Performance evaluation of process industries resilience: Risk-based with a network approach

Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

A method for determining critical events during large disasters of production platforms

This article presents a calculation-based methodology to determine the dominant event class in each of the phases of disasters being analysed, and to address the question of whether different disasters have similarities at crucial times in each phase of the disaster. Our approach is based on event network analysis. Disasters can be modelled using block diagrams and multiphase process trees. We propose trees in this article can be used as a tool for modelling phases of a disaster. The starting point for developing these models was fault tree analysis used for modelling the reliability structure of complex systems. This study demonstrates the possibility of using dual fault trees to describe the process as opposed to the structure. In our analyses, we examined four major disasters of production platforms that occurred in the last 50 years: Ixtoc I, Piper Alpha, Petrobras 36 and Deep Water Horizon. The course of each of these disasters has been described, the basic events of these disasters have been isolated, and assigned to event classes. The hierarchical importance of events was determined using the Birnbaum reliability measure, Birnbaum structural measure, Fussell-Vesely measure, criticality measure and improvement potential. For each phase of the analysed disasters, event importance is ranked, and the most important events that contributed to the phase are identified. General principles on the analysed disasters and the methodology used are also discussed.     

Risk analysis of deepwater drilling operations using Bayesian network

Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.     

Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis

The work presented in this paper used a quantitative analysis of relevant risks through the development of fault tree analysis and risk analysis methods to aid real time risk prediction and safety evaluation of leak in a storage tank. Criticality of risk elements and their attributes can be used with real time data to predict potential failures likely to occur. As an example, a risk matrix was used to rank risk of events that could lead to a leak in a storage tank and to make decisions on risks to be allowed based on past statistical data. An intelligent system that recognizes increasing level(s) and draws awareness to the possibility of additional increase before unsafe levels are attained was used to analyse and make critical decisions. After a visual depiction of relationships between hazards and controls had been actualized, dynamic risk modelling was used to quantify the effect controls can potentially have on hazards by applying historical and real-time data into a probabilistic model. The output of a dynamic risk model is near real-time quantitative predictions of risk likelihood. Results from the risk matrix analysis method mixed with RTD and FTA were analyzed, evaluated, and compared.     

Accident modelling and analysis in process industries

Accident modelling is a methodology used to relate the causes and effects of events that lead to accidents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure probabilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further.     

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.     

Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology

A safety analysis was performed to determine possible accidental events in the storage system used in the liquefied natural gas regasification plant using the integrated application of failure modes, effects and criticality analysis (FMECA) and hazard and operability analysis (HAZOP) methodologies. The goal of the FMECA technique is the estimation of component failure modes and their major effects, whereas HAZOP is a structured and systematic technique that provides an identification of the hazards and the operability problems using logical sequences of cause-deviation-consequence of process parameters. The proposed FMECA and HAZOP integrated analysis (FHIA) has been designed as a tool for the development of specific criteria for reliability and risk data organisation and to gain more recommendations than those typically provided by the application of a single methodology. This approach has been applied to the risk analysis of the LNG storage systems under construction in Porto Empedocle, Italy. The results showed that FHIA is a useful technique to better and more consistently identify the potential sources of human errors, causal factors in faults, multiple or common cause failures and correlation of cause-consequence of hazards during the various steps of the process.     

Initial phase modelling in numerical explosion applied to process safety

The utilisation of computational fluid dynamics (CFD) in process safety has increased significantly in recent years. The modelling of accidental explosion via CFD has in many cases replaced the classical Multi Energy and Brake Strehlow methods. The benefits obtained with CFD modelling can be diminished if proper modelling of the initial phase of explosion is neglected. In the early stages of an explosion, the flame propagates in a quasi-laminar regime. Proper modelling of the initial laminar phase is a key aspect in order to predict the peak pressure and the time to peak pressure. The present work suggests a modelling approach for the initial laminar phase in explosion scenarios. Findings are compared with experimental data for two classical explosion test cases which resemble the common features in chemical process areas (confinement and congestion). A detailed analysis of the threshold for the transition from laminar to turbulent regime is also carried out. The modelling is implemented in a fully 3D Navier–Stokes compressible formulation. Combustion is treated using a laminar flamelet approach based on the Bray, Moss and Libby (BML) formulation. A novel modified porosity approach developed for the unstructured solver is also considered. Results agree satisfactorily with experiments and the modelling is found to be robust.     

铅碱性精炼废渣的资源化利用研究

介绍了铅碱性精炼废渣资源化利用工艺流程,探讨了该过程的工艺条件,讨论了有关影响因素.实验结果表明,经脱砷和脱铅后,Sb以锑精矿形式产出,可作为炼锑的原料;Pb以PbCl2或铅渣形式、Sn以锡渣形式产出,其品位较高,可作Pb或Sn冶金的原料;该工艺具有化工试剂消耗少、综合利用率高、污染小、易于实现大规模工业化等优点.     

Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station

A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

事故过程的确定性混沌分析方法

综述了对事故致因理论的研究现状 ;提出了系统事故过程的确定性混沌分析方法 ;介绍了混沌分析方法的计算步骤 ;并以矿井火灾过程进行了实例分析。结果表明 :这种方法可对事故过程的系统状态进行连续分析。     

Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

Batch process usually differs from the continuous process because of its time-varying variables and the process parameters. An early detection and isolation of faults in the process will help to reduce the process upsets and keep it safe and reliable. This paper discusses on the application of multi-layer perceptron neural network in detecting various faults in batch chemical reactor based on an esterification process that involves the reaction of ethanol and acetic acid catalyzed by sulfuric acid. A multi-layer feed forward neural network with double hidden layers has been used in the neural network architecture. The detection was based on the different patterns generated between normal and faulty conditions. An optimum network configuration was found when the network produced the minimal error with respect to the training, testing and data validation.     

Model-based hazard identification in multiphase chemical reactors

Chemical productions operated in extreme conditions (high pressure, high temperature) require a detailed analysis of all potentially dangerous situations that can lead to a major industrial accident and thus cause a loss of life and property. Many accidents in the near or distant history underline the need of a detailed safety analysis in process industries, not only in the phase of plant design but also during the operation of the plant. It would be shown that simulation of a chemical unit using an appropriate mathematical model and the nonlinear analysis theory can be a suitable tool for safety analysis. This approach is based on mathematical modeling of a process unit where both the steady-state analysis, including the analysis of the steady states multiplicity and stability, and the dynamic simulation are used. Principal objective of this paper is to summarize problems regarding the model-based hazard identification in processes. A case study, focused on phenomena of multiple steady states in ammonia synthesis reactor will be presented. The influence of the model complexity and model parameters uncertainly on the quality of safety analysis would be underline.     

Hydrocarbon releases on oil and gas production platforms: Release scenarios and safety barriers

The main objective of this paper is to present and discuss a set of scenarios that may lead to hydrocarbon releases on offshore oil and gas production platforms. Each release scenario is described by an initiating event (i.e., a deviation), the barrier functions introduced to prevent the initiating event from developing into a release, and how the barrier functions are implemented in terms of barrier systems. Both technical and human/operational safety barriers are considered. The initiating events are divided into five main categories: (1) human and operational errors, (2) technical failures, (3) process upsets, (4) external events or loads, and (5) latent failures from design. The release scenarios may be used as basis for analyses of: (a) the performance of safety barriers introduced to prevent hydrocarbon releases on specific platforms, (b) the platform specific hydrocarbon release frequencies in future quantitative risk analyses, (c) the effect on the total hydrocarbon release frequency of the safety barriers and risk reducing measures (or risk increasing changes).     

第三方挖掘作用下PE燃气管道失效行为研究

针对挖掘破坏导致的城镇燃气管道失效,开展了挖掘作用下管道力学失效机理分析。考虑管土接触作用,建立了城镇燃气PE管道在挖掘齿作用下的三维力学响应分析模型,分析了典型工况下管道的失效过程,讨论了基于应力准则与基于应变准则等2种失效准则的适用性,并开展了影响因素分析。结果表明:机械齿作用下管道主要失效位置为机械齿与管道接触位置两端;采用基于应变的失效准则可以更好地利用PE管材的塑性性能;机械齿的作用位置对管道力学响应影响较小;管径和壁厚的增大能减小管道内的应力,同时能够减小管道的截面椭圆度;内压的改变对管道的力学响应几乎没有影响。以上结果可为城镇燃气管道的力学失效分析与安全评价提供一定的参考。     

An extension of Multiple Greek Letter method for common cause failures modelling

Common cause failure describes a condition where several components share the same source of failure that causes them to fail or become unavailable simultaneously. The objective of this paper is to present an improved approach to common cause failure modelling within reliability analyses. The currently used methods allow one component to share common characteristics with only one group of components, which may be affected by the same source of failure. Therefore, an improved method was developed, where components can be assigned to several groups of components that are susceptible to faulty operation with respect to their similar characteristics. A mathematical derivation of the method is presented and the theory is applied to smaller theoretical samples and to a simplified real example. The results show that the new method enables a more detailed reliability analysis. The results prove that consideration of common cause failures using the improved method may decrease the system reliability compared to traditional common cause failure consideration. The system reliability decreases more, if the redundant components have more similarities and are therefore assigned to several common cause failure groups.     

低功率和停堆工况下人员可靠性分析

低功率和停堆工况下人的错误操作引起的人误事件,是电站风险的重要根源之一,应对其进行认真分析并找出其发生的主要原因。笔者根据低功率和停堆工况下人误事件的特点,通过对5种人员可靠性分析方法的比较,选择了SPAR -H作为人误事件定量化分析的方法;以停堆工况下的抽水过多事件为实例,对该事件中包含的3个人误事件进行了定量化分析,给出了定量化分析结果;通过分析、比较及实例应用的结果表明,SPAR H作为低功率和停堆工况下HRA分析方法是合适的,符合该工况下人误事件的特点,同时SPAR H过程简单,有利于电站人员进行实际应用。     

降雨条件下尾矿坝饱和-半饱和渗流模拟分析

降雨条件下,由于渗透压力的存在,降低了尾矿坝坝坡的整体稳定性,可能产生坝体自身的变形和破坏,甚至导致尾矿库溃坝发生.针对某尾矿库,分析了降雨条件下对尾矿坝渗流及稳定的影响,运用饱和-非饱和渗流理论及降雨入渗理论,分析了尾矿坝渗流场的变化过程,根据计算出来的瞬态渗流场,利用非饱和尾砂抗剪强度理论,对尾矿坝的瞬态稳定性进行了分析.研究成果对于提高尾矿坝降雨条件下安全运行,降低尾矿库溃坝事故发生率具有重要意义.