Bayesian estimation and consequence modelling of deliberately induced domino effects in process facilities

Process facilities handling hazardous chemicals in large quantities and elevated operating conditions of temperature/pressure are attractive targets to external attacks. The possibility of an external attack on a critical installation, performed with an intention of triggering escalation of primary incidents into secondary and tertiary incidents, thereby increasing the severity of consequences needs to be effectively analysed. A prominent Petrochemical Industry located in Kerala, India was identified for studying the possibility of a deliberately induced domino effect. In this study, a dedicated Bayesian network is developed to model the domino propagation sequence in the chemical storage area of the industry, and to estimate the domino probabilities at different levels. This method has the advantage of accurately quantifying domino occurrence probabilities and identifying possible higher levels of escalations. Moreover, the combined effect from multiple units can be modelled easily and new information can be added into the model as evidences to update the probabilities. Phast (Process hazard analysis) software is used for consequence modelling to determine the impact zones of the identified primary and secondary incidents. The results of the case study show that such analyses can greatly benefit green field and brown field projects in determining the appropriate safety and security measures to be implemented or strengthened so as to reduce its attractiveness to external threat agents.     

Reliability assessment of ultra-deep oil and gas wellbore casing using data statistics and numerical simulations

Ultra-deep oil and gas wells have become a new development trend in onshore oil and gas exploitation. However, Ultra-deep oil and gas wellbore casing is with high failure risk due to the harsh environment. It is essential to evaluate the reliability of wellbore casing. This paper assesses the operational reliability of wellbore casing using data statistics and numerical simulation. Firstly, the theoretical model for reliability analysis of wellbore casing is established, and the variables in the model are determined, including rock mechanics, cement ring, and casing string strength factors. Subsequently, considering the random distribution of model variables, many statistics and analyses are performed to determine the distribution parameters of the model variables. Eventually, Monte Carlo based numerical simulations are carried out to obtain the residual strength distribution and the reliability of wellbore casing. The production casing in the ultra-deep well with a depth of 6.5 km in China as an industrial case is used to illustrate the present study. It is observed that this study can be useful to guide a more accurate assessment of the reliability of ultra-deep wellbore casing.     

Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis

In almost all industries, fire alarm systems play a vital role in the reducing the risks associated with fires and damages. Therefore, it is necessary to evaluate their reliability and performance in emergency situations. The present study aimed to use fault tree analysis (FTA) to determine the root causes involved in the failure of fire alarm systems, to use Fuzzy set theory and expert elicitation to determine relative probabilities, and finally, to evaluate the reliability of a fire alarm system using dynamic Bayesian networks (BNs) during a thirty-six months period. A total of 29 basic events were detected from the FT. The reliability of the fire alarm system was estimated at 0.954 according to the FT and 0.957 according to conventional BNs. The reliability of the fire alarm system after 36 months was estimated at 0.375 according to dynamic BNs. All the events involved in the failure of fire alarm systems were drawn in the fault tree diagram. The results indicate that remodeling of these systems and simultaneous construction activities are the most important factors in the failure of the fire alarm system. System reliability can also be increased to 0.965 by providing preventive and control measures to reduce the probability of critical events.     

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.     

基于CFD的核电厂防火安全评估方法研究

目前国内核电厂普遍采用确定论方法进行防火安全评估。采用CFD模型对核电厂某典型电气间火灾发生过程进行数值模拟研究。模拟火灾行为（火势增长和蔓延）、温度场变化、烟气浓度变化等,分析结果中温度对电缆和电气设备的失效判定、烟气层对电缆和电气设备的风险影响,研究该方法对于核电厂防火安全分析的指导作用。通过分析数值模拟数据,计算结果与二代机型确定论分析方法结果相符,有效验证了CFD火灾模型在核电厂防火安全评估中的适用性,为国内自主建立核电厂火灾数值模拟评价体系提供参考。     

Integration of data analytics with cloud services for safer process systems,application examples and implementation challenges

Emerging sensors, computers, network technologies, and connected platforms result potentially in an immeasurable collection of data within plant operations. This creates the possibility of solving problems innovatively. Because most of the data appear to be unstructured or semi-structured, organizations shall design and adopt new strategies. Further, workflow architectures with data analytics are needed including machine learning tools and artificial intelligence techniques before proto-type solutions can be developed. We shall discuss several prospects of using (big) data analytics integrated with cloud services to produce solutions for improving plant operations. The paper outlines the vision and a systematic framework highlighting the data analytics lifecycle in the area of plant operation, process safety, and environmental protection. Four rather diverse example case studies are demonstrated including (1) deep learning-based predictive maintenance monitoring modeling, (2) Natural Language Processing (NLP) for mining text, (3) barrier assessment for dynamic risk mapping (DRA), and (4) correlation development for sustainability indicators. It further discusses the challenges in both research and implementation of proposed solutions in the industry. It is concluded that a well-balanced integrated approach including machine supporting decisions integrated with expert knowledge and available information from various key resources is required to enable more informed policy, strategic, and operational risk decision-making leading to safer, reliable and more efficient operations.     

Resilience-based optimal firefighting to prevent domino effects in process plants

Domino effects triggered by fire can cause extremely severe damages to the chemical and process plants. In the need of a more effective prevention of fire domino effects, the present study focuses on firefighting which has received less attention compared to passive and active fire protection systems. Considering both the vulnerability and recoverability phases during fire domino effects, we have introduced a methodology for optimal identification of firefighting strategies so as to increase the resiliency of process plants in dealing with fire escalation scenarios. The area above the resilience curve (AARC), which is equal to the accumulation of loss of resilience over time, was considered as the metric to identify the optimal firefighting strategies. In other words, the strategy leading to the lowest AARC can be selected as the optimal strategy from a resiliency perspective.     

同异反向量模型在电厂安全评价中的应用

根据集对分析理论,针对安全评价系统的不确定性特点,采用了同异反向量模型对某电厂进行安全评价,评价结果与原文献吻合.该方法数学表达式简单,计算方便,物理意义明确.在安全评价过程中,差异度系数I的不同取值,反映了该电厂安全状况的变化,是安全管理工作的"晴雨表",可以使安全状况受到"增益"或"惩罚",映射出安全管理工作的重要性.这种方法为企业管理者提供了量化分析,使管理者对企业的安全程度及发展趋势有了更确切的了解,为安全管理工作提供依据.     

Reliability and availability modeling of Subsea Xmas tree system using Dynamic Bayesian network with different maintenance methods

Subsea Xmas tree is a vital equipment for offshore oil and gas development. Aiming at the fault mode of subsea Christmas tree system under production conditions, the fault tree of subsea tree system was established, which was transformed into Dynamic Bayesian network, and the reliability and availability of subsea tree system with different repair states are quantitatively analyzed. In this paper, the DBNs are partially verified by the method based on three axes. The results show that the reliability of subsea vertical tree system is slightly higher than that of subsea horizontal tree system. After repair and maintenance, the performance of subsea tree system has been significantly improved, and the improvement of the system performance by preventive maintenance is more obvious. Compared with the perfect repair, the performance of the system with imperfect repair is not significantly reduced. Compared with perfect repair & preventive maintenance, the performance of the system with imperfect repair & preventive maintenance is slightly reduced. In addition, the influence of failure rates and degradation probability on reliability and availability is analyzed. By comparing the influence of failure rates on the system performance of non-maintenance and maintenance, it is found that the change of failure rates has the greatest influence on the reliability and the least influence on the availability of perfect repair & preventive maintenance. By comparing the performance of each component in the subsea tree system, it is found that the failure rates has the most obvious influence on the chock module, and gate valve and tree cap have the most significant influence on the reliability of the system. In order to improve the reliability of subsea tree system, it is necessary to improve the reliability of chock module, gate valve and tree cap.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

The impact of differences in reliability data on the results of probabilistic safety analyses

The lack of plant-specific reliability data for probabilistic safety assessments is occasionally used for discrediting and doubting the validity of such analyses. On the other hand, analyses are often performed without even discussing the applicability of generic reliability data. In an attempt of clarification the impact of several sets of reliability data stemming from different sources on the explosion frequency of two processes with exothermal reactions (one of them is analyzed with plant-specific data) are quoted. As a further case study the reactor cooling system of one of the processes is analyzed. Additionally, a procedure using frequency and probability ranges is employed for comparison. The results show agreement within their respective uncertainty bounds; the identification of key components for safety is not hampered by data differences. The superiority of plant-specific data, which should of course be acquired, cannot be doubted. Nevertheless, improving the safety of a plant is possible using probabilistic safety analyses even with data which do not stem from the plant under investigation.     

化工装置泄漏频率数据源比较研究

为降低泄漏频率对化工装置定量风险分析结果不确定性的影响,从覆盖设备设施类型、基准泄漏频率和频率计算模型等方面,比较分析中国、英国健康安全管理局、挪威船级社、国际油气生产商联合会(IOGP)发布的泄漏频率数据源,筛选出更适合计算化工装置泄漏频率的数据源;并以某化工装置2个泄漏单元为例,比较《危险化学品生产装置和储存设施外...     

Improved management practice and process hazard analysis techniques for minimizing likelihood of process safety incidents in Taiwan

In Taiwan, process safety accidents often occur despite the prior implementation of process hazard analysis (PHA). One of the main reasons for this is the poor quality of the PHA process; with the main hazards not being properly identified, or properly controlled. Accordingly, based on the findings of 86 process safety management (PSM) audits, dozens of post-accident site resumption review meetings, and hundreds of PSM review sessions, this study examines the main deficiencies of management practice and PHA implementation in Taiwan, and presents several recommendations for improved PHA assessment techniques and procedures. The study additionally examines the feasibility for using PSM-related information, such as process safety information and process incident information, as a tool for further enhancing the PHA quality. Overall, the study suggests that, in addition to following the basic rules of PHA and requirements of OSHA (1992),management in Taiwan should also provide training in the enhanced assessment techniques proposed herein and take active steps to incorporate PSM information into the PHA framework in order to improve the general quality of PHA and reduce the likelihood of process safety accidents accordingly.     

建筑火灾风险指数法权重确定

建筑火灾风险指数法中指标权重的确定是一个关键技术问题。本文探讨了基于的AHP的评价指标权重的确定方法,通过专家调查表的形式,邀请40位相关专家评分,通过层次分析法建立判断矩阵,对判断矩阵的计算借助软件Yaahp 0．4．1,得出各位专家的权重值,再采用算术平均法求专家组的综合结果,从而得出总体累计权重的分布。研究结果为厘定火灾保险费率提供依据,为制定财产保险火灾风险评价标准提供技术上的支持,将填补我国在该领域的空白。     

尾矿坝地震液化评价可靠度模型及应用

为适应计算参数本身具有的随机性和未确知性,将可靠度理论引入尾矿坝地震液化评价中。以测试数据的统计分析结果为基础,应用一次二阶矩法建立尾矿坝地震液化分析可靠度模型,探讨了可靠度指标与抗液化安全系数之间的关系,并将该模型应用到某尾矿坝地震液化分析中。结果表明,新建可靠度模型各参数的物理意义与统计指标明确,相比传统的确定性分析方法,不仅能判断液化的发生与否,还能给出液化发生的概率;可靠度理论在液化分析中能更好地考虑计算参数的变异性,进一步完善了尾矿坝地震液化分析理论,为进行基于风险分析的尾矿坝抗震设计和地震安全评价提供更全面的依据。     

A thermal hazard risk evaluation of emulsion polymerisation and vinyl acetate monomers in the latex manufacturing process

Latex is extensively used in industrial products. However, completing some processes at scale leads to unacceptable levels of risk that need to be quantified and mitigated. Systemic risks must be eliminated wherever possible, and safety takes priority over efficiency and quality. To assess the process risks accurately, four raw materials were examined in this study: polyvinyl acetate (PVA), latex process-initiator-ammonium persulfate (APS) and hydrogen peroxide (H₂O₂), and vinyl acetate monomer (VAM). The physicochemical composition of the PVA latex process was determined via calorimeters, including differential scanning calorimetry (DSC) and vent sizing package 2 (VSP2). The calorimetry results showed that the protective colloid was a critical component in the polymerisation reaction. In addition, when adding initiators to the system, it is vital to observe the normal ratio of materials and keep the stirring system operating. The scenario system also simulated the effects of shutting down various inhibitory programs, including the build-up of free radicals that could result in a runaway reaction when the initiator was added in excess. On the other hand, the result of the risk matrix displayed as a medium level, indicating that although the probability of an accident is low, the resulting severity is at disaster level. As a result, this study provides process safety engineers with a reliable frame of reference for assessing the potential dangers in the PVA latex manufacturing process.     

三峡库区城市给水厂多环芳烃分布变化评价

多环芳烃PAHs(Polycyclic Aromatic Hydrocarbons)是广泛存在的典型持久性有机污染物,了解三峡库区城市供水中PAHs的分布和变化具有重要的理论和现实意义.本文选用EPA推荐的3510c液-液萃取前处理方法和GC/MS 8270c测定方法,对三峡库区6个城市自来水厂(重庆主城3个,库区3个)原水与出厂水的多环芳烃物质的种类和含量进行了测定,并对主城某水厂两套常规处理工艺各工段PAHs的分布和水平进行了分析评价.结果表明,6个水厂的原水和出厂水中检出了萘、菲、蒽、芘、荧蒽、二氢苊共6种PAHs,其中萘、蒽、荧蒽检出率较高,枯水期含量水平在1～40 ng/L.PAHs经2种常规的水处理工艺后,浓度有所降低.但滤后水经加氯消毒等工序后,PAHs浓度有所增加.     

社区安全脆弱性评估模型的构建与应用

为了准确衡量社区的安全水平,提出了社区安全脆弱性的概念模型,并分析社区安全脆弱性的影响因素。通过由人员特征、基础设施、社区环境和管理水平构成的结构性因素和由自然风险、人为风险构成的胁迫性因素,构建出社区安全脆弱性评估模型,并选取3个代表性社区进行实证分析。结果表明:社区安全脆弱性评估模型计算得出的社区安全脆弱性指数,与社区安全的评估水平相吻合,能够准确反映出社区安全状况。     

FSRU作业过程火灾爆炸危险性评估研究

浮式储存和再气化装置（FSRU）运行过程中易导致火灾爆炸等事故的发生,为有效评估FSRU作业过程火灾爆炸危险性,采用火灾爆炸危险指数评估法,对运用FSRU的某浮式LNG接收终端进行危险性评估;选取LNG运输船与FSRU装料作业等9个单元,研究确定了一般工艺危险性系数、特殊工艺危险性系数、安全措施补偿系数等参数,得出了补偿前后的火灾爆炸危险性指数,有效评估了FSRU作业过程火灾爆炸危险性,并基于研究结果提出了保障FSRU作业安全的对策措施与建议。研究结果表明,安全措施补偿前,缘于LNG/NG本身的火灾危险性和数量较大,能量高度集中,LNG运输船与FSRU装料作业等单元的火灾爆炸危险等级均达到了“非常大”;在采取了一系列的安全措施补偿后,火灾爆炸危险指数降低了3/5左右。这对系统深入地研究FSRU作业安全具有较重要的理论意义和实际应用价值。