Lessons learned from past accidents - The integration of human and organizational factors with the technical aspect

It is of prime importance to ensure the safety of chemical process plants due to volatile nature of the industry and drastic consequences of the accidents. A number of parameters can affect the safety of the process plants. One of the main parameters that has the influence on the safety of operations is the Human and Organizational Factors (HOF) as suggested by numbers of existing studies. Therefore, in order to enhance the safety of operations it is required to improve the HOF. These factors can be improved by an integrated approach as proposed in this work, instead looking at these factors in an isolation. A number of existing risk assessment approaches have been analysed in this work and their compliance requirements to the relevant International Standards with respect to the HOF. A new quantitative methodology “Method for Error Deduction and Incident Analysis (MEDIA)” has been developed in this work. During the development of this methodology, practicality; consistency; integration with other risk assessment techniques and efficient use of information were explicitly ensured. The MEDIA can help to integrate the HOF around the technical aspect and can prioritize the follow up actions based on risk. The quantification of this methodology is based on results of the accident analysis, that has been carried out in this work. The accidents of 25 years (1988-2012) in the Seveso establishments and that were reported to the European Commission’s Major Accident Reporting System (eMARS) have been studied. The results from the accident analysis have further used in order to learn lessons and to propose future recommendations. These recommendations are mainly aimed at further integration of the HOF and to improve the overall safety of chemical process plants. More specifically, these recommendations are addressed to the use of organizational checklist during the Hazard Identification (HAZID) study; improvement of existing eMARS reporting structure and the legal obligation towards the EU Member States to report their accidents to the European Commission

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Development of a Human Reliability Analysis (HRA) model for break scheduling management in human-intensive working activities

2016 - 2017Human factors play an inevitable role in working contexts and the occurrence of human errors impacts on system reliability and safety, equipment performance and economic results. If human fallibility contributes to majority of incidents and accidents in high-risk systems, it mainly affects the quality and productivity in low-risk systems. Due to the prevalence of human error and the huge and often costly consequences, a considerable effort has been made in the field of Human Reliability Analysis (HRA), thus arriving to develop methods with the common purpose to predict the human error probability (HEP) and to enable safer and more productive designs. The purpose of each HRA method should be the HEP quantification to reduce and prevent possible conditions of error in a working context. However, existing HRA methods do not always pursue this aim in an efficient way, focusing on the qualitative error evaluation and on high-risk contexts. Moreover, several working aspects have been considered to prevent accidents and improve human performance in human-intensive working contexts, as for example the selection of adequate work-rest policies. It is well-known that introducing breaks is a key intervention to provide recovery after fatiguing physical work, prevent the growth of accident risks, and improve human reliability and productivity for individuals engaged in either mental or physical tasks. This is a very efficient approach even if it is not widely applied. ... [edited by Author]XXX cicl

Evaluation of human error probabilities for post-initiating events

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2007.Includes bibliographical references (leaves 84-85).The United States Nuclear Regulatory Commission is responsible for the safe operation of the United States nuclear power plant fleet, and human reliability analysis forms an important portion of the probabilistic risk assessment that demonstrates the safety of sites. Treatment of post-initiating event human error probabilities by three human reliability analysis methods are compared to determine the strengths and weaknesses of the methodologies and to identify how they may be best used. A Technique for Human Event Analysis (ATHEANA) has a unique approach because it searches and screens for deviation scenarios in addition to the nominal failure cases that most methodologies concentrate on. The quantification method of ATHEANA also differs from most methods because the quantification is dependent on expert elicitation to produce data instead of relying on a database or set of nominal values. The Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H) method uses eight performance shaping factors to modify nominal values in order to represent the quantification of the specifics of a situation. The Electric Power Research Institute Human Reliability Analysis Calculator is a software package that uses a combination of five methods to calculate human error probabilities. Each model is explained before comparing aspects such as the scope, treatment of time available, performance shaping factors, recovery and documentation. Recommendations for future work include creating a database of values based on the nuclear data and emphasizing the documentation of human reliability analysis methods in the future to improve traceability of the process.by Phillip E. Dawson.S.M

A MODEL-BASED HUMAN RELIABILITY ANALYSIS METHODOLOGY (PHOENIX METHOD)

Despite the advances made so far in developing human reliability analysis (HRA) methods, many issues still exist. Most notable are; the lack of an explicit causal model that incorporates relevant psychological and cognitive theories in its core human performance model, inability to explicitly model interdependencies between human failure events (HFEs) and influencing factors on human performance, lack of consistency, traceability and reproducibility in HRA analysis. These issues amongst others have contributed to the variability in results seen in the application of different HRA methods and even in cases where the same method is applied by different analysts. In an attempt to address these issues, a framework for a model-based HRA methodology has been recently proposed which incorporates strong elements of current HRA good practices, leverages lessons learned from empirical studies and the best features of existing and emerging HRA methods. This research completely develops this methodology which is aimed at enabling a more credible, consistent, and accurate qualitative and quantitative HRA analysis. The complete qualitative analysis procedure (including a hierarchical performance influencing factor set) and a causal model using Bayesian Belief network (BBN) have been developed to explicitly model the influence and dependencies among HFEs and the different factors that influence human performance. This model has the flexibility to be modified for interfacing with existing methods like Standard-Plant-Analysis-Risk-HRA-method. Also, the quantitative analysis procedure has been developed, incorporating a methodology for a cause-based explicit treatment of dependencies among HFEs, which has not been adequately addressed by any other HRA method. As part of this research, information has been gathered from sources (including other HRA methods, NPP operating experience, expert estimates), analyzed and aggregated to provide estimates for the model parameters needed for quantification. While the specific instance of this HRA method is used in nuclear power plants, the methodology itself is generic and can be applied in other environments

Evaluation of the use of engineering judgements applied to analytical human reliablity analysis methods (HRA)

Due to the scarcity of Human Reliability Analysis (HRA) data, one of the key elements of any HRA analysis is use of engineering judgment. The Electric Power Research Institute (EPRI) HRA Calculator guides the user through the steps of any HRA analysis and allows the user to choose among analytical HRA methods. It applies Accident Sequence Evaluation Program (ASEP), Technique for Human Error Rate Prediction (THERP), the HCR/ORE Correlation, and the Caused Based Decision Tree Method (CBDTM). This program is intended to produce consistent results among different analysts provided that the initial information is similar. Even with this analytical approach, an HRA analyst must still render several judgments. The objective of this study was to evaluate the use of engineering judgment applied to the quantification of post-initiator actions using the HRA Calculator. The Comanche Peak Steam Electric Station (CPSES) Level 1 Probabilistic Risk Assessment (PRA) HRA was used as a database for examples and numerical comparison. Engineering judgments were evaluated in the following ways: 1) Survey of HRA experts. Two surveys were completed, and the participants provided a range of different perspectives on how they individually apply engineering judgment. 2) Numerical comparison among the three methods. 3) Review of CPSES HRA and identification of judgments and the effects on the overall results of the database. The results of this study identified thirteen areas in which an HRA analyst must interpret and render judgments on how to quantify a Human Error Probability (HEP) and recommendations are provided on how current industry practitioners render these same judgments. The areas are: identification and definition of actions to be modeled, identification and definition of actions to be modeled, definition of critical actions, definition of cognitive portion of the action, choice of methodology, stress level, rule-, skill- or knowledge-based designation, timing information, training, procedures, human interactions with hardware, recoveries and dependencies within an action, and review of final HEP

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment

Evaluation of the use of engineering judgements applied to analytical human reliablity analysis methods (HRA)

Due to the scarcity of Human Reliability Analysis (HRA) data, one of the key elements of any HRA analysis is use of engineering judgment. The Electric Power Research Institute (EPRI) HRA Calculator guides the user through the steps of any HRA analysis and allows the user to choose among analytical HRA methods. It applies Accident Sequence Evaluation Program (ASEP), Technique for Human Error Rate Prediction (THERP), the HCR/ORE Correlation, and the Caused Based Decision Tree Method (CBDTM). This program is intended to produce consistent results among different analysts provided that the initial information is similar. Even with this analytical approach, an HRA analyst must still render several judgments. The objective of this study was to evaluate the use of engineering judgment applied to the quantification of post-initiator actions using the HRA Calculator. The Comanche Peak Steam Electric Station (CPSES) Level 1 Probabilistic Risk Assessment (PRA) HRA was used as a database for examples and numerical comparison. Engineering judgments were evaluated in the following ways: 1) Survey of HRA experts. Two surveys were completed, and the participants provided a range of different perspectives on how they individually apply engineering judgment. 2) Numerical comparison among the three methods. 3) Review of CPSES HRA and identification of judgments and the effects on the overall results of the database. The results of this study identified thirteen areas in which an HRA analyst must interpret and render judgments on how to quantify a Human Error Probability (HEP) and recommendations are provided on how current industry practitioners render these same judgments. The areas are: identification and definition of actions to be modeled, identification and definition of actions to be modeled, definition of critical actions, definition of cognitive portion of the action, choice of methodology, stress level, rule-, skill- or knowledge-based designation, timing information, training, procedures, human interactions with hardware, recoveries and dependencies within an action, and review of final HEP