant area was divided into square grids and risk scores were estimated for each grid. The overall cost is a function of the probable cost of property damage due to fires or explosions and the interconnection cost including piping, cable, and management. The proposed approach uses a mixed integer linear optimization programming (MILP) that identifies attractive locations by minimizing the overall cost. A case study is presented for a hexane–heptane separation facility that considers the meteorological data for the given area in order to show the applicability of the proposed methodology. Results from this study will be useful in assisting the selection of locations for facilities and for risk management.     

Integrating flare gas with cogeneration systems: Operational risk assessment

Flare gas utilization in a cogeneration plant is an attractive proposition considering its environmental and economic incentives. Evaluation of the operational risk of integrating flare gas with cogeneration is complex due to the uncertainty in flare gas quality and process conditions. The current study delves into the change in operational risk after modifying the existing cogeneration process with the addition of fuel from flare gas. Based on the process hazards evaluation, the current study identified two critical loss control events (or top events) - boiler gas temperature exceeding operating design temperature and rich fuel mixture in the boiler firebox. The underlying causes that may contribute and lead to these loss control events were identified using fault trees and were updated to the existing cogeneration scenarios. Similarly, different consequential events that may arise from the loss control events were analysed using event trees with existing system safeguards. A Bayesian network model with its explanatory power mapped all the identified dangerous scenarios from the fault trees and event trees to predict integrated systems reliability and diagnose causal factors. Bayesian Network analysis illustrates the dynamic cause-effect relationship and determines the risk escalation due to the changes in the composition of flare gas that is fed to the boiler. The presence of a higher percentage of hydrogen (above 40 mol%) in the flare gas escalates the risk of lean air to fuel ratio in the boiler firebox and increases boiler radiation zone duty. These conditions are detrimental to the boiler firebox operation and can result in critical scenarios such as flame impingement and tube rupture. Additionally, other consequences-a steam explosion and boiler stack explosion were also investigated. However, their probability of occurrence was relatively insignificant with the given frequency of flare gas utilization in the cogeneration system.     

A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

采用遗传算法优化气体探测器布局策略研究

石油石化装置具有结构复杂且危险性高的特点,所加工物料多为易燃易爆有毒物质,且工艺单元之间集成度高,一旦发生泄漏若无法及时探测到则易形成气液积聚和火灾爆炸后果强化,装置拥塞度高使人员逃生困难。火气系统FGS作为安全关键系统,其中的气体探测网络如何快速可靠的实现对气体泄漏事件的探测显得尤为重要。已知探测时间,通过引入遗传算法利用其全局搜索的特点克服传统分支定界法的缺点,实现立体空间不同高度下设置探测网络达到场景全覆盖和缩短探测时间,同时求出探测时间附近的多组最优解,为探测器放置提供多种布置方案。通过与传统等间距探测器布置方案比较,从多种布置方案中选择更符合实际的最佳方案。通过海上浮式生产储油船的生产夹板气体探测案例,验证了所提方法的有效性。     

Worst-case identification of gas dispersion for gas detector mapping using dispersion modeling

To quickly and accurately quantify the material release in process units, gas detectors may be placed according to the results of gas dispersion modeling. DNV's PHAST software is one of the most useful and reliable tools for material dispersion modeling. In this software, fluid dispersion is modeled based on the process conditions, the weather conditions and the specifications of the material release point. However, varying weather conditions throughout the year and the exact determination of the release point on the plot plan and the release elevation are problematic; these issues cause the results to be non-exact and non-integrated. Choosing the most appropriate conditions is challenging. In this paper, a scheme was provided to select the most appropriate conditions for gas dispersion modeling. This scheme approaches modeling based on the worst-case scenario (the situation in which the dispersed gas reaches the detector later in comparison to the other cases). Therefore, different weather conditions, release elevations and release points on the plot plan were modeled for an absorber tower of the Gonbadli Dehydration Unit of the Khangiran Refinery. The worst case of each release condition was then chosen. Finally, gas detectors were placed using the gas dispersion modeling results based on the worst-case scenario.     

基于MTBF的光电感烟探测器可靠性研究

针对火灾探测报警系统重要组成模块—光电感烟探测器-报警控制器,根据其在实际使用过程中的不完全故障数据,改良了前人对该问题的传统研究方法,并提出了分段定时截尾可靠性参数模型。该模型主要考虑火灾探测报警系统控制器工作时序性和故障处理时间的影响,采用自主提出的原始故障数据筛选统计方法,分别求得光电感烟模块的首次使用寿命与平均故障间隔时间的参数估计。该方法能够减少火灾探测报警系统初始使用寿命过长而产生的平均故障间隔时间的估计偏差,对于火灾探测报警系统整体使用寿命的估计和维修管理有着重要意义。同时基于序贯思想的动态截尾也克服了定时截尾模型无法准确判断试验停止时间的不足,在保证参数较为精确的前提下最大限度地节省时间和资源。     

可燃气体报警器快速校准装置的研究

针对石化企业中可燃气体报警器校准工作存在着校准周期长、维护困难、准确性不能保证的现状,提出了一种通过将高浓度可燃气体物质经过定量稀释配制成用于可燃气体报警器标定的多种浓度气体物质的方法,并设计了一种由两路流量检测与控制模块和一个监控单元构成的可燃气体报警器快速标定装置。叙述了可燃气体报警器快速标定装置的原理、元器件构成、设计结构、流量控制方法和防爆措施的实现。     

Mitigation of operational failures via an economic framework of reliability,availability, and maintainability (RAM) during conceptual design

Abnormal process situation may lead to tremendous negative impact on sustainability, wellbeing of workers and adjacent communities, company's profit, and stability of supply chains. Failure of equipment and process subsystems are among the primary causes of abnormal situations. The conventional approach in handling failure-based abnormal situations has usually focused on operational strategies. Such an approach overlooks the critical role of process design in mitigating failure, while simultaneously considering the effects of such failure on process economic performance. The aim of this work is to introduce a systematic methodology that accounts for failure early enough during the conceptual design stages. Once a base-case design is developed, the methodology starts by identifying the sources of failure that are caused by reliability issues including equipment, operational procedures, and human errors for a given process system or subsystem. This allows for the identification of critical process subsystem(s) that are more failure-prone or cause greater downtime than other subsystems. Bayesian updating and Monte Carlo techniques are utilized to determine the appropriate distributions for the failure and repair scenario(s), respectively, in question. Markov analysis is used to determine the system availability. Next, the process revenue is described as a function of inherent availability. The effects of failures are incorporated into profitability calculations to establish an economic framework for trading off failure and profitability. In the proposed framework, the economic potential of alternative design scenarios is evaluated and an optimization formulation with the objective of maximizing incremental return on investment (IROI) is utilized to make a design decision. A case study on an ethylene plant is solved to demonstrate the applicability and value of the proposed approach.     

基于BP神经网络的红外可燃气体探测器温度补偿研究

针对红外可燃气体探测器输出电压随温度变化,而又缺乏理论补偿公式的情况,提出采用BP神经网络对基于NDIR的可燃气体探测器进行温度补偿的方法,解决了以往温度补偿通过硬件补偿带来的探测器体积大、重量增加的弱点;与已有的软件补偿方法相比,BP神经网络方法通过对数据进行训练、学习获得温度补偿模型,得到了较好的结果,使得红外可燃气体探测器具有较宽的使用温度范围。     

Mechanism analysis and risk assessment of escalation scenario in chemical industry zones

Many major hazard installations (MHIs) are located in chemical industry zones and escalation effect may be triggered when the fire or explosion occurs on a MHI. To investigate the mechanism of the accident escalation, a systematic quantitative assessment methodology is proposed by the considering the feature and uncertainty of the escalation scenario. The main accident energy carriers of the escalation are heat radiation, overpressure of blast and fragments. The escalation probability, joint influence of the three energy carriers and risk characterization of the accident scenarios are carried out. By the new methodology, the escalation scenario in chemical industry zones can be analyzed and the risk escalation morphology is demonstrated by the simulation software. The visualized risk cloud figure gives a supplementary way to prevent the escalation scenario in chemical industry zones planning.     

基于广义概念的城市燃气管道泄漏精确定位

为了准确地检测城市燃气管道泄漏,提出了一种基于广义概念的管道泄漏检测定位方法。声发射技术对于管道泄漏的检测、定位是一个极好的工具,但由于泄漏源的传播容易受到周围背景噪声以及复杂工况的影响,其定位误差较大。基于时延估计的互相关信号处理方法被广泛用于管道泄漏检测定位,但由于泄漏应力波传播通道的动态特性,使得源信号在传播过程中会产生波形变化,给互相关函数峰值位置的确定带来困难。由此引入广义相关分析方法,通过对信号进行前置滤波,在一定程度上减少了传播通道动态特性因素对泄漏点定位的不利影响,得到了更为准确的时延估值。在此基础上,通过模拟实验,编写Matlab神经网络代码,构造GRNN模型,进一步预测定位。结果表明,GRNN预测的声发射检测值、互相关定位值以及广义相关定位值,相比之前定位精度分别得到提高,其中基于广义相关的延时估计方法定位最为精确,将该方法用于工程实际中,可以更加精确地定位出泄漏点。     

Leak location of pipelines based on characteristic entropy

The leakage of oil/gas pipelines is one of the major factors to influence the safe operation of pipelines. So it is significant to detect and locate the exact pipeline leakage. A novel leak location method based on characteristic entropy is proposed to extract the input feature vectors. In this approach, the combination of wavelet packet and information entropy is called “wavelet packet characteristic entropy” (WP-CE). The combination of empirical mode decomposition and information entropy is called “empirical mode decomposition characteristic entropy” (EMD-CE). Both pressure signal and flow signal of low noise and high noise of pipeline leakage are decomposed to extract the characteristic entropy. The location of pipeline leak is determined by the combination of the characteristic entropy as the input vector and particle swarm optimization and support vector machine method (PSO-SVM). The results of proposed leak location method are compared with those of PSO-SVM based on physical parameters. Under the condition of high noise, the results of proposed leak location method are better than those of PSO-SVM based on physical parameters.