Systematic inherent safety and its implementation in chlorine liquefaction process

As a proactive safeguard, inherent safety has been regarded as the top hierarchy for loss prevention and risk management due to its salient features in eliminating or significantly reducing risks at source rather than mitigating them by add-on protections. Simultaneously, various assessment tools have been developed for ranking and selecting inherently safer designs or modifications. However, there still lacks a metric that can systematically incorporate various hazardous factors, which may hinder most industries from utilizing it to a full extent. To address this limitation, this work developed a Systematic Inherent Safety Metric (SISM) for measuring the inherently safer modifications. Firstly, the conceptual framework of SIS was proposed based on 5M1E (man, machine, material, method, measurement, and environment). Subsequently, analytic hierarchy process and fuzzy comprehensive evaluation were adapted to conduct risk identification and assessment. Finally, taking chlorine liquefaction process as a case study, the applicability and efficacy of SIS were validated based on PDCA (plan-do-check-action) cycle. The results show that the SISM value has improved from the relatively dangerous (RD) to the relatively safe (RS) after implementing SIS, thus demonstrating that the revised design is inherently safer than the base design.     

Bayesian networks based laboratory retrofitting towards inherent safety: A risk-based implementation framework

Over the years, a number of high-profile laboratory accidents involving severe injuries, fatalities, and economic losses have been reported, prompting a significant increase in efforts towards laboratory safety. However, the dominant safety measures rely excessively on add-on safeguards such as sprinklers and respirators and pay little attention to reducing the hazardous factors at their sources. This study introduced the inherent safety concept to minimize laboratory hazards and developed a dedicated implementation tool called Generic Laboratory Safety Metric (GLSM). The Traditional Laboratory Safety Checklist (TLSC) was first used to represent the safety indicators, and then the Precedence Chart (PC) and Bayesian Networks (BN) methods were used to reconcile the safety indicators to develop the GLSM. The developed GLSM was subsequently demonstrated through a case study of a university laboratory. The results revealed that the safety level increased from 2.44 to 3.52 after the risk-based inherently safer retrofitting, thus creating laboratory conditions with a relatively satisfactory safety level. This work presented a set of generic solutions to laboratory retrofitting towards inherent safety with a novel GLSM as the implementation tool. The proposed GLSM would contribute to risk quantification and identification of key risk factors for assigning targeted and fundamental safety measures to achieve inherently safer laboratories.     

Application of dynamic risk analysis in offshore drilling processes

Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences.     

基于模糊综合评价的化工工艺本质安全指数研究

建立化工工艺的本质安全评价指标体系,包括可燃性、爆炸性、毒性、反应性、温度、压力、储量等7个指标。为解决前人指数法中的边界波动效应,采用模糊综合评价建立化工工艺本质安全评价指数模型,确定各指标的权重、危险分级及对各分级的隶属函数。以甲基丙烯酸甲酯的5条工艺路线为例进行分析,结论与前人提出的指数方法的评价结果相吻合,表明该指数方法可以用来进行化工工艺的本质安全评价,以指导设计初期的本质安全型工艺路线选择。     

城市安全风险评估方法体系研究及实践

针对目前我国城市安全风险管控能力滞后于城市化发展进程的问题,开展城市安全风险评估是推动管控能力提升的重要手段。在国内没有统一的城市安全风险评估体系和标准情况下,研究提出以技术专家为支撑,政府、行业监管部门、企业3个层面协调推进为主体,试点先行、总结归纳、全面实施为路径的城市安全风险评估组织实施方式;总结提出以企业安全风险辨识评估为基础,采用层次分析法和综合模糊评价法进行叠加分析的城市安全风险评估方法体系;以贵州六盘水城市安全风险评估工作为实际应用,并基于相关评估成果研发城市安全风险管控系统和城市安全风险地图信息化平台,为我国进一步深化城市安全风险评估工作提供实践经验。     

A quantitative risk management framework for dust and hybrid mixture explosions

Dust and hybrid-mixture explosions continue to occur in industrial processes that handle fine powders and flammable gases. Considerable research is therefore conducted throughout the world with the objective of both preventing the occurrence and mitigating the consequences of such events. In the current work, research has been undertaken to help move the field of dust explosion prevention and mitigation from its current emphasis on hazards (with an accompanying reliance on primarily engineered safety features) to a focus on risk (with an accompanying reliance on hierarchical, risk-based, decision-making tools). Employing the principles of quantitative risk assessment (QRA) of dust and hybrid-mixture explosions, a methodological framework for the management of these risks has been developed.The QRA framework is based on hazard identification via credible accident scenarios for dust explosions, followed by probabilistic fault-tree analysis (using Relex – Reliability Excellence – software) and consequence severity analysis (using DESC – Dust Explosion Simulation Code – software). Identification of risk reduction measures in the framework is accomplished in a hierarchical manner by considering inherent safety measures, passive and active engineered devices, and procedural measures (in that order). An industrial case study is presented to show how inherent safety measures such as dust minimization and dust/process moderation can be helpful in reducing dust and hybrid-mixture explosion consequences in a 400-m³ polyethylene storage silo.     

Supporting the selection of process and plant design options by Inherent Safety KPIs

Effective support of inherent safety implementation in process design requires a quantitative metric for monitoring and communicating the expected safety performance of alternative design options. The Inherent Safety Key Performance Indicators (IS-KPIs) methodology was developed to provide both a flexible procedure for the identification of the hazards, and a sound consequence-based quantification of the safety performance. The integration of different hazard identification techniques yields the relevant accident scenarios for each unit in the plant. The calculation of credible damage distances by consolidate consequence simulation models provides a sound basis for the definition of the KPIs based on worst case effects. Specific indicators were devoted to hazards from external actions, as natural events and intentional malicious acts. The methodology was demonstrated by the comparison of alternative technological options for LNG regasification. The application evidenced the potential of the IS-KPI method in pinpointing the critical issues related to each alternative configuration.     

基于改进贝叶斯神经网络的航空公司安全敏感性分析

针对当下航空公司安全质量管理体系(Quality Management System,SQMS)中风险识别与可靠性改进的问题,提出了基于区间数学改进的贝叶斯神经网络的灵敏度分析方法。利用区间数学理论分析贝叶斯神经网络中各指标与整体安全质量状况的扰动关系,实现指标灵敏度分析。通过东方航空公司的实例分析,发现在对指标进行人工干预时组合指标干预效果较好,且安全管理体系实施后指标的灵敏度有明显向好的方向变化的趋势。     

北京市危险化学品企业安全生产风险评估分级研究

为对北京市危化品企业基本情况的全面掌握,开展了危险化学品企业安全生产风险评估分级研究,借鉴国外经验和国内有关科研成果,提出了固有风险和动态风险相结合的危化品安全生产风险评估方法。固有风险为企业的基本风险水平,主要由危险化学品物质量、工艺水平、安全监控和周边环境决定,为共性指标;动态风险,反映企业安全生产管理绩效的水平,主要包括安全基础管理和现场管理,为个性指标,不同企业类型,评估动态风险指标有差异。从大量数据中抽取了最能反应企业安全生产状态的指标因素,通过专家组打分法和层次分析法确定评价因素的权重,该评估方法具有较强的实用性和可操作性,是本质安全和安全绩效的创新结合,为企业安全生产评估分级提供了新思路。通过分级,按照"风险优先"原则,政府部门实行差别监管,降低安全监管成本,促进危险化学品安全生产工作向规范化、科学化转变。同时,研制了危化品企业安全生产风险分级系统,为建立北京市危化品企业风险分级数据库奠定了基础。     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

Fuzzy-AHP在LNG接收站风险辨识中的应用

笔者以液化天然气(LNG)接受站的生产工艺、设备、作业环境的特点为背景,提出一种基于模糊层次分析方法(Fuzzy-Analytic Hierarchy Process)的风险评估方法。首先,根据LNG接收站风险事故多层次、多因素及不确定的特点,结合本行业的安全标准,确立了LNG接受站的风险因素和评价指标,建立接受站的安全评价体系;随后,运用Fuzzy-AHP方法确定权重矢量和模糊评价矩阵,并通过模糊运算求出决策矢量,实现对风险因素的排序以达到风险辨识目的;最后,对国内某LNG站进行了实例计算。计算表明该接收站的硬件设施完善,但应加强风险管理,制定相应的措施应对因气候条件、储罐区管理不当及船舶安全保护不到位等引发的事故。结果表明基于LNG站的Fuzzy-AHP风险辨识模型的安全性综合评价是有效的。     

基于风险修正的城市安全风险评估方法及应用*

为推动实现城市安全风险的系统化、信息化管理,基于我国城市安全风险管理的要求与特点,总结城市安全风险评估的分层分类原则、突出固有风险原则和合理修正原则等基本原则;提出“点位-行业-区域”逐层展开的城市安全风险评估程序,明确各层级的评估要点;提出城市安全风险评估方法,分别制定点位、行业风险修正规则,结合风险参数与情景构建开展基于风险矩阵法的点位风险评估和行业风险评估,采用加权计算法由行业风险评估结果叠加评估区域风险。结果表明:该方法能够从不同角度有效评估城市的风险状态,为不同层级的城市安全管理者明确风险管控重点提供决策依据。     

Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities

Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.     

多因素耦合的加工车间关键风险因素定量分析

为了准确分析加工车间关键风险因素,考虑多风险因子交互影响的情境,提出了一种基于模糊决策实验室分析法的加工车间关键风险因素定量分析方法。首先,从人员、设备、环境、物料和管理因素等5个维度辨识加工车间风险因素并建立风险因素集;基于此,采用三角模糊数对专家判断语义进行表征,然后运用DEMATEL法计算多因素耦合下的风险重要度,从而确定加工车间的关键风险因素。最后,以安徽省某机械加工车间为例,说明所提出方法的应用过程。结果表明,加强对安全制度和安全文化建设等关键风险因素的控制,有助于加工车间的风险控制,改善车间生产安全。     

基于火灾场景的大型浮顶储罐区全过程风险防范体系研究

针对大型浮顶罐区火灾事故风险特征,通过对火灾场景辨识及其火灾风险影响因素分析,结合预防初期火灾场景风险要素和消防系统有效性,提出了罐区火灾事故现实风险的评估框架及确定方法。在此基础上,构建了基于火灾场景的全过程风险防范体系,包括库区评估规划、罐体本质安全化设计、库区火灾预防、罐区火灾扑救、园区火灾事故管理等5个方面,并给出相应的对策措施。大型浮顶罐区全过程火灾风险防范是一个持续改进的结构化过程风险管理体系,有助于实现罐区火灾风险的过程安全管理,进而提高仓储型园区的整体消防安全保障能力和风险防控水平。     

ExSys-LOPA for the chemical process industry

The chemical process industries are characterized by the use, processing, and storage of large amounts of dangerous chemical substances and/or energy. Among different missions of chemical plants there are two very important ones, which: 1. provide a safe work environment, 2. fully protect the environment. These important missions can be achieved only by design of adequate safeguards for identified process hazards. Layer of Protection Analysis (LOPA) can successfully answer this question. This technique is a simplified process of quantitative risk assessment, using the order of magnitude categories for initiating cause frequency, consequence severity, and the likelihood of failure of independent protection layers to analyze and assess the risk of particular accident scenarios. LOPA requires application of qualitative hazard evaluation methods to identify accident scenarios, including initiating causes and appropriate safeguards. This can be well fulfilled, e.g., by HAZOP Studies or What-If Analysis. However, those techniques require extensive experience, efforts by teams of experts as well as significant time commitments, especially for complex chemical process units. In order to simplify that process, this paper presents another strategy that is a combination of an expert system for accident scenario identification with subsequent application of LOPA. The concept is called ExSys-LOPA, which employs, prepared in advance, values from engineering databases for identification of loss events specific to the selected target process and subsequently a accident scenario barrier model developed as an input for LOPA. Such consistent rules for the identification of accident scenarios to be analyzed can facilitate and expedite the analysis and thereby incorporate many more scenarios and analyze those for adequacy of the safeguards. An associated computer program is under development. The proposed technique supports and extends the Layer of Protection Analysis application, especially for safety assurance assessment of risk-based determination for the process industries. A case study concerning HF alkylation plant illustrates the proposed method.     

Safety analysis and risk assessment of a Micro-GtL Plant

Laboratory hydrogen generators, medical oxygen, and micro-breweries are examples of modular and micro technologies that are commercial successes. Researchers, patients, and unskilled workers operate these facilities but more complex processes require highly qualified personnel to ensure they operate safely. Modular-micro processes in isolated locations meet economic objectives when operated remotely thereby minimizing labor costs. Mitigating the risk requires a comprehensive hazards analysis with advanced control systems particularly for explosive and toxic compounds. Here, we propose a method called Failure Mode Risk Decision (FMRD) to review the inherent hazards of a micro-refinery unit (MRU) that converts flared and wasted natural gas to long chain hydrocarbons. This approach combines the Process Flow Failure Mode (PFFM) methodology as a systematic and reliable technique with a novel numerical risk assessment to improve the analytical evaluation of hazardous conditions. The objective is to combine causes and consequences in a single metric, where scaled probability of evident causes and severity of consequences are used to derive a risk level measure. With the proposed metric, the magnitude of a potential hazard is directly correlated with the risk level. This mechanism identifies extra risk scenarios compared to the classical hazard analysis method and provides a straightforward comprehensive numerical assessment to represent the inherent and residual risks to facilitate justifying the hazardous scenarios. Accordingly, we design a safety loop and supply all the required facilities to remove the potential risks at the process plant. Not only the proposed methodology clarifies the risks of the MRU presented in this study, but can be extended to review the hazards of other chemical process plants.     

基于贝叶斯网络的养老院火灾风险评估方法研究

为预防养老院火灾事故,结合事故树法(FTA)和贝叶斯网络法(BN),建立了一套养老院火灾风险定量评估模型。首先,采用事故树法建立潜在的养老院火灾事故场景;其次,考虑到养老院火灾事故场景中不确定因素的影响,将事故树模型转化为贝叶斯网络模型,并结合有人员伤亡的养老院火灾事故发生发展实际优化模型;最后,以某市养老院为例,结合调研、文献及统计数据确定先验概率及条件概率,并用GENIE 2.0软件实现贝叶斯图形化,分析验证该模型逻辑可行性。结果表明:通过该模型和方法,不仅可以预测养老院火灾事故中各场景发生发展概率,还能对各风险因素敏感度和最大致因链进行分析,提高了风险因素辨识和评价的准确性,可以为养老院火灾风险分析和防控提供参考。     

重大疫情下疫苗冷链物流在物联网环境中的风险评估*

为识别和应对重大疫情下疫苗冷链过程中的风险,研究物联网环境中在人-机-环-管理下,以加工包装、储存、运输、装卸搬运、接种为风险链的评估指标体系,全面分析各个环节风险因素,提炼16个影响疫苗冷链质量安全风险点。根据因果关系将风险评价指标体系转换为贝叶斯网络结构,建立基于贝叶斯网络的风险评估模型,并使用GeNIe软件进行计算,搜索相关数据,以印度新冠疫苗冷链物流为实证案例检验模型。研究结果表明:重大疫情下疫苗冷链物流存在风险敏感点,必须明确防控重点,制定针对性控制措施,如加强人员培训、合理规划路径及接种点位置、加大设施设备检查与更新等,研究结果可为实现疫苗冷链物流的高效安全运转提供参考。