Standardized Plant Analysis Risk-Human Research Articles

Developing a new method for improving the human resource reliability using the hybrid approach of SPAR‐H and DSM in the urban railway

AbstractAs one of the most important reasons of irreparable losses at work, human error is still a significant problem in spite of the rapid technological advancement and reduced role of humans in the systems set up and control. Therefore, with the aim of reducing human error, this study presents a new method to improve human reliability using the hybrid approach of SPAR‐H and DSM. For this purpose, the number of 53 tasks was specified for the terminal conductor using Hierarchical Task Analysis (HTA). Then, the error related to each task was calculated utilizing the Standardized Plant Analysis‐Risk Human (SPAR‐H) method, and based on the expert team's opinion. Next, the terminal conductor's tasks were entered into the DSM along with the error numbers and dependencies between tasks, and the most optimal arrangement of tasks was obtained by coding in MATLAB software. Finally, by using the SPAR‐H method, the error of the terminal conductor was calculated based on the new tasks arrangement and compared with the primary state. Based on the obtained results, the 30% increase in reliability shows the significant contribution of the optimal arrangement of tasks in reducing human error. The proposed method of this study helps managers to reviewing the task structure in high‐risk processes as an effective strategy to reduce human error.

Handling dependencies among performance shaping factors in SPAR-H through DEMATEL method

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

Safety Analysis of Offshore Wells Plugging and Abandonment Process with Riserless Well Intervention System Using a DBN based Comprehensive Method

The Riserless Well Intervention (RLWI) system that performs complex offshore oil well Plugging and Abandonment (P&A) operations is a typical Multi-Mission Phased-Mission System (MM-PMS), which requires multiple missions to be completed within a phase. P&A processes involve complex operations and equipment that can contaminate local marine ecosystems if they fail. Therefore, it is necessary to evaluate the reliability of the RLWI system. This paper proposes a dynamic reliability evaluation model for analyzing the RLWI MM-PMS. The GO model of the phase operation process and the Fault Tree (FT) model used to analyze the failure of each mission were established, and a Dynamic Bayesian Network (DBN) model based on the GO model and the FT model was developed for reliability evaluation. The established model can analyze the changes in the reliability of the RLWI MM-PMS more comprehensively, and can also clarify the importance of different missions and different system components. In addition, considering the impact of the marine environment on operators, the Standardized Plant Analysis Risk-Human (SPAR-H) reliability analysis is used for quantification. These findings can guide the improvement of the reliability of the RLWI system and the success rate of P&A operations.

Investigation of the Occupational Hygiene Errors in the Tasks of Overhead Crane Operator Using Standardized Plant Analysis Riskhuman Reliability Analysis Technique in One Steel Industry

Investigation of the Occupational Hygiene Errors in the Tasks of Overhead Crane Operator Using Standardized Plant Analysis Riskhuman Reliability Analysis Technique in One Steel Industry

A proposed improvement for the design of safety injection system in VVER-1000/V446 reactor

If the Total Loss of Feedwater (TLFW) accident occurs in a VVER reactor, the primary side Feed & Bleed (F&B) recovery strategy prevents core damage by reducing the reactor's pressure by opening the pressurizer's safety valves and establishing core cooling by the Emergency Core Cooling System (ECCS). Although the strategy saves the core, the leaked coolant together with the containment spray system pollutes the containment environment, deactivates electrical devices, and jeopardizes the containment integrity by the release of the generated hydrogen during the core re-flooding. Further, Steam Generators (SGs) would be exposed to excessive thermal stress because of drying out, making use of recovered feedwater too risky because of the primary to secondary side leakage possibility. ECCS in VVER-1000/V446 has low-pressure Hydro accumulators (HAs) that could carry out secondary side F&B through SGs. This feasibility study uses Deterministic Safety Analysis (DSA) to show that the secondary side F&B recovery strategy can provide 6 h after losing Emergency Feedwater (EFW) which is a significant time to recover the failed safety systems. This strategy is more significant, especially if the TLFW accident is followed by the loss of A.C. power, which makes the primary side F&B ineffective, leading to the core meltdown within 2 h. The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) is used to calculate Human Error Probability (HEP) for the primary and secondary side F&B recoveries. Probabilistic Safety Assessment (PSA) reveals that the proposed recovery strategy reduces the Core Damage Frequency (CDF) by one order of magnitude.

Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR

Nowadays, the Occupational Health and Safety (OHS) is more and more recognized as a crucial process to be properly managed and continuously improved by every organization. Primarily addressed to prevent workers’ injuries and diseases, it positively impacts on productivity, competitiveness and reputation as well as it contributes to cost savings in general. OHS management is grounded upon the risk assessment results, on the basis of which defining corrective measures to be taken to reduce risks to acceptable values. In this regard, the paper proposes a Multi-Criteria Decision Making (MCDM) based methodology addressed to the occupational risks prioritization. In order to overcome the shortcomings of the traditional risk matrix, the new methodology considers three evaluation criteria differently weighted by the Analytic Hierarchy Process (AHP) method. Among criteria, one specifically refers to the contribution of human factors to the occurrence of accidents, and a combination of Human Error Assessment and Reduction Technique (HEART) and Standardized Plant Analysis Risk-Human (SPAR-H) techniques is used to this aim. Afterwards, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is applied to rank risks. The proposed approach is implemented in a Sicilian manufacturing company that produces and commercializes wooden products, together with the sale of outdoor furniture. The comparison of the obtained results with the ones arising from previous risk analyses performed by the company highlights the higher ability of the proposed methodology to differentiate risks.

Human error probability quantification for NPP post-accident analysis using Cognitive-Based THERP method

Human Reliability Analysis (HRA) studies human actions from the safety point of view, modeling the most important of them and assessing their probabilities. The Technique for Human Error Rate Prediction (THERP) is a classical HRA approach that models Human Error Probability (HEP) for a specific action regardless of the human cognitive process. Another HRA method is Human Cognitive Reliability (HCR) that expresses the non-response probability as a function of available time regarding the performance type and ignores other affecting factors. In this study, the strengths of the Deterministic Safety Analysis (DSA), THERP and HCR are integrated to form a systematic framework introduced as Cognitive Based THERP (CB-THERP) method to calculate HEP for the Nuclear Power Plant (NPP) post-accident analysis. The calculated values of HEP during the Total Loss of Feedwater (TLFW) accident reveal that a better Human-Machine Interface (HMI) condition reduces HEP. However, operators' higher training level compensates even the worst HMI. In addition, the HMI condition has the greatest impact on an averaged-knowledge Main Control Room (MCR) operator and then an expert and a novice. Dependency almost doesn't influence the worst HMI condition and it much affects highly trained operators when the HMI condition is excellent. Also, the calculated HEP values from the Standardized Plant Analysis Risk-Human Reliability (SPAR-H) and CB-THERP methods for different cases of the scenario have similar trend while THERP calculates a fixed value for them.

Application of a Standardized Plant Analysis Risk-human Reliability Method to Pipeline Inspection Gauge Operations

Application of a Standardized Plant Analysis Risk-human Reliability Method to Pipeline Inspection Gauge Operations

An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’

Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+.We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability.For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk.

Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant

Background: Pigging operation is one of the maintenance activities that is used to check pipeline functionality in operational conditions using a PIG device and high pressure of liquid/gas, which is potentially hazardous. Objectives: The present study aimed at customizing SPAR-H methodology for the pigging operation using Bayesian networks (BNs). It also aimed at identifying and analyzing human errors in pigging operation in a gas transmission company. Methods: The current article was composed of two main steps. In the first step, the SPAR-H BN model was developed using expert-elicited prior probabilities of pigging operation applied to Bayesian network. In this step, CPTs of PSF nodes are constructed using prior probabilities, which are achieved from expert opinion. The CPT of error node is developed using coding process of SPAR-H formula in a simulation node. In the second step, a descriptive study was carried out to estimate the probability of human errors in pigging operation in a gas transmission plant in Iran. First, hierarchical task analysis (HTA) was conducted by walking through the pigging operation and interviews with workers. Next, the SPAR-H BN model was utilized for estimation of human error probability. Results: The developed model was tested on the pigging operation subtasks. In the considered case study, the mean probability of human error was estimated as 0.184. The highest probability of human error was related to “opening the kicker valve for enhancing pressure” subtask. Conclusions: The BNs were helpful to adapt the SPAR-H methodology to the pigging operation using dedicated prior probabilities. Beside that, the probabilities of human error can be updated taking into account the more realistic operational and environmental conditions.

Lessons learned from applying a new HRA method for the petroleum industry

Petro-HRA is a new human reliability analysis method developed for the petroleum industry. The human error quantification step in the Petro-HRA method was adapted from the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method, which was originally developed for nuclear applications. During the Petro-HRA project, emphasis was placed on evaluating and adapting the SPAR-H quantification process to make it more suitable to a petroleum context. In addition, since HRA is relatively new to the petroleum industry, emphasis was also placed on the development of guidance for how to perform qualitative analysis to inform and provide the necessary substantiation for human error quantification and human error reduction. Petro-HRA is thus considered to be a “complete” method. This paper describes the key differences between the petroleum and nuclear industries, which created the need for a new HRA method. An overview of the Petro-HRA method is provided, and we document our observations and experiences from application of this new method in case studies and in commercial practice.

Human Error Probability Quantification using SPAR-H Method: Total Loss of Feedwater case study for VVER-1000

The primary side Feed & Bleed (F&B) recovery strategy is the last device to avoid core damage in the event of loss of Ultimate Heat Sink (UHS) accident. In the context of accident management, besides multiple safety systems’ well-functioning, human factor has a significant role in avoiding the extreme scenarios. In this research, it is tried to quantify Human Error Probability (HEP) of operators’ interference to initiate F&B recovery procedure when the Total Loss of Feedwater (TLFW) accident occurs in a typical VVER-1000 Nuclear Power Plant (NPP) via Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) Method. Among those factors that affect human performance is the available time for the operators’ late interference in order to reestablish natural circulation to remove the residual heat after the reactor scrammed which is computed using the best estimate code RELAP5 Mod 3.2. It is revealed that the diagnosis and the action HEPs of the operators are highly dependent and the combined HEP value is too risky with an unacceptable amount of uncertainty. It is concluded that the accident management procedure must be redesigned so as to eliminate diagnosis and action dependency.

Human reliability assessment in a 99Mo/99mTc generator production facility using the standardized plant analysis risk-human (SPAR-H) technique

Introduction. Reducing human error is an important factor for enhancing safety protocols in various industries. Hence, analysis of the likelihood of human error in nuclear industries such as radiopharmaceutical production facilities has become more essential. Methods. This cross-sectional descriptive study was conducted to quantify the probability of human errors in a 99Mo/99mTc generator production facility in Iran. First, through expert interviews, the production process of the 99Mo/99mTc generator was analyzed using hierarchical task analysis (HTA). The standardized plant analysis risk-human (SPAR-H) method was then applied in order to calculate the probability of human error. Results. Twenty tasks were determined using HTA. All of the eight performance shaping factors (PSFS) were evaluated for the tasks. The mean probability of human error was 0.320. The highest and the lowest probability of human error in the 99Mo/99mTc generator production process, related to the ‘loading the generator with the molybdenum solution’ task and the ‘generator elution’ task, were 0.858 and 0.059, respectively. Conclusion. Required measures for reducing the human error probability (HEP) were suggested. These measures were derived from the level of PSFS that were evaluated in this study.

Operator response modeling and human error probability in TRIGA Mark II research reactor probabilistic safety assessment

This study presents the quantitative part of the analysis of human actions (HAs) in human reliability analysis (HRA) for a research reactor level 1 probabilistic safety assessment (PSA). In responding to the abnormal conditions, type C HAs which are identified in the previous study are elucidated furthermore for their cognitive response element, prior to the quantification of their human error probabilities (HEPs). The HEPs are afterward quantified by mean of Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method. For the type A and B HAs, their HEPs are quantified by using the Technique for Human Error Rate Prediction (THERP) method. Incorporation of the human failure events (HFEs) and their respective HEPs into the PSA models further allow the evaluation of human contribution to the risk associated with the operation of the research reactor. In this study, it is quantitatively shown that human attribute has major contribution to the risk and with the introduction of nominal level of procedure-performance shaping factor (PSF), risk can be significantly reduced.

Modeling Human Reliability in the Power Grid Environment: an Application of the SPAR-H Methodology

Cascading failures, such as the Northeast outage of 2003, can keep millions of people without power for extended periods of time. A combination of simultaneous occurrence of rare events in the physical grid, the human inability to apprehend and act on the real circumstances, and failures in the IT infrastructure result- ing in poor situation awareness may lead to power outages. The formation and propagation of cascading events is nonlinear. There is a precursor phase, in which some system recovery is possible and human oper- ators are able to act and recover failed lines, followed by an escalation phase, in which the number of failed lines increases rapidly, and by a cascading phase that corresponds to the blackout. Although stochastic power grid simulation is an appropriate tool to study cascading events, human intervention, which is mostly ignored in simulation, can nonetheless influence the recovery and the extension of the blackouts. In this pa- per we model the probability of human failure by applying a human reliability modeling approach, the, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H), to the analysis of failure scenari- os narrated by grid operators with different levels of expertise. The SPAR-H methodology is a simplified approach to human error quantification that accounts for individual performance-shaping factors that affect perception, processing and response to events in complex environments. To implement this approach, we started by interviewing system operators from New England and the South East, and took note of the narra- tives of the emergencies that happened during their shift. Next, we applied the SPAR-H methodology to each task and calculated dependency between the cognitive and action-based tasks. We then calculated the final failure probability and compared the empirical evaluation of error probability with what actually oc- curred. Using Monte Carlo methods and the SPAR-H equations, we finally estimated the HEP that can be readily integrated into the stochastic simulation of a physical power grid. With the intention to calculate the HEP specific to each cascading phase, we propose to increase the number of narrated events and classify each, according to a phase (precursor and escalation). We will then improve the model by calculating the frequency associated with each level of severity for each factor, and by creating a probability distribution for each factor, conditional to a specific phase.

Human Error Analysis in a Permit to Work System: A Case Study in a Chemical Plant.

BackgroundA permit to work (PTW) is a formal written system to control certain types of work which are identified as potentially hazardous. However, human error in PTW processes can lead to an accident.MethodsThis cross-sectional, descriptive study was conducted to estimate the probability of human errors in PTW processes in a chemical plant in Iran. In the first stage, through interviewing the personnel and studying the procedure in the plant, the PTW process was analyzed using the hierarchical task analysis technique. In doing so, PTW was considered as a goal and detailed tasks to achieve the goal were analyzed. In the next step, the standardized plant analysis risk-human (SPAR-H) reliability analysis method was applied for estimation of human error probability.ResultsThe mean probability of human error in the PTW system was estimated to be 0.11. The highest probability of human error in the PTW process was related to flammable gas testing (50.7%).ConclusionThe SPAR-H method applied in this study could analyze and quantify the potential human errors and extract the required measures for reducing the error probabilities in PTW system. Some suggestions to reduce the likelihood of errors, especially in the field of modifying the performance shaping factors and dependencies among tasks are provided.

Task complexity as a performance shaping factor: A review and recommendations in Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) adaption

This review is part of the PetroHRA project that aims at adapting human reliability analysis to the petroleum industry. The different elements that make up complexity are reviewed through a thematic analysis which identified 83 complexity elements that were categorized into 13 complexity categories. Six of these were found to be relevant and were included in the “Task Complexity” performance shaping factor which is presented in this paper. The description of “Task Complexity” includes levels and multipliers.

Human Error Data Collection and Comparison with Predictions by SPAR‐H

There is a scarcity of empirical data on human error for human reliability analysis (HRA). This situation can increase the variability and impair the validity of HRA outcomes in risk analysis. In this work, a microworld study was used to investigate the effects of performance shaping factors (PSFs) and their interrelationships and combined effects on the human error probability (HEP). The PSFs involved were task complexity, time availability, experience, and time pressure. The empirical data obtained were compared with predictions by the Standardized Plant Analysis Risk-Human Reliability Method (SPAR-H) and data from other sources. The comparison included three aspects: (1) HEP, (2) relative effects of the PSFs, and (3) error types. Results showed that the HEP decreased with experience and time availability levels. The significant relationship between task complexity and the HEP depended on time availability and experience, and time availability affected the HEP through time pressure. The empirical HEPs were higher than the HEPs predicted by SPAR-H under different PSF combinations, showing the tendency of SPAR-H to produce relatively optimistic results in our study. The relative effects of two PSFs (i.e., experience/training and stress/stressors) in SPAR-H agreed to some extent with those in our study. Several error types agreed well with those from operational experience and a database for nuclear power plants (NPPs).

Comparing SLIM, SPAR-H and Bayesian Network Methodologies

Human factors always affect maintenance performance, and in some cases, it’s critical to systems availability and reliability. Despite such importance, in so many cases, there’s no human reliability method applied to analyze maintenance tasks in order to understand better human factors influence in maintenance performance. There are several human analysis methodologies and regarding human factors, SLIM (Successes Likelihood Methods), SPAR-H (Standardized Plant Analysis Risk-Human Reliability Analysis Method) and Bayesian Net take into account such factors and may be a good approach to minimize human error. In order to propose a human reliability methodology to analyze maintenance tasks taking into account human factors, a case study about turbine star up tasks will be carried out. Therefore, different human reliability methods will be performed based on specialist opinion. Finally, the human error probability as well as drawbacks and advantages from different methods will be discussed to get a final conclusion.

The application of the SPAR-H method in managed-pressure drilling operations

This paper aims to share insights gained in the application of the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) technique applied to a normal-operations scenario for a managed pressure drilling concept. SPAR-H was used to identify the potential for error for each individual task related to the concept. A Systematic Human Error and Prediction Approach (SHERPA) was performed to gain more insight into the qualitative aspects of the operation including the potential consequences for safety should a task be performed erroneously. For this project, the relative Human Error Probabilities (HEP) values of SPAR-H were used rather than HEPs as absolute performance indicators. With this approach, an evaluation of the concept was performed in terms of its potential for human error and a prioritized set of improvements for the concept was presented. Such application of SPAR-H was found to be a useful approach to aiding project managers in directing focus towards those issues that require most attention.