Determination of human error probabilities for offshore platform musters

The focus of this work is on prediction of human error probabilities during the process of emergency musters on offshore oil and gas production platforms. Due to a lack of human error databases, and in particular human error data for offshore platform musters, an expert judgment technique, the Success Likelihood Index Methodology (SLIM), was adopted as a vehicle to predict human error probabilities. Three muster scenarios of varying severity (man overboard, gas release, and fire and explosion) were studied in detail. A panel of 24 judges active in the offshore oil and gas industry provided data for both the weighting and rating of six performance shaping factors. These data were subsequently processed by means of SLIM to calculate the probability of success for 18 muster actions ranging from point of muster initiator to the final actions in the temporary safe refuge (TSR). The six performance shaping factors considered in this work were stress, complexity, training, experience, event factors and atmospheric factors.     

A framework for human error risk analysis of coal mine emergency evacuation in China

Evacuation from underground coal mine in emergency as soon as possible makes the difference between life and death. Human factors have an important impact on a successful evacuation, but literature review shows that there is a lack of consideration of human error risk during coal mine emergency evacuation in China. To address the above problems, in this paper, we established a framework for human error risk analysis of coal mine emergency evacuation, consisting of scenario and task analysis, risk assessment and risk reduction. A general evacuation procedure which is applicable for different causes is detailed through the scenario and task analysis. A new method based on expert judgment, named OGI-Model, is proposed to evaluate the reliability of human safety barrier. In this new approach, human safety barrier is divided into three sub-barriers, i.e., organization safety sub-barrier (OSSB), group safety sub-barrier (GSSB), and individual safety sub-barrier (ISSB). Each sub-barrier consists of a series of concrete measures against specific evacuation actions. An example is provided in this paper to demonstrate the use of this framework and its effectiveness.     

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.     

Reconstructing human contributions to accidents: the new view on error and performance

PROBLEM: How can human contributions to accidents be reconstructed? Investigators can easily take the position a of retrospective outsider, looking back on a sequence of events that seems to lead to an inevitable outcome, and pointing out where people went wrong. This does not explain much, however, and may not help prevent recurrence. METHOD AND RESULTS: This paper examines how investigators can reconstruct the role that people contribute to accidents in light of what has recently become known as the new view of human error. The commitment of the new view is to move controversial human assessments and actions back into the flow of events of which they were part and which helped bring them forth, to see why assessments and actions made sense to people at the time. The second half of the paper addresses one way in which investigators can begin to reconstruct people's unfolding mindsets. IMPACT ON INDUSTRY: In an era where a large portion of accidents are attributed to human error, it is critical to understand why people did what they did, rather than judging them for not doing what we now know they should have done. This paper helps investigators avoid the traps of hindsight by presenting a method with which investigators can begin to see how people's actions and assessments actually made sense at the time.     

Risk assessment in a hypothetical network pipeline in UK transporting carbon dioxide

With the advent of Carbon Capture and Storage technology (CCS) the scale and extent of its handling is set to increase. Carbon dioxide (CO₂) capture plants are expected to be situated near to power plants and other large industrial sources. Afterward CO₂ is to be transported to storage site using one or a combination of transport media: truck, train, ship or pipeline. Transport by pipeline is considered the preferred option for large quantities of CO₂ over long distances. The hazard connected with this kind of transportation can be considered an emerging risk and is the subject of this paper.The paper describes the Quantitative Risk Assessment of a hypothetical network pipeline located in UK, in particular the study of consequences due to a CO₂ release from pipeline.The risk analysis highlighted that some sections of pipeline network cross densely populated areas. For this reason, some changes in the original path of the network have been proposed in order to achieve a significant reduction in the societal risk.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

基于CREAM和贝叶斯网络的空管人误概率预测方法

针对研究管制人因可靠性时存在的模糊性和片面性问题,采用认知可靠性与失误分析方法（CREAM）中的扩展预测法,计算10项管制通用任务的人误概率;在此基础上,以管制行为形成因子作为根节点构建贝叶斯网络,建立其与情景控制模式的不确定关系模型,对管制员在多任务中的人误概率进行预测。研究结果表明:在由相同评判者给出行为形成因子影响效应的前提下,由CREAM扩展预测法和构建贝叶斯网络的方法预测得到的多数任务的人误概率差异较大,从方法的客观性、合理性和适用性角度分析,贝叶斯网络在研究该问题时更具优势。     

Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000-2009

The objective of this work is to determine and study, analyze and elaborate, classify and categorize the main risk analysis and risk-assessment methods and techniques by reviewing the scientific literature. The paper consists of two parts: a) the investigation, presentation and elaboration of the main risk-assessment methodologies and b) the statistical analysis, classification, and comparative study of the corresponding scientific papers published by six representative scientific journals of Elsevier B.V. covering the decade 2000-2009. The scientific literature reviewing showed that the risk analysis and assessment techniques are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative-quantitative, semi-quantitative). The qualitative techniques are based both on analytical estimation processes, and on the safety managers-engineers ability. According to quantitative techniques, the risk can be considered as a quantity, which can be estimated and expressed by a mathematical relation, under the help of real accidents’ data recorded in a work site. The hybrid techniques, present a great complexity due to their ad hoc character that prevents a wide spreading. The statistical analysis shows that the quantitative methods present the highest relative frequency (65.63%) while the qualitative a lower one (27.68%). Furthermore the hybrid methods remain constantly at a very low level (6.70%) during the entire processing period.     

Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.     

Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

The oxygen-enhanced combustor has the advantages of high burning efficiency and low emissions. However, it should not be promoted for industrial use until its reliability and safety have been fully recognized. A new methodology is proposed to assess the risk of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree. In addition, it is applied to a selected pilot semi-industrial combustor. To identify the hazard source comprehensively, the pilot is divided into four subsystems: the combustor subsystem, feed subsystem, ignition subsystem and exhaust subsystem. According to the operational parameters of flow (flow rate, temperature and pressure) and the component functions in different subsystems, the cause and effect matrix can be built using the structural model, and the relationship between the operational parameters and the effects of the change for the operational parameters on the system can be presented. Based on the results of cause and effect matrix, the FMEA can be built to describe the failed models and accident scenarios of the pilot. The main accident forms include leakage, injury, fire and explosion. Accordingly, with the severity and probability analysis of different accident forms, the fire and explosion accidents should be further accessed quantitatively using the fuzzy fault tree analysis. The fault trees can be obtained in accordance with the FMEA, and the qualitative assessments of the basic events can be collected by using expert scoring. A hybrid approach for the fuzzy set theory and weight analysis is investigated to quantify the occurrence probability of basic events. Then, the importance analysis of the fault trees, including the hazard importance of basic events and the cut set importance, is performed to help determine the weak links of the fire and explosion trees. Finally, some of the most effective measures are presented to improve the reliability and safety of the combustion system.     

电梯检验过程中的人因失误分析与管理对策

近年来,我国电梯行业发展迅速,但电梯安全事故频发.导致电梯安全事故的原因中,人为失误占很大比重.因此,研究电梯检验过程中的人因失误及管理对策具有重要意义.应用层次分析法构建了理论模型,对可能的人因失误的关键要素进行提取并按相对重要性进行排序,确定了影响人因失误的重要因素.结合近6年南京市电梯检验人因失误的统计数据,应用灰色关联度理论对操作因子进行关联分析,并由此提出了减少电梯检验过程中人因失误的管理建议.     

Villains,victims, and heroes: Accounting for the roles human activity plays in LOPA scenarios

When a team is analyzing a LOPA scenario, the team needs to consider all three roles played by human interaction in the scenario: that of cause, as a result of human error; that of receptor, both in terms of safety impacts (inside the fence line) and community impacts (outside the fence line); and that of independent layer of protection (IPL), considering both administrative controls and human responses. Frequently, the nature of these three roles are inter-related, and setting guidance that is internally consistent is important to using LOPA to assess risk rather than as a means to game the analyses to simply achieve a wished-for result.A number of criteria have been proposed to quantify human involvement, typically as cause, as receptor, or as IPL. Establishing a framework to look at all three in a unified way is more likely to result in analyses that are consistent from scenario to scenario.This paper describes such a framework and presents it in a way that allows organizations to review their own criteria for quantifying human involvement in LOPA. It also examines some of the published LOPA criteria for human involvement and looks at them in terms of consistency of approach between evaluation of cause, receptor, and IPL. Finally the paper makes suggestions to use in calibrating LOPA methodologies to achieve consistent and believable results in terms of human interaction within and between scenarios that have worked for other organizations.     

Developing a new alternative risk assessment framework in the work sites by including a stochastic and a deterministic process: A case study for the Greek Public Electric Power Provider

An individual method cannot achieve the optimum risk-assessment result in the worksites, and future perspectives should focus on the parallel application of a deterministic approach with a stochastic approach. In particular, the risk analysis and assessment techniques of the deterministic (DET) approach are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative-quantitative, semi-quantitative). Furthermore, the stochastic (STO) approach includes the classic statistical approach (CSA) and the accident forecasting modeling (AFM). The objective of this paper is triple: (a) the presentation and classification of the main risk analysis and risk assessment methods and techniques of the deterministic approach and the stochastic approach as well, (b) the development and presentation of a new alternative risk assessment framework (called as STODET) including a stochastic and a deterministic process, and (c) the application of STODET in the Greek Public Power Corporation (PPC) by using occupational accidents that have been recorded, during the 17-year period of 1993-2009. In particular, the STODET application proves that required actions (or suppressive measures) are essential and must be taken in a medium-term period (1 working year) for abolishing the hazard sources.     

Fisher法在土壤污染综合评价优化布点中的应用

根据Fisher最优分割的理论,研究其应用于土壤环境监测优化布点的可行性。以福建省闽江河口湿地土壤重金属污染综合评价为实例,用Fisher法计算不同分类方式下的最小误差函数,确定最优的分割方法,确定优化布点方案。结果表明,原布设的8个监测点用Fisher法优化为3个典型点位,可客观反映闽江河口湿地土壤重金属污染的综合评价结果。     

Integration of Recursive Operability Analysis,FMECA and FTA for the Quantitative Risk Assessment in biogas plants: Role of procedural errors and components failures

With more than 350 GWh per year and thousands of installations around the world, biogas is an appealing strategy in the field of energy production and industrial waste optimization. In this sense, it is of paramount importance to address the risk associated with such plants, as an increasing trend of accidents have been recorded in the last 20 years. In this work, a representative biogas production plant was considered, and a risk assessment was carried out through the combination of Recursive Operability Analysis and Failure Mode and Effects Criticality Analysis. The methodology is rigorous and allows for both the identification and the quantification of accidental scenarios due to procedural errors and equipment failures, which miss in the literature for the case of biogas. The analysis allows the automatic generation of the Fault Trees for the identified Top Events, which can be numerically solved. Results show that the most critical accidental scenario in the biogas plant here considered is the formation of an explosive air-biogas mixture, which can occur in both anaerobic digester and condensate trap. The calculated probabilities agree with the results available in literature on similar plants. Pumps and Distributed Control System were found to be the most critical components.