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Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators.

In addition to revealing the cracks in global health care, emergency preparedness, and response systems, COVID-19 also exposed the lack of capacity to run chemical plants safely under such conditions. On 7th May 2020, self-polymerization runaway reaction from an atmospheric storage tank in a polymer facility triggered the release of styrene to the atmosphere, resulting in 12 fatalities and hospitalization of more than 1000 individuals. A similar incident had happened 35 years back at Bhopal involving the release of methyl isocyanate resulting in one of the deadliest process safety incidents to date. Therefore, it is very important to understand the causal factors so that such high-risk incidents can be prevented in future. This paper presents a comprehensive investigative study of styrene gas leak at Vizag with special emphasis on probabilistic risk analysis of the loss of containment. Hazard perception study was performed to understand the possible hazardous scenarios in bulk styrene storage facilities for preventing such catastrophic recurrences. Energy barrier analysis was performed to study the inadequacy of pro-active and reactive barriers with respect to the accident case study. The analysis also considers the escalation factors resulting from extremities of COVID-19 lockdown. The self-polymerization reaction that resulted in toxic styrene dispersion was preventable owing to the advancements in safety engineering and loss prevention since Bhopal Gas Tragedy (1984). Based on the investigative analysis, it can be pointed out that this accident would have occurred even in the absence of COVID-19 restrictions, mainly due to negligence and complacency shown towards safety by the company’s management.

Dynamic risk assessment is a main research task of process safety. One of the main methods of dynamic risk assessment is to measure the distance between the real-time state and its threshold (dangerous state). Due to the influence of temperature, pressure and other factors, the dangerous state will often be expanded into a “dangerous range” of states. At present, most of the “dangerous range” are determined subjectively or according to experiences. Based on the safety structure theory, instability domain as the scientific term of "dangerous range" is proposed, and then its mathematical expression is developed in this paper. The safety structure composed of an atomic activity factor (AAF) and an atomic environmental factor (AEF) is regarded as the smallest unit to analyze safety issues in safety structure theory. When the state of a safety structure falls into its instability domain, the state of intrinsic factor will fall into the instability domain of the safety event. This is a sufficient condition to cause an accident. In this paper, the attribute of intrinsic factor about a safety event is determined by developing the performance extension membership function of the intrinsic factor, thus the mathematical expression on instability domain of the safety event is established. Taking the instability domain of safety event as the domain of the inverse mapping of state relationship functions among environmental factors, and then the mathematical expression on atomic environmental instability domain (AEID) of safety structure is established. Taking AEID as domain of the inverse mapping of state relationship function between AAF and AEF, thus the mathematical expression on atomic activity instability domain (AAID) of safety structure is established. The instability domain of safety structure is composed of AEID and AAID together. The mathematical expression on instability domains can provide theoretical basis for the dynamic risk assessment, prediction and active control of safety accidents.

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator’s attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator’s learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

A vertical spill fire is one of the typical phenomena in fire scenes, which usually occurs with liquid fuel usage and storage and may pose a threat to process safety. In this study, a number of transformer oil continuous spill fire experiments were carried out on a steel experimental setup, in order to obtain the vertical spill fire characteristics of transformer oil or fuels that have similar properties as transformer oil. The flame shape, temperature and radiation data in the process of flame propagation were recorded. According to the change of the burning area, the formation and development process of a vertical spill fire can be divided into four stages. The relationship between burning area and fuel leakage rate in the quasi-stable combustion stage is analyzed and a prediction model for the burning area of a transformer oil vertical spill fire is established. The radiation model of a transformer oil vertical spill fire is established as a function of leakage rate. Based on heat transfer analysis, a mass burning rate model is derived and verified by experimental data.

International oil and gas corporations operating in Brunei may apply process safety management (PSM) and analysis techniques, resulting in varying approaches and measures to address process safety issues. Global corporations may have developed their own process safety standards while smaller firms employ established ones. This research compares the local PSM systems and standards with international ones to determine which employers face the most difficulties in implementing or increasing process safety inside their organizations. This study found that Occupational Safety and Health Administration (OSHA) regulations are used by 30% of local users in downstream operations. Common challenges encountered by local users are management/leadership commitment to process safety (11.9%), mechanical integrity and management of safety critical devices (5.3%), management review and intervention for continuous improvement (4.9%), communication amongst workers (3.8%), management of change (3.8%), operational control, permit to work and risk management (3.8%) and incident reporting (3.8%).

For the characteristics of high frequency and strong suddenness of dynamic disasters in deep coal mines, the traditional detection and evaluation techniques applied to shallow coal mine failed to accurately judge the risk degree of disasters. Therefore, it is of great significance to use advanced detection technologies and appropriate evaluation methods to improve the accuracy and efficiency of risk assessment in the process of coal mining. The present paper applies the rapid detection and multi-source dynamic detection technologies used in the field of mining with the purpose of improving the reliability of detection technologies for typical dynamic disaster. In this study, the data fusion technology was used to analyze data obtained from laboratory experiments, engineering survey, detection and historical data, so as to form the final dynamic and static indicators. Then, the new combined evaluation models with time series of coal and gas outburst as well as rock burst were established respectively to carry out the comprehensive risk evaluation using the least-squares method and the time-varying weight method. After the comprehensive analysis on the results of the above two evaluation models, the risk areas of the typical dynamic disasters were judged and classified. Finally, the evaluation models were coded to build an early-warning software platform that could achieve automatic evaluation and the actual 3D visualization of coal mining areas. The early-warning software platform was applied to risk assessment of dynamic disasters in Gengcun Coal Mine in Yima City, Henan Province, China. The results of the 6-month experiment showed that the risk assessment accuracy and reliability of the proposed evaluation models was 100% and 90% respectively, which indicates that the newly developed approach is reliable and can be recommended for applying in more coal mines to improve the process safety risk control.

Emergency response planning associated with written procedures play a key role in the process safety to ensure that accident can be properly managed. Although the extensive use of digital techniques have significantly reduced the accident risk by early warnings and operation guidance, the Fukushima nuclear disaster reminds us that the unexpected events may still exceed existing system capability to indicate appropriate responding measures. A novel digitalized approach is hence required for planning decision support. This article presents a joint utilizing of two modeling and analysis methodologies, which are complementary to each other to provide comprehensive insights for decision-making in the accident emergency response. Multilevel Flow Modeling (MFM) is applied to develop an intelligent reasoning system, which can harness plant’s qualitative functional knowledge to search available equipments and corresponding action sequences potentially being able to manage the accident. Go-Flow is a success-oriented reliability analysis method specially suitable for quantitative risk assessment in the situations involving configuration changes. The underlying configuration of each MFM-driven emergency response measure will update the understanding of goal achievement and modify the Go-Flow chart, by which plan’s effectiveness of risk reduction can be evaluated. Moreover, Go-Flow is also used to conduct the risk matrix assessment for identifying critical components, which can provide a reference for effective assignment of resources in the long-term accident management. This work is expected to strengthen operator’s ability of emergency response planning from perspective of both measure development and optimization. An accident scenario similar to what occurred in the Fukushima Daiichi nuclear power plant has been used to demonstrate the application of the propose approach.

Learning from past incidents is critical to achieving and maintaining high process safety performance. Accident and incident records provide one way for learning; however, these are usually in the form of unstructured texts, which makes analysis difficult. Recently, text mining methods based on supervised learning have been proposed for analyzing accident reports; however, they require an impractically large number of labeled records as training examples. This paper proposes an automated, semi-supervised, domain-independent approach for analyzing accident reports. Given a set of user-defined classification topics and domain literature such as handbooks, glossaries, and Wikipedia articles, the method can identify domain-specific keywords and group them into topics with minimal expert involvement. These keywords and topics can then be used for various data mining purposes, including classification. The proposed approach is demonstrated using two different case studies across domains: (1) in aviation to identify the stage of flight when an accident occurs, and (2) in the process industry domain to identify the cause of pipeline accidents. The average classification accuracy of the proposed method was 80% which is comparable to that of supervised learning methods. The key benefits of this approach are that it can generate domain-specific predictive models with limited manual intervention.

Methyl chloride is the starting raw material for chloromethanes and the demand for chloromethane is increasing steadily in the world market. Continuous improvement in product quality, reducing the product cost, improving the operation efficiency, minimizing the environmental emissions, and enhancing the process plant safety are some of the challenges or constraints facing by the chloromethane industry. In this research work, an attempt has been made to address these challenges using process systems engineering. As a first step, a process simulation model is designed conceptually using the Aspen HYSYS V11 for the production of 99.9 % pure methyl chloride from the methane chlorination route. Energy analysis is performed for the effective utilization of the utilities by adding one new heat exchanger. This modification reduced the utility cost by 31.17 % and recovered the 13028 kW of energy from the process with a payback period of 0.8759 years. Energy savings reduced the product cost by 3.26 %. Heat integration reduced greenhouse gas emissions by 16.5 %. Process plant safety is improved by using depressurization studies. Safety calculations are performed for the storage tank protection and the tank is used for the storage of methyl chloride product. Sizing calculations are performed for safety valves to vent off excess vapor through pressure safety valves to protect the equipment against overpressure scenarios. Process design engineers and process safety engineers can use the methodology developed in this study to design energy-efficient, economically feasible, and environmentally safe chemical process plants.

Recently, big data and machine learning based damage detection methods to support risk management of offshore facilities have received great attention, compared to traditional modal parameters-based methods. This paper illustrates the application of deep learning methods in damage detection of offshore platforms using measured vibration response of the structures subjected to random excitations. The numerical example of a jacket-type offshore platform under random wave excitation is applied to verify the applicability of convolutional neural network (CNN), long short-term memory (LSTM) networks, and CNN-LSTM method. The comparison of the three approaches are conducted in terms of accuracy and efficiency of damage localization and severity estimation for the simulated damage cases. In addition, the random decrement technique (RDT) for data preprocessing is used to improve the capability of damage detection of the three deep learning methods in noisy conditions. Moreover, the proposed RDT combined with the deep learning methods are applied to laboratory tests of a jacket platform model under random loading produced by a shaking table. Minor and major damages at different locations are discussed. Results show that the proposed combination method has an outstanding performance in structural damage detection even in noisy conditions, and also has great potential application in industrial process safety and operational risk management.

Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptoms as early as possible. In recent years, fault prognosis approaches have led to the remaining useful life prediction. Therefore, in a process system, advancing prognosis approaches will be beneficial for early fault detection in terms of process safety, and to predict the remaining useful life, targeting the system's reliability. In data-driven models, early fault detection is regarded as a time-dependent sequence learning problem; the future data sequence is predicted using the previous data pattern. Studying recent years' research shows that a recurrent neural network (RNN) can solve the sequence learning problem. This paper proposes an early potential fault detection approach by examining the fault symptoms in multivariate complex process systems. The proposed model has been developed using the Convolutional Neural Network (CNN)- Long Short-Term Memory (LSTM) approach to forecast the system parameters for future sampling windows' recognition and an unsupervised One-class-SVM used for fault symptoms' detection using forecasted data window. The performance of the proposed method is assessed using Tennessee Eastman process time-series data. The results confirm that the proposed method effectively detects potential fault conditions in multivariate dynamic systems by detecting the fault symptoms early as possible.

OCI Nitrogen, one of Europe's largest fertilizer producers, is investigating the extent to which it is possible to take targeted measures at an early stage and stop the development of major hazard accident processes. An innovative model has been developed and recently explained and elaborated in a number of publications. This current paper contains a validation of the model by looking at the BP Texas City incident in 2005. The bowtie metaphor is used to visually present the BP Texas City refinery incident, showing the barrier system from different perspectives. Not only is the barrier system looked at from its trustworthiness on the day of the incident but also from the perspective of the control room operator, and from a design to current standards of best practice. The risk reductions of these different views are calculated and compared to their original design. In addition, evidence and findings from the investigations have been categorized as flaws and allocated to nine organizational factors. These flaws may affect the barrier system's quality or trustworthiness, or may act as ‘accident pathogens’ (see also Reason, 1990) creating latent, dangerous conditions. This paper sheds new light on the monitoring of accident processes and the barrier management to control them, and demonstrates that the BP Texas City refinery incident could have been foreseen using preventive barrier indicators and monitoring organizational factors.

Accidental fires result in extensive loss of property, damage to the environment and a large number of fatalities every year. When fuel collects in the form of a pool and burns, it is termed a pool fire. Pool fires may burn in isolation – Non-interacting or as Single Pool Fires (SPFs) – or flames from multiple pool fires may interact with each other and burn in what is termed as Multiple Pool Fires (MPFs). Even as single pool fires (SPFs) are difficult to control, interacting flames due to multiple pool fires (MPFs) are even more difficult to extinguish. Compared to SPF, MPFs tend to have much higher flames and are reported to have extraordinary thermal intensity. Accidental pool fires in warehouses, commercial and residential buildings may interact, giving rise to interacting pool fires (MPFs). The commonly used technique to suppress fires in enclosures such as warehouse, commercial or residential buildings is to use sprinklers. Hence, this study reports the numerical simulations performed to analyse the effectiveness of water-mist sprinklers in extinguishing MPFs. The related CFD coding and simulations were performed using Fire Dynamics Simulator (FDS), FDS tools like Pyrosim (pre-processor) and Smokeview (post-processor) packages. The effect of the ratio of the separation distance between the pools (S) to the diameter (D) on the overall fire suppression efficiency of the water-mist sprinkler as a function of the water mist flow rate, the height of the ceiling, and sprinkler particle size are vigorously analysed in this work. The analysis presented in this article will mainly help to resolve problems associated with the effectiveness of mist-sprinkler in warehouse fires regarding process safety viewpoint.

This work involves the application of process safety concepts to other fields, specifically bow tie analysis and inherently safer design (ISD) to COVID-19. An analysis framework was designed for stakeholders to develop COVID-19 risk management plans for specific scenarios and receptor groups. This tool is based on the incorporation of the hierarchy of controls (HOC) within bow tie analysis to identify priority barriers. The analysis framework incorporates inherently safer design (ISD) principles allowing stakeholders to assess the adequacy of controls along with the consideration of degradation factors and controls. A checklist has also been developed to help stakeholders identify opportunities to apply the ISD principles of minimization, substitution, moderation, and simplification. This work also considers barrier effectiveness with respect to human and organization factors (HOF) in degradation factors and controls. This paper includes a collection of bow tie elements to develop bow tie diagrams for specific receptor groups and scenarios in Nova Scotia, Canada. The pandemic stage (At-Peak or Post-Peak) and its influence on different scenarios or settings is also considered in this work. Bow tie diagrams were developed for numerous receptor groups; bow tie diagrams modelling a generally healthy individual, a paramedic and a hair salon patron contracting COVID-19 are presented in this work.

Operators of major hazard chemical sites increasingly face the problem that their assets are ageing. This paper highlights the major hazard aspects of ageing, particularly in the Netherlands where there are around 400 major hazard chemical sites, many coming to the end of or exceeding their design lifetime. Targeted inspections find failures in the safety management of ageing and every year there are ageing related accidents. Dutch investigated major accidents are analysed in a bow-tie structured database called Storybuilder, which contains 83 major accidents resulting from material degradation, this being 25% of all accidents in the database. The paper provides unique details on the accident causes and the difficulties operators have in foreseeing the outcomes. It explains the reasons for taking a holistic approach to modelling, which considers management, human and technical aspects. The analysis results provide information on the safety barrier, barrier task and barrier management failures. A detailed ageing accident scenario is also illustrated within the holistic model. Two prevention approaches are suggested. One uses scenarios as a basis for identifying the necessary prevention measures. The other results from a Dutch multi-disciplinary maintenance programme concerning smart maintenance, a shared initiative approach of companies looking for innovative solutions. The conclusion is that a scenario-based approach is needed for identifying currently unanticipated material degradation causal events and that, given the condition of ageing assets, a shift to condition-based maintenance, combined with technical, organisational and cultural changes, underpins the future approach to physical ageing.

Recently, a new industrial relevant wastewater (water/Cyclohexane/sec-butyl alcohol) separation process with vapor recompression and thermal coupling was proposed as the conventional homogeneous extractive distillation. This highly integrated and interactive multivariable process presents great challenges to the dynamic controllability and process safety analysis. In this paper, based on the concept of “simultaneous design”, the newly proposed process is systematically investigated from the perspective of process improvement, plantwide control and safety analysis. Firstly, for energy conservation and CO2 emission reduction, the new complex process is further improved by applying the features of heterogeneous distillation. And a modified heterogeneous distillation process with heat pump is proposed, which can save reboiler duty about 16.8 % and save compressor electricity up to 67.7 %, while the reduction of CO2 emissions is 23.5 %. Secondly, for the efficient heterogeneous distillation process, an effective and robust plantwide control strategy is developed and tested under large throughput and feed composition disturbances. Last, with the dynamic simulation assisted HAZOP, a quantitative safety analysis is conducted, which can help experts to consider the nature, scale and response time of hazards and identify the potential propagation of deviation between different units, and then propose more specific or accurate safety recommendations correspondingly.

In our previous study—area-partition-involved collaborative patrolling (APCP) game, we proposed a cooperative patrolling game by utilizing multiple patrol vehicles to protect the chemical cluster. Except for fixed countermeasures like intrusion detection, the APCP game was proved to reduce the security risks of chemical clusters being attacked by terrorists effectively. However, some general assumptions (i.e., complete information game and perfect rational players) in the game model make it difficult to be applied in reality since the game model is not robust enough to deal with some common uncertainties on attackers. In this paper, we extend the APCP game model by considering the uncertainties of the game model from two aspects: (1) One is the bounded rationality of attackers. Attackers may adopt the e-optimal response strategy without maximizing their payoffs strictly. In this circumstance, we propose the bounded rational APCP game solver. (2) The other is the uncertainties of the defender’s estimation on the attacker’s parameters. Some parameters regarding attackers are changed from exact values to distribution-free intervals. Correspondingly, we propose the interval APCP game solver. Through extensive simulations on a chemical cluster (i.e., the Antwerp Chemical Cluster), results of the case study indicate that when considering two types of uncertainties mentioned above, the defender’s payoff will be slightly reduced, whereas the more robust patrolling routes are more easily convinced and adopted by the management department of chemical clusters and improve the process safety of chemical clusters.

Accurate and advanced early warning of coal and gas outburst is an important means to ensure the process safety of coal mining. It is a difficult problem to realize the reliable early warning of coal and gas outburst because the cause of the complex causes and disaster-causing mechanism has not been clearly defined. At present, the traditional early warning models have some problems, such as low degree of information fusion, non-dynamic decision-making, and short early warning time. To optimize the early warning time and improve the process safety of tunneling, a new early warning model is proposed. This new model is a multi-source information fusion dynamic early warning model based on combining Autoregressive Integrated Moving Average (ARIMA) and the Transferable Belief Model (TBM). The ARIMA described the changes of different types of sensor data with spatial heterogeneity over time, and the TBM fused different types of sensor data and makes dynamic decisions to achieve early warning of Coal and gas outburst. Finally, Acoustic Emission (AE), Electromagnetic Resonance (EMR), and Gas concentration data of NO.12223 conveyor roadway of Jinjia coal mine from August 3, 2017, to August 7, 2017, were used as the experimental data set. The proposed model was applied to the NO.12223 conveyor roadway of Jinjia Coal Mine to verify the dynamic early warning for the risk of coal and gas outburst. The results showed that the gas gush out with tunneling is identified 1 and 38 min in advance. It was about 9 min earlier than the earliest response EMR signal obtained by the linear regression prediction model of a single indicator used in the original monitoring and early warning system. The research results are of practical significance to optimize the time of early warning of coal and gas outburst and improve process safety risk control in coal mine production.

Process monitoring is essential and important strategy for ensuring process safety and product quality. However, due to the nonlinear characteristics and multiple working conditions in process industries, the traditional process monitoring method cannot be effectively applied. Therefore, we propose a novel process monitoring framework, termed as mixture enhanced kernel canonical correlation analysis framework (M-NAKCCA). The innovations and advantages of M-NAKCCA are as follows: 1). The traditional CCA method is re-boosted as a new method, M-NAKCCA, to better nonlinear fault detection. Also, a matter-element model (MEm) is assimilated into M-NAKCCA to make the information more refined. 2). To overcome the curse of dimensionality that usually occurs in the high-dimensional dataset, M-NAKCCA uses the Nystrom approximation technology to compress the kernel matrix. Moreover, the T2 control chart is reconstructed and the corresponding control upper limit is re-configured to improve the method sensitivity and to better the fault detection performance. 3). The proposed M-NAKCCA framework is firstly used to monitor a wastewater treatment plant (WWTP) and chemical plant with diverse process behaviors. The experimental results showed that the M-NAKCCA framework achieved the best performance for both of case studies.

The fatal consequences of industrial incidents have made evident the need for suitable tools to develop inherently safer process design options. Traditionally, in a process design project, the evaluation of safety aspects is left for analysis after the detailed design has been completed. This approach leads to the use of control loops, barriers and protection layers as the only ways to prevent incidents and to reduce the possible outcomes. An alternative to this approach is the application of the concept of inherent safety, which was introduced to set up several principles that aim to enhance process safety by eliminating, avoiding or minimizing sources of risk. In this work, we present a comparison of different safety metrics in their role to evaluate the risk associated with a given process design. The indices selected for consideration are the Dow’s fire and explosion index (F&EI), the fire and explosion damage index (FEDI), the process route index (PRI) and the process stream index (PSI). All these indices use different input information and their outcomes have different rankings. The metrics were applied to an ethylene production process from shale gas to identify hazard levels, and the location of streams and pieces of equipment that pose the highest hazard within the process. An evaluation of the indices in their capability to track design changes in operating conditions aiming to improve the safety level of the process was developed. To perform the assessment of the safety metrics in a more extensive manner, an uncertainty analysis based on a Monte Carlo simulation framework was implemented and compared to the traditional use of single-value design variables. Within this context, an insightful assessment of uncertainty’s effect on process safety characteristics was achieved because of the identification of ranges of safety-relevant performance outcomes (zones of risks and opportunities) that can be probabilistically characterized. The application of the approach to the ethylene process showed how some indexes are better suited to capture the hazard characteristics associated with the process when changes in the operating conditions of most hazardous section were implemented. The methodology can be extended to other processes of interest, and may serve as a basis for the safety and process design community to propose adjustments in the structure of safety indices based on a better understanding of their performance and reliability as part of broader efforts towards their continuous improvement and refinement.

Improved tools are needed to manage major accident hazard of progressively more complex offshore oil&gas systems in environmentally sensitive areas. Inherent safety principles provide a strategic opportunity to reduce major accident hazards since the early design phase, but a suitable metric to orient safer design choices is needed to apply such principles intro practice. This study aims at providing a systematic approach to the assessment of the hazard profile of alternative process designs in offshore oil & gas production facilities. A novel methodology providing a ranking of inherently safer solutions in conceptual design is described. The methodology is able to highlight the different contributors to the safety profile of the offshore oil & gas production system, linking them to the specific features of the design. The proposed approach, based on multi-criteria Key Performance Indicators (KPIs), addresses different targets (people, assets, environment) and provides a quantitative assessment of the safety score, accounting for both the possible accident consequences and their credibility. An application to a case study concerning an offshore facility for gas production is discussed to demonstrate the potential of the methodology.

Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods.

The first European Loss Prevention Symposium under the auspices of the European Federation of Chemical Engineering (EFCE) took place in 1974 in the then new Aula of the Delft University of Technology in the Netherlands. After the triennial symposium over a period of 45 years having been organized in several European cities, it returned in 2019 to the Delft Aula. Authors of this paper are members of EFCE Loss Prevention Working Party organizing the LP Symposia and after nearly half century, it is therefore worthwhile to look back and also to look forward, following the evolution of process safety. This paper presents an impression of the changes in process safety, risk management approaches, and methods over that 45-year period, while at the same time the last symposium contributions are briefly reviewed and a few highlights, called breakthroughs, are identified.

Chemical accidents occur frequently in China owing to inadequate process safety risk management and control in warehouses. Real-time dynamic risk assessment can identify stored process risks and reduce the accident probability. The support vector machine (SVM) is an effective dynamic risk assessment method. To improve the dynamic risk assessment performance of the SVM model, the electrostatic discharge method (ESDA), which has a strong optimization ability, was used to optimize the model parameters. An improved mixed kernel (NP mixed kernel) that was a linear combination of the novel radial basis function and polynomial kernel was constructed, and an intelligent assessment model of the warehouse fire dynamic risk based on the ESDA and improved SVM (ESDA-NPSVM) was proposed. The experimental results indicated that the proposed model had excellent performance for the dynamic risk assessment of fire accidents in Class A hazardous chemical warehouses, suggesting that it is useful for practical applications.

Multi-criteria Decision Making (MCDM) is widely used for problem-solving in various research fields and has shown good results in risk analysis as well. However, given the gaps found in previous studies in the context of risk analysis, this article proposes and conducts a novel MCDM-based HAZOP analysis: a hybrid methodology with an MCDM approach, Strategic Options Development and Analysis (SODA), and Intuitionistic Fuzzy Sets (IFS). We combine their advantages by using SODA with Intuitionistic Fuzzy Cognitive Maps and by reducing the inherent uncertainty and subjectivity of HAZOP with an IFS-based MCDM sorting algorithm. To demonstrate the usefulness and flexibility of this approach, a real application with a continuous pyrolysis system was conducted, and a comparative analysis was made of the main related studies to demonstrate the advantages of the approach now proposed. On applying it, 40 hazards were identified (seven more than a previous analysis conducted in 2016), and only 3 (7.5 %) of them required special attention. Among the positive points highlighted by the team, the most cited were the resource-saving, greater focus and objectivity, and a more realistic perception of hazards. In particular, we show that our methodology is more consistent and appropriate than those in previous related articles. To assist future users, we provide a framework for the application.

Nonlinear dependencies among highly correlated variables of a multifaceted process system pose significant challenges for process safety assessment. The copula function is a flexible statistical tool to capture complex dependencies and interactions among process variables in the causation of process faults. An integration of the copula function with the Bayesian network provides a framework to deal with such complex dependence. This study attempts to compare the performance of the copula-based Bayesian network with that of the traditional Bayesian network in predicting failure of a multivariate time dependent process system. Normal and abnormal process data from a small-scale pilot unit were collected to test and verify performances of failure models. Results from analysis of the collected data establish that the performance of copula-based Bayesian network is robust and superior to the performance of traditional Bayesian network. The structural flexibility, consideration of non-linear dependence among variables, uncertainty and stochastic nature of the process model provide the copula-based Bayesian network distinct advantages. This approach can be further tested and implemented as an online process monitoring and risk management tool.

The term “Industry 4.0″, which refers to a fourth industrial revolution, is a recent neologism. The rise of digital technology including, artificial intelligence, the Internet of things and networks, “smart” and responsive devices, etc … is more and more omnipresent. In solving the related challenges of Industry 4.0, it is necessary to improve the involvement and training of employers and employees. This is particularly true for their education in process safety. Two surveys were submitted to the relevant stakeholders. Their results showed absence of industry 4.0 topics in the current courses on process safety. On the other hand, a review of the master's degree programs available in Europe revealed the recent emergence of new highly specialized courses in some of the topics of industry 4.0. The definition of “Safety 4.0″ leads to recommend two potential global training pathways, with one corresponding to the requirements of Industry 4.0 & Safety 4.0 intersections, recommended within the framework of pedagogical taxonomy models.

Resilience is an emergent property of a system, which changes with various internal and external factors. Resilience is also a hidden property of a system that cannot be observed. Thus, experiments should be performed for a given system to measure its resilience. However, physical experiments are practically impossible. Inspired by the tensile test for the stress-strain curve in Material Science, this paper proposes a virtual experiment for measuring system resilience and applies it to a chemical process system. The physical parameters of system resilience of a process system are mapped to those of material resilience. A process system is viewed as a 'specimen' in this experiment. The system performance variation caused by disruptions is seen as the displacement of the specimen caused by the applied load. In absorption phase, the decrease speed of system performance is determined by the failure rate of components under disruptive condition. Response time, including fault diagnosis time and resource allocation time, is used to represent adaptation ability. Restoration ability depends on repair rate of components. For simplicity purpose, the proposed method is applied to resilience assessment of a release prevention barrier system used in the Chevron Richmond refinery crude unit and its associated upstream process.

This article presents a bibliometric analysis and mapping of the Chinese process safety research, focusing on the contributions made in core process safety journals and on the influences of international collaborations and knowledge sources on the developments of this research domain. Collaboration networks, term co-occurrence networks, and co-citation network were analyzed to identify trends, patterns, and the knowledge distribution of the Chinese research on process safety. Work to data has been clustered mainly on safety of chemical processes, fire and explosion, and risk management and accidents. Chinese research contributions are concentrated in only few journals, while the corresponding intellectual base draws on the wider literature focused on understanding and modeling phenomena, and on the broader risk research literature, although to a lesser extent. While various foreign authors are highly cited by Chinese authors, only very few direct collaborations with international scholars are identified. The results are used as a basis for a discussion on future research directions and developments for the community. Increased focus on uncertainty treatment and handling of black swan events, risk evaluation and economic aspects of safety decisions, interorganizational risk management, road and maritime transport of hazardous substances, risk perception and communication, and integrated safety and security assessment, are highlighted as fruitful directions for future scholarship. It is hoped that the insights obtained from this work can facilitate new and consolidated collaborations, as well as further invigorate the Chinese process safety domain, ultimately contributing to improved safety performance of process industries in China and elsewhere.

Hydrofluoroolefin 1234ze (1,3,3,3-Tetrafluoropropene) is a fluorinated hydrocarbon introduced as a low global warming potential alternative for HFC-134a (1,1,1,2-tetrafluoroethane) and is classified as mildly flammable (A2L). Definition of appropriate engineering controls and electrical area classification requires accurate knowledge of the flammable properties of a material. Gaps and conflicts in published flammability data for HFO-1234ze are a concern for defining safe handling practices and appropriate layers of protection. The data available in literature has been compiled and analyzed to understand the sources of the data variability and the conditions under which HFO-1234ze will pose a flammability hazard. New data on the minimum ignition energy at 25C, flammable limits at 25C, and maximum experimental safe gap at 40C is also reported here and discussed in context with the large body of data from literature. It is proposed that much of the data measured at ambient conditions in the absence of water vapor does not effectively measure the true limits of flame propagation. Additionally, it appears that ignition limitations, and potentially other design factors, in the current standard test methods may result in the inaccurate measurement of flammable limits for HFO-1234ze under some conditions. It is concluded that HFO-1234ze can be safely handled when appropriate engineering controls are implemented based on a thorough process safety risk assessment.

Preventive maintenance (PM) is an essential strategy to ensure the integrity of safety barriers and process safety on offshore installations. However, determination of the maintenance interval is challenging. Inadequate maintenance is likely to increase the unreliability of safety barriers and major accident risk, while excessive maintenance may increase personnel exposure and operational risk. In addition, it is highlighted that the reduction of maintenance cost should also be taken into consideration. In this study, a new integrated methodology is proposed to determine the maintenance interval of a specific group of safety barriers, which require periodic testing. Specifically, the study deals with the trade-off between risk increase and reduction associated with maintenance, and optimizes the allocation of maintenance cost. It aims at minimizing the total risk level whilst reducing the maintenance cost. The dynamic data model is established first to predict the state and trend of risk level for the safety barriers. Then, the classification model is established to classify the risk level and optimize the allocation of maintenance cost. Finally, the maintenance decision model is established to balance the maintenance-related risks. The proposed methodology is tested by a case study which is to determine the recertification interval of PSVs on a specific offshore installation on the Norwegian Continental Shelf (NCS). It is demonstrated that the proposed methodology is effective in determining the PM interval. The methodology is also useful in minimizing the total risk level of the safety barriers and reducing the maintenance cost per unit time.

Process Safety Information (PSI) provides the fundamental information necessary for developing the elements of Process Safety Management (PSM). We report the PSI management deficiencies found in PSM audits of more than 70 plants. It should be noted that the PSM audits were not limited to OSHA requirements. The deficiencies found were frequently encountered in the management of the PSI element. The chemical hazards information provided on the Safety Data Sheets (SDS) are not sufficient and may not be appropriate for the process condition used; therefore, the chemical hazards information should not be limited to only the SDS. Although the information provided by the SDS satisfies the OSHA requirements, it is suggested that management further develop the chemical hazards information necessary to ensure the safety of the process. Process incidents resulting from undesired reactions are more frequent than from the desired reaction. Therefore, in terms of safety, the process chemistry of the undesired reactions is essential information for the process technology. However, such undesirable reactions are not identified, and their process chemistries are lacking for most processes. The frequently encountered problem for the information of the process equipment is the inconsistency between the piping and instrument diagram and the actual process. Summarily, the necessary information of the PSI should be developed based upon the characteristics of the process.

Process safety and risk management remain a significant challenge for the process and manufacturing industries. Digital systems have been applied over many decades to assist in process safety management throughout the lifecycle of a process plant. There has been much hype in recent years regarding Industry 4.0, digitalization and digital twins regarding the transformative potential that exists within these technologies to improve operational performance and reduce process safety accidents. In this article, a fundamental systems thinking approach is applied to the implementation of the digital twin within the process industries. The importance of having a standardized language and ontology, such as ISO15926, enables the use of reasoning engines and the ability to interconnect models and systems across the process and product lifecycle. We discuss use-cases and forms of the digital twin to improve safety within the process industries. A specific focus shows how an operator training simulator and its embedded dynamic models are applied within this environment. The article concludes with a summary of process safety related opportunities and threats associated with the application of digitalized dynamic models in industry.

The dependency of the society on the hydrocarbon as an energy source has increased tremendously, leading to the rapid development of this process industry. A fire accident that occurred on the 6th of July 2016 at a petrochemical complex plant in the southern part of Iran, Mahshahr petrochemical zone, has called for a more robust and all-inclusive efforts toward ameliorating and forestalling future occurrence. The on-site investigations concluded that the fire was triggered by the leakages through the ruptured blind flange gasket in the pipeline. Thus, certain inquiries on the development of robust process safety technologies gave useful insight into those capable enough to identify and handle various uncertainties in the short and long time basis, to forestall catastrophic accidents. Therefore, it is worthy and pertinent to ascertain whether process safety technology is developing correspondingly at the same pace with the process industry. Are the correct things done in the right way? If yes, then why do these catastrophic accidents keep happening? If no, how can these uncertainties in the process be properly and adequately handled, contained and managed? Failure to provide adequate and incontrovertible answers to these questions toward taking uncompromising safety actions is an invitation to more accidents in the near future. In this study, explanation on how to identify and cope with various uncertainties in process safety science is provided through learning from a real case study of a fire accident that occurred in the aforementioned petrochemical plant.

The modified Stoessel criticality diagram was present in this research. The final temperature under adiabatic condition was considered for thermal hazard assessment. We reported the systematic thermal hazard assessment of synthesis of tert-butyl peracetate, preparation of hydroxylamine and synthesis of 2,6-diethyl-4-methylPhenylpropanamide and discussed their classification according to the modified Stoessel criticality diagram. The process temperature, maximum temperature of synthesis reaction, maximum temperature for technical reasons, temperature at which TMRad is 24 h and the final temperature under adiabatic condition(Tp, MTSR, MTT, TD24, Tf) were obtained by experiments and calculation. The evaluation results by the modified Stoessel criticality are more rational and conforms to reality.

Late Dr. Sam Mannan promoted in his last year the concept of the Safety Triad as a basis for effective process safety. As he mentioned himself, nobody will be surprised that this triad consists of Prevention, Mitigation, and Response. If everything were perfect an accident would not occur, but in hindsight after an accident experience teaches that at least one of these pillars of the triad is missing or defective. Also, given the recent hurricane Harvey experience in the Houston region, socio-technical associated hazard threats emerge not only from inside a plant but can be external: NaTech or technological accidental occurrences evoked by natural forceful events. Hence to be acceptably safe, the robustness and reliability of the triad components must be well designed, and their performance over time must be periodically verified. This objective supposes a risk-based approach both for design and for reliable operation. However, risk assessment itself is fallible. Over the years it has been shown that there are many limitations, but fortunately during the last few years we have seen an advent of new approaches, methods, and techniques to perform a more realistic scenario definition of what can go wrong, enabling to cope with uncertainties in models and parameter values, to analyze the risk dynamics of situations, such as reliability and availability degradation, but also process risk evolving in continuous processes. In all this, not only the technical aspects but also the associated human and organizational factors must be included. The latter dominate the scene in many situations. Last but not least is the concept of resilience in case risks are overlooked due to lack of knowledge or even to the notorious "black swan" incidents. Preparing for unexpected events should provide not only error tolerant design but early warning agility, capability to improvise, and means to recover operations. It is business continuity that will be at stake! The paper will elaborate both the triad and Sam Mannan’s view on risk assessment.

The paper presents a method for the development of safety indicators for a process industry application based on a system engineering perspective. Traditional approaches use probabilistic risk assessment or linear accident models which assume that accidents are linear chains of events and do not consider complex systemic factors and interactions. After BP's Texas City refinery incident, the investigation committee reported that BP had a false sense of safety performance due to providing more focus on managing personal safety rather than process safety. System engineering concepts may help the process industry to operate their activities without any severe accidents by establishing a better safety management system. This paper adopts the STAMP (System-Theoretic Accident Model and Processes) accident causation model to identify system specific indicators and also describes the proposed method with the help of a simple process industry application which is an LNG ship to ship transfer process. It compares the developed method with other methods for practical case applications. The first step of the present method is to establish the safety control structure, then safety performance indicators are identified. Further work is necessary to investigate to what degree these STAMP based indicators are complementary to indicators developed by other methods.

Globally industries have successfully implemented Process Safety Management (PSM) systems which have enormously reduced incident magnitude, their dreadful repercussions, and have proven organisational stability thereby maximizing profit margins. Due to limited data access, lack of incident and near-miss reporting & recording trend, lack of understanding and implementation of process safety management, petroleum refining sector as one of the most prone-to-incident industries globally was selected as a base case to evaluate process safety management practices in Pakistan. Various process and safety veterans were interviewed to figure out the present status of PSM systems in downstream (refining) sector and identify the gaps. Local regulations were reviewed to gauge their extent for process safety coverage in comparison to international process safety management regulations. Pertinent international publications and Center for Chemical Process Safety (CCPS) books were also reviewed to gain valuable insights of PSM in highly hazardous processes. Interviewing process and safety veterans and reviewing local regulations surfaced several gaps and provided areas of improvement in order to setup a healthy process safety management framework for hazardous petroleum refineries. This includes, absence of local PSM regulations, lack of PSM awareness, inadequate leadership commitment, lack of implementation of a dedicated PSM system, ineffective near-miss and incident reporting and database maintaining policy. Outcomes of this study are not merely limited to the refining sector of Pakistan but provide an opportunity for other sectors as well to implement PSM in an effective manner.

Quantitative risk assessment is a crucial step in the safety analysis of process systems. The advancement of modern process systems has made a large volume of process data and information available for process safety analysis. This tendency urges the need for developing new risk assessment approaches. Fault tree (FT), a conventional risk analysis method, is found to be ineffective in dynamic risk analysis and data analytics due to its static nature and reliance on experts' judgment. Artificial Neural Network (ANN) is a structured model built upon data samples and learning algorithms to process complex input/output data in the way that it is being trained. The application of ANN can help to overcome some of the limitations of FT. The data-driven nature, independency on prior information on events relationships, and less reliance on experts’ judgment are the advantages of ANN over FT. The use of ANN in risk assessment is not a new concept. However, there is limited work on the development of ANN-based risk assessment models using conventional methods such as FT as an informative base. This study proposes a methodology for mapping FT into ANN to support the convenient and practical application of ANN in risk assessment. The proposed method is demonstrated through its application to the analysis of a system failure in the Tesoro Anacortes Refinery accident. The results have shown that the ANN model mapped from the FT is an effective risk assessment technique.

As an important part of the protection measures, safety instrumented systems are widely used in process industries, especially in coal chemical industry. The setpoint value and process safety time (PST), which are contents of safety requirements, of the safety instrumented function (SIF) directly affects whether the safety function can correctly protect a specific scenario. In this paper, the setpoint value and PST of the SIF of “low coke oxygen ratio” is studied. The HAZOP-LOPA method is used to analyze the deviations and the possible cause and consequences. Steady-state model of coke oven gas (COG) to methanol is established based on Aspen Plus to research the effect of the coke oxygen ratio on the high-temperature gas temperature and oxygen concentration in syngas under different working conditions. Based on the simulation results, combined with the sensor working principle and signal processing, the setpoint value is determined. The PST is determined by using Aspen Dynamics. It is found that the smaller the coke oxygen ratio, the higher the high-temperature gas temperature, and the higher the oxygen concentration in syngas; for controlling the coke oxygen ratio, controlling oxygen flow is more critical than controlling COG flow; the setpoint value should be set to 3.78; the PST should be or less than 3.4s.