Accidents In Process Industries Research Articles

System dynamics approach for assessing the performance of safety management systems in petrochemical plants

Accidents in process industries, such as chemical, petrochemical, and refinery sectors, often stem from intricate causes. Consequently, safety management systems (SMS) have emerged as crucial mechanisms for accident prevention. In process industries, this is an interrelated and sociotechnical system consisting of numerous interacting components. However, current safety management approaches appear to be limited by linear thinking in terms of enhancing safety performance. Adopting systems thinking that considers the feedback loop and time delay is necessary to improve the efficiency and effectiveness of system performance. This study proposes a model that can predict the performance of SMS in petrochemical plants to manage risks using system dynamics (SD). Methods are proposed to build causal relationships in SMS and to develop a simulation model grounded in SD, facilitating performance evaluation within process industries. The proposed model is applied to an organization as a case study, and scenario-based simulations are performed. The case study results indicate that senior management interests are more influential to safety performance. The model is expected to be able to forecast the performance level of SMS and help decision-makers identify better policies and obtain insights to improve safety.

Dynamic risk assessment of storage tank using consequence modeling and fuzzy Bayesian network

Accidents in process industries cause irreparable economic, human, financial and environmental losses annually. Accident assessment and analysis using modern risk assessment methods is a necessity for preventing these accidents. This study was conducted with the aim of Dynamic risk assessment of tank storage using modern methods and comparing them with traditional method. In this study, bow tie (BT) method was used to analyze the Leakage event and its consequences and model the cause of the outcome, and the Bayesian network method was used to update the probability rate of the consequences. Then, four release scenarios were used. Possible selection and release outcome were modeled using version 5.4 of ALOHA software. Finally, according to the degree of reproducibility of possible consequences and risk number modeling for the four scenarios were estimated. The results of modeling the cause and effect showed that 50 Basic events are effective in chemical leakage and Pool fire is the most probable consequence due to chemical leakage in both BT and Bayesian network (BN) models. Also, the modeling results showed that Leakage 50 mm diameter has the highest Emission rate (80 kg/min) and Leakage of 1 mm have the lowest emission rate. The results of risk assessment showed that the estimated risk number in both models is in the unacceptable range. In this study, an integrated approach including BT, Fuzzy Bayesian networks and consequence modeling was used to estimate the risk in tank storage. The use of these three approaches makes the results of risk assessment more objective than conventional methods. The results of outcome modeling can be used as a guide in adopting accident prevention and emergency preparedness approaches.

Fire and Explosion Economic Losses (FEEL) Index: A new approach for quantifying economic damages due to accidents in hydrocarbon storage sites

One of the areas most affected by the occurrence of fires and explosions is the economy. These accidents lead to direct economic damages, which results in the withdrawal of capital for the reconstruction of damaged areas. In addition, there are other indirect economic losses, such as business interruption and reputational losses. The DOW Fire and Explosion Index (F&EI-DOW) is currently the most complete methodology for quantifying economic losses. Notwithstanding, the F&EI-DOW focuses on quantifying the hazardous potential of process units, and, does not provide an overall criterion for the hazardous potential of the entire industrial site with regard to: the inclusion of the so-called domino effect, the probabilistic risk assessment, the updating of existing risk analysis tools with new emerging Artificial Intelligence Techniques, and the quantification of reputation losses. For this purpose, the development of a new Fire and Explosion Economic Losses-Index (FEEL-Index) is proposed, which integrates the F&EI-DOW with new factors, such as the Damaged Area Factor, the Accident Probability Factor and the Reputation Loss Factor. With regard to the application to a case study, the total losses quantified by the overall FEEL-Index predict a value higher than the one predicted by the F&EI-DOW. The results obtained on an hydrocarbon storage case study show that the application of the FEEL-Index is relevant for increasing the risk perception, as well as providing guidance for prevention, mitigation, and risk management of accidents in process industries.

“Context-Dependent Cognitive Workload Monitoring using Pupillometry for Control Room Operators to Prevent Overload”

Occupational Application Modernization of process plants has replaced traditional machine control with sophisticated computerized control, with the work required of control room operators changing from predominantly physical to cognitive. As a result, excessive cognitive workload during an abnormal condition is not an uncommon situation. Almost 70% of accidents in process industries are the result of human error, which is largely modulated by high cognitive workload. A context-dependent methodology using pupillometry was adopted in this study for workload assessment. We found that workload has a significant effect on task performance and subsequent success in handling an abnormal condition. Incorporating contextual information along with pupillometry-based measures was useful to explore the dynamics of variations in workload in depth. In addition, the proposed methodology is a more reliable method for a dynamic environment in which the situation evolves with operator interactions with the process. Technical : Background: Operator workload monitoring has become an important aspect of system engineering due to ever increasing cognitive demands in the control room. Higher cognitive workload, beyond capability, is directly associated with human error in plant operations. Even a small increase in workload during peak demand may result in human error, which can endanger the safety of a process plant. Purpose: Subjective methods are of limited use due to the requirement for near real-time evaluation of cognitive workload. Applications such as process plant control room require the operator to reiterate and re-represent the situation based on the contextual cues from environment. Hence, our objective was to develop and test a methodology that incorporates contextual information in workload measurement using pupillometry. Method: Participants (n = 44) performed simulated process control tasks. Pupil size was recorded using an eye-tracking device. Recorded pupil size was analyzed based on a methodology that incorporates contextual information from the human-machine interface. Events that present new information about the current state of the process were categorized as critical events. Pupil size variation was adopted to provide an estimate of cognitive workload between consecutive critical events. Results: Cognitive workload was associated with process behavior and operator actions. An increase in cognitive workload for specific events was observed when participant actions deteriorated process conditions. We also observed that overall task performance was associated with the frequency of certain events and corresponding variations in pupillary behavior. Conclusion: Incorporating contextual information provides more detailed insights into the dynamics of variations in cognitive workload. The mental representation of a current situation may vary depending on expertise level. The proposed methodology assesses such dynamic mental representations in the form of frequency of critical events and corresponding changes in the cognitive workload that ultimately determines the likelihood of an operator successfully completing a task.

Multi-hazard risk assessment in process industries: State-of-the-Art

Multi-hazard accidents in process industries, which can cause more severe consequences compared to individual accidents, have gained growing attention from administrators and scholars in recent years. With the development of process industries and the expansion of the urban area, high-risk zones may emerge in densely populated areas. Accurate risk assessment of the multi-hazard accidents in process industries is essential for protecting properties, human life, and the environment. This study reviews past studies on the risk assessment of three types of multi-hazard accidents in process industries: Natech events, domino effects, and concurrent hazards. The development trends of risk assessment of multi-hazard accidents are analyzed and the research gasps of past research are identified. Based on the identified gaps in previous research, future perspectives on multi-hazard research in process industries are discussed. To improve the assessment methods for multi-hazard risks, more advanced basic models and applicative risk analysis methods are required. Considering multi-hazard interactions and other factors are also important for process plants against multi hazards. This study can potentially contribute to developing better risk assessment models of multi-hazard accidents and therefore safer and resilient process industries.

Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident

Fuzzy Bayesian network (FBN) has been widely used for risk assessment of accidents in process industries to deal with complex causality and uncertainty arising from complex interdependence among risk factors, insufficient data and complex environments. The similarity aggregation method (SAM) is a method of aggregating fuzzy opinions considering consensus degree. However, SAM does not take into account the impact of individual differences on consistency, which will bring a certain degree of uncertainty. Therefore, this work proposes an improved SAM based FBN model to better deal with various types of uncertainty. This methodology makes the prediction results of the storage tank accident more accurate and reliable. The result analysis indicates that the improved SAM is of significance to improve the reliability of the input data of FBN. Then, the critical analysis of the root node shows the effectiveness and reliability of FBN in identifying the critical events of the storage tank accident. The proposed method can predict the probability of storage tank accidents, determine the proportion of main contributing factors and identify the critical causes of storage tank accidents more reliably and accurately. It can provide important supporting information for decision-makers to optimize risk management strategies.

Potential benefits of eye tracking within process control monitoring tasks

AbstractStatistically, about 70% of the process industry accidents in the refinery control room are caused by human error. It is necessary to develop a deeper understanding of the interaction between humans and the automated systems. This experiment investigated the effectiveness of Overview Displays in a refinery control room by using eye‐tracking measures. Specifically, control room operators were presented with three Overview Displays that have been adopted by the oil and gas industry under two levels of difficulty for each display. The dependent variables include five eye‐movement metrics that previous studies have related to cognitive processing. The results indicated that Overview Display type significantly affected eye‐movement behavior. The consistency between eye tracking results and the performance results suggesting the possibility that eye‐tracking measures could be used to indicate the performance results. The findings could be used to increase operator assessment effectiveness.

Using Bayesian Networks for Quantifying Domino Effect Probability in a Hydrocarbon Processing Area

Accidents in process industries include fires, explosions, or toxic releases depending on the spilled material properties and ignition sources. One of the worst phenomena that may occur is the called domino effect. This triggers serious consequences on the people, the environment, and the economy. That is why the European Commission defined the domino effect prediction as a mandatory challenge for the years ahead. The quantification of the domino effect probability is a complex task due to the multiple and synergic effects among all accidents that should be included in the analysis. However, these techniques could be integrated with others in order to represent the domino effect occurrence reliably. In this matter, artificial intelligence plays a vital role. Bayesian networks, as one of the artificial intelligence nets, have been widely applied for domino effect likelihood determination. This research aims to provide a guide for quantifying domino effect probability using Bayesian networks in a hydrocarbon processing area. For this purpose, a four-step model is proposed integrating some classical risk analysis techniques with Bayesian networks. Moreover, this methodology is applied to an actual hydrocarbon storage and processing facility. After that, the joint probability can reach 9.37% for the process unit tank 703 which storages naphtha. Hence, safety management plans must be improved in this area for reducing this actual risk level. Finally, this research demonstrates how Artificial intelligence techniques should be integrated with classical ones in order to get more reliable results.

Stakeholder outreach on process safety for process industry using risk based approaches

AbstractFor past decades, the industry and academician realized the core of effective process safety management (PSM) is closely related to good process safety culture. Major accidents in process industry occur at low frequency but the consequences on people, environmental, and assets damage are catastrophic. Previous accidents in process industry recorded many deaths and injuries of facility personnel and public. Hence, many regulations and best practices were introduced to avoid major accidents from occurring and to mitigate the consequences. Risk based process safety (RBPS) Guideline is one of best practices to manage PSM, using risk‐based approaches. When accidents happened, internal and external stakeholders are greatly impacted if it is not mitigated effectively. Thus, element stakeholder outreach which lies in Commit to Process Safety pillars are considered as important elements to protect the stakeholders and promoting good safety culture. This article presents a structured technique for stakeholder outreach using RBPS approaches and a work‐aid model to ease its implementation in process industry. The technique and work‐aid model are beneficial to help the organization managing stakeholders, educating the public on risk management and the importance of embracing safety culture in their life.

Dynamic domino effect risk assessment using Petri-nets

The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.

Review of Process Industry Accidents Analysis towards Safety System Improvement and Sustainable Process Design

AbstractRisk assessment is the tool for maintaining perfect safety management systems and aiding sustainable process design, with hazard identification as the critical step. This step can be executed by past accidents analysis (PAA) to achieve the mentioned objectives. Despite of available analyses, the recurring of accidents identifies the shortcomings in PAA and requires a detailed examination as reported in this review. The intensified exploration of accident information will strengthen both the safety management system at existing facilities and process designing in terms of sustainability.

Chemical reactor inherent safety index at preliminary design stage

The design inadequacy of chemical reactor has caused many major accidents in process industries. The absence of safety analysis for chemical reactor especially in the early design stage is one of the reasons for faulty designs. Inherent safety can be used to perform the safety analysis at preliminary design phases. However, the literature is deficient in reporting inherent safety assessment method for chemical reactors. Therefore, this paper introduces a new indexing method for inherently safety assessment of chemical reactors at the preliminary design stage. Chemical Reactors Inherent Safety Index (CRISI) is based on three sub-indices; chemical index, process index and reaction index. These sub-indices are estimated through scores of numerous parameters in each dimension. For the unacceptable score, the process conditions are changed according to the favorable reaction conditions. CRISI is estimated for all combination of process conditions and lowest CRISI value indicates the inherently safer design of the reactor.

Toward Preventing Accidents in Process Industries by Inferring the Cognitive State of Control Room Operators through Eye Tracking

While modern chemical plants have numerous layers of protection to ensure safety, the human operator is often the final arbiter, especially during abnormal situations. It is therefore not surprisin...

Analysis for Fire Plume Temperature in Developing Area and Radiation Heat Flux Distribution in Small-scale Pool Fire

Abstract Pool fires are the most common burning phenomena that are often reported to cause huge casualties and properties in process industry accidents. Therefore, it is important to deeply understand the combustion characteristic and thermal radiation of pool fires. This paper presents experimental study and theoretical analysis on the fire plume temperature in developing area and thermal radiation distribution of small-scale propane pool fires. The results show that fire plume temperature should be formulated by (z/Q2/5) for z/Q2/5 ranging from 0 to 0.02, in comparison with the constant temperature assumption in literature. The fire plume temperature reaches the maximum value as z/Q2/5 equals 0.02. The Point Source Model and Shokri-Beyler Correlation can relatively accurately calculate the radiant heat flux, while Mudan-Croce Method gives a bad prediction on the thermal radiation in small pool fires.

A risk-based methodology for the optimal placement of hazardous gas detectors

Abstract Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and a majority of process installations have a large number of sensitive detectors in place, the actual operating performance of gas detection systems still does not meet the expected requirements. In this paper, a risk-based methodology is proposed to optimize the placement of hazardous gas detectors. The methodology includes three main steps, namely, the establishment of representative leak scenarios, computational fluid dynamics (CFD)-based gas dispersion modeling, and the establishment of an optimized solution. Based on the combination of gas leak probability and joint distribution probability of wind velocity and wind direction, a quantitative filtering approach is presented to select representative leak scenarios from all potential scenarios. The commercial code ANSYS-FLUENT is used to estimate the consequence of hazardous gas dispersions under various leak and environmental conditions. A stochastic mixed-integer linear programming formulation with the objective of minimizing the total leak risk across all representative leak scenarios is proposed, and the greedy dropping heuristic algorithm (GDHA) is used to solve the optimization model. Finally, a practical application of the methodology is performed to validate its effectiveness for the optimal design of a gas detector system in a high-sulfur natural gas purification plant in Chongqing, China. The results show that an appropriate number of gas detectors with optimal cost-effectiveness can be obtained, and the total leak risk across all potential scenarios can be substantially reduced. This methodology provides an effective approach to guide the optimal placement of point-type gas detection systems involved with either single or mixed gas releases.

Simultaneous Operation (SIMOP) Review: An Important Hazard Analysis Tool

Simultaneous operations (SIMOPs) occur within process facilities when two or more activities occur at the same time and place. They may involve risks that are not identified when each activity is considered by itself. A SIMOP review identifies possible interactions between activities that may adversely impact people, property, or the environment. SIMOP reviews are an important adjunct to the performance of process hazard analyses such as hazard and operability studies. A number of major process industry accidents have involved SIMOPs. Several examples are described. A procedure for conducting a SIMOP review and an example are provided. The development of a plan to address the performance of SIMOPs using the results of SIMOP reviews is discussed and plan contents are identified. © 2016 American Institute of Chemical Engineers Process Saf Prog 36: 62–66, 2017

Assessing the hazards from a BLEVE and minimizing its impacts using the inherent safety concept

Many worlds' major process industry accidents are due to BLEVE such as at Feyzin, France, 1966 and San Juan Ixhuatepec, Mexico City, 1984. One of the approaches to eliminate or minimize such accidents is by the implementation of inherently safer design concept. This concept is best implemented where the consequence of BLEVE can be evaluated at the preliminary design stage, and necessary design improvements can be done as early as possible. Thus, the accident could be avoided or minimized to as low as reasonably practicable (ALARP) without resorting to a costly protective system. However, the inherent safety concept is not easy to implement at the preliminary design stage due to lack of systematic technique for practical application. To overcome these hurdles, this paper presents a new approach to assess process plant for the potential BLEVE at the preliminary design stage and to allow modifications using inherent safety principles in order to avoid or minimize major accidents. A model known as Inherent Fire Consequence Estimation Tool (IFCET) is developed in MS Excel spreadsheet to evaluate BLEVE impacts based on overpressure, radiation heat flux and missile effects. In this study, BLEVE impacts are the criteria used as the decision-making for the acceptability of the design. IFCET is integrated with iCON process design simulator for ease of data transfer and quick assessment of potential BLEVE during the design simulation stage. A case study was conducted to assess of potential BLEVE from a propane storage vessel at the design simulation stage using this new approach. The finding shows promising results that this approach has a potential to be developed as a practical tool.

Domino Effect Analysis, Assessment and Prevention in Process Industries

AbstractDomino effect is a fairly common phenomenon in process industry accidents, which makes many process industry accidents serious and the consequent losses enhanced. Domino effect of the major accidents in chemical cluster is emphasized. Many researchers have studied domino effect in chemical clusters from different perspectives. In the review, we summarize the research from three aspects: The statistical analysis of domino accidents in chemical process industry, the evaluation of domino accidents and the prevention of domino accidents in chemical clusters by game theory. From the analysis, we can find the characteristic of domino accidents such as the time and the location, the origin and causes of domino accidents. The methods of assessing domino effects such as quantitative risk assessment (QRA), Bayesian networks (BN) and Monte Carlo simulation (MCS) are analyzed. The prevention of domino accidents in chemical clusters using game theory is seldom, and there is still much space for improvement in enterprises’ efforts to prevent risk of domino accidents.

Towards predicting human error: Eye gaze analysis for identification of cognitive steps performed by control room operators

Today's plants use highly reliable equipment, state-of-the-art automation and control, and deploy sophisticated safety management regimes so as to make accidents rare. Despite these measures, a number of recent accidents point to the continued need to improve process safety. Various studies indicate that more than 70% of the accidents in process industries originate from human errors. Traditionally, human reliability is accounted for by using Human Error Probabilities, which emphasizes inevitability. In this work, we demonstrate that eye tracking serves as a reliable sensor of various cognitive tasks performed by the operator while managing process abnormalities. Experimental studies conducted on 11 participants reveal characteristic fixation patterns that contain information about their cognitive ability to orient, diagnose and execute recovery actions during abnormal situations. These results hold promise for predicting incipient cognitive failures which in turn would enable proactive measures to prevent human error.

Modified Swiss Cheese Model to Analyse the Accidents

Human and organizational factor (HOF) can lead to accidents in process industries, but at the same time HOF can also be used as a safety barrier in order to optimize the existing resources. This paper illustrates the idea to use the revised Swiss cheese model to study an accidental situation, since this new model can provide a better understanding of the system with respect to automatic and manual safety barriers. Afterwards, accidental scenario (i.e. Top Event) can be analyzed by using the Bow-tie analysis. However, in this paper only the descriptive analysis of accidental scenario has been provided using Bow-tie analysis.