Hazard and operability (HAZOP) analysis. A literature review

Measuring risk of renewable diesel production processes using a multi-criteria decision strategy

This study proposes measuring the risk of five alternative renewable diesel production technologies using a multi-criteria decision matrix strategy. Evaluated criteria include environmental, economic, technological, social, and process safety risks. The subjective Analytical Hierarchy Process (AHP) with stakeholder input provides criteria and sub-criteria weightings and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks alternatives. Alternative renewable diesel options are Green Diesel from first, second, and third-generation feedstocks, Fischer-Tropsch Diesel from second-generation biomass, and the transesterification of vegetable oils (VO) to make biodiesel. This study is a response to an earlier work measuring the sustainability of the same renewable technologies. While the previous work indicated Fischer-Tropsch Diesel as the most sustainable, this current work indicated the process as the "most risky," suggesting that risk is a significant driver of decision making over sustainability, and newly developed decision tools should address both perspectives.

Using fuzzy cognitive map in bow tie method for dynamic risk assessment of spherical storage tanks: A case study

The present study aimed to perform a dynamic risk assessment of liquefied petroleum gas (LPG) in spherical storage tanks using the Hazard and Operability (HAZOP) method. The "LPG release from spherical storage tank" scenario was determined to be a top event based on the risk level. The causes and consequences of this scenario were then specified using the Bow-Tie method. Additionally, fuzzy cognitive mapping (FCM) was utilized to identify the most critical threats, consequences, and effective barriers in a dynamic approach. Results showed that fracture/rupture in pipelines/a storage tank due to fire had the highest output degree and was identified as the most influential threat in the occurrence of the LPG release. Fatality/injury due to fire and explosion had the highest input degree and was more influenced by the release of LPG as the main consequence in the selected scenario. The findings of this study enabled us to make a logical decision about effective barriers for avoiding and minimizing the release of LPG and its threats and consequences based on the fuzzy cognitive map. Moreover, the results of this study showed that the FCM method could determine the most critical nodes with a higher degree and represent their relationships.

A systematic review of consequence modeling studies of the process accidents in Iran from 2006 to 2022

Analytical models and the prediction of accidents and their consequences are important tools for preventing accidents in the industry. Therefore, the present study was conducted to review process accident consequence modeling in Iran (2006-2022), helping improve incident modeling, and creating a context for preventing these accidents. In this study, the articles of 5 international Embase Medline/PubMed databases, ProQuest, Scopus, Google Scholar, and Web of Science, and four Iranian databases SID, MagIran, IranMedex, and IranDoc were examined using the PRISMA checklist. After reviewing the studies, 40 articles were included in the final analysis. The results showed that 25 studies used PHAST software, 11 studies used ALOHA software and 4 studies used ALOHA and PHAST software together. The highest number of studies was conducted in Imam Khomeini Port and Asaluyeh city, respectively. In addition, the number of studies published in Persian was more than in other studies. There was no similar agreement between the results of the two software in providing correct results at low concentrations. PHAST software also provided more accurate results than ALOHA over longer distances in stable and relatively stable weather conditions. The study and identification of hazards and scenarios in the studies that used PHAST software were more, more accurate and more coherent than in the studies that used ALOHA software. It is suggested that in future studies the same scenario can be compared with PHAST, ALOHA, and newer modeling software such as Fire Dynamics Simulator (FDS).

A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries

There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process.

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

After the Seveso disaster occurred more than 40 years ago, there has been an increasing awareness of the potential impacts that similar accident events can occur in a wide range of process establishments, where the handling and production of hazardous substances pose a real threat to society and the environment. In these industrial sites denominated "Seveso sites," the urgent need for an effective strategy emerged markedly to handle hazardous activities and to ensure safe conditions. Since then, the main challenging research issues have focused on how to prevent such accident events and how to mitigate their consequences leading to the development of many risk assessment methodologies. In recent years, researchers and practitioners have tried to provide useful overviews of the existing risk assessment methodologies proposing several reviews. However, these reviews are not exhaustive because they are either dated or focus only on one specific topic (e.g., liquefied natural gas, domino effect, etc.). This work aims to overcome the limitations of the current reviews by providing an up-to-date and comprehensive overview of the risk assessment methodologies for handling hazardous substances within the European industry. In particular, we have focused on the current techniques for hazards and accident scenarios identification, as well as probability and consequence analyses for both onshore and offshore installations. Thus, we have identified the research streams that have characterized the activities of researchers and practitioners over the years, and we have then presented and discussed the different risk assessment methodologies available concerning the research stream that they belong to.

Procedural Guide for System-Level Impact Evaluation of Industrial Artificial Intelligence-Driven Technologies: Application to Risk-Based Investment Analysis for Condition Monitoring Systems in Manufacturing

Industrial artificial intelligence (IAI) and other analysis tools with obfuscated internal processes are growing in capability and ubiquity within industrial settings. Decision-makers share their concern regarding the objective evaluation of such tools and their impacts at the system level, facility level, and beyond. One application where this style of tool is making a significant impact is in Condition Monitoring Systems (CMSs). This paper addresses the need to evaluate CMSs, a collection of software and devices that alert users to changing conditions within assets or systems of a facility. The presented evaluation procedure uses CMSs as a case study for a broader philosophy evaluating the impacts of IAI tools. CMSs can provide value to a system by forewarning faults, defects, or other unwanted events. However, evaluating CMS value through scenarios that did not occur is rarely easy or intuitive. Further complicating this evaluation are the ongoing investment costs and risks posed by the CMS from imperfect monitoring. To overcome this, an industrial facility needs to regularly and objectively review CMS impacts to justify investments and maintain competitive advantage. This paper's procedure assesses the suitability of a CMS for a system in terms of risk and investment analysis. This risk-based approach uses the changes in the likelihood of good and bad events to quantify CMS value without making any one-time point-wise estimates. Fictional case studies presented in this paper illustrate the procedure and demonstrate its usefulness and validity.

Process Hazard Analysis Based on Modeling and Simulation Tools

Chemical and oil processes are intrinsically sources of potential hazards. Although traditional qualitative hazard identification methods are simple, systematic, and flexible, such methodologies present limitations related to the inherent subjectivity, dependence on the team’s level of experience, and widespread time consumption of the members involved. In this context, the present work aims to develop a systematic way to use computational modeling and simulation tools for hazard identification. After extensive literature review, the present work proposes a methodology based on the association of the main points of previous works, with new contributions regarding the preparation for the simulations and the characterization of the minimum set of process variables that can enable appropriate interpretation of the results. The propene polymerization process (LIPP-SHAC process) was used as a case study to illustrate the proposed procedure. The paper explores how the model can be adapted for safety analyses and simulations for different hazard scenarios. The results obtained with different models are discussed and compared to those obtained with a traditional hazard identification approach to discuss how computational process modeling and simulation tools can sum to heuristic analysis. In conclusion, the use of simulations complementing the human-based approach can indeed enhance the understanding of mechanisms of hazardous scenarios, lessen conservative decision-making, and avoid overlooking device failures that can pose a severe hazard to the process.

HAZOP Ontology Semantic Similarity Algorithm Based on ACO-GRNN

Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse.

Implementation of Food Safety Management Systems along with Other Management Tools (HAZOP, FMEA, Ishikawa, Pareto). The Case Study of Listeria monocytogenes and Correlation with Microbiological Criteria

The food industry’s failure in planning and designing of and in implementing a Food Safety Management System and its foundation elements leads, in most instances, to compromised food safety and subsequent foodborne illness outbreaks. This phenomenon was noticed, worldwide, for all food processors, but with a much higher incidence in the medium- and small-sized food processing plants. Our study focuses on the importance of Food Safety Management System (FSMS), Critical Control Points Hazard Analysis (HACCP) and the Prerequisite Programs (PRPs) as the foundation of HACCP, in preventing foodborne outbreaks. For emphasis, we make use of the example of organizational food safety culture failures and the lack of managerial engagement which resulted in a multi-state listeriosis outbreak in USA. Moreover, we correlate this with microbiological criteria. Implementation of food safety management systems (ISO 22000:2018) along with incorporation of management tools such as HAZOP, FMEA, Ishikawa and Pareto have proved to be proactive in the maintenance of a positive food safety culture and prevention of cross-contamination and fraud.

Risk assessment of fire, explosion and release of toxic gas of Siri-Assalouyeh sour gas pipeline using fuzzy analytical hierarchy process

Background

Risk assessment of gas pipelines is very important because of various hazards and economic losses. Using fuzzy logic increases the reliability and accuracy of the results. The purpose of this study is to evaluate the fuzzy risk of fire, explosion and release of toxic gas in the pipeline using fuzzy analytical hierarchy process.

Methodology

Extraction of all hazards from HAZOP and HAZID was carried out. Fuzzy risk assessment was performed using MATLAB software. Using fuzzy hierarchy process analysis, the weight of each basic risk item (BRI) was summed up in a framework, and the fuzzy risk level was determined with a five-state criterion including highly desirable, favorable, moderate, undesirable and highly undesirable.

Results

The final risk score was equal to 0.1492, which according to the five-state criterion the risk level is in the favorable area. The highest risk score was related to hot work with open flame item with a risk score of 0.2485.

Conclusions

The final risk score of fire, explosion and release of toxic gas in Siri-Assaluyeh gas pipeline is in the optimal area. Fuzzy risk assessment, compare to conventional risk matrices, provides more data to safety managers about the hazards and their rankings. Accordingly, the results are expected to be applicable to the safety managers while making decisions related to the risk management of gas pipelines.

A Study on Standardization of Security Evaluation Information for Chemical Processes Based on Deep Learning

Hazard and operability analysis (HAZOP) is one of the most commonly used hazard analysis methods in the petrochemical industry. The large amount of unstructured data in HAZOP reports has generated an information explosion which has led to a pressing need for technologies that can simplify the use of this information. In order to solve the problem that massive data are difficult to reuse and share, in this study, we propose a new deep learning framework for Chinese HAZOP documents to perform a named entity recognition (NER) task, aiming at the characteristics of HAZOP documents, such as polysemy, multi-entity nesting, and long-distance text. Specifically, the preprocessed data are input into an embeddings from language models (ELMo) and a double convolutional neural network (DCNN) model to extract rich character features. Meanwhile, a bidirectional long short-term memory (BiLSTM) network is used to extract long-distance semantic information. Finally, the results are decoded by a conditional random field (CRF), and then output. Experiments were carried out using the HAZOP report of a coal seam indirect liquefaction project. The experimental results for the proposed model showed that the accuracy rate of the optimal results reached 90.83, the recall rate reached 92.46, and the F-value reached the highest 91.76%, which was significantly improved as compared with other models.

Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

Purpose

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.

Design/methodology/approach

The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.

Findings

The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.

Originality/value

The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Ontology-based computer aid for the automation of HAZOP studies

Hazard and Operability (HAZOP) studies are conducted to identify and assess potential hazards which originate from processes, equipment, and process plants. These studies are human-centered processes that are time and labor-intensive. Also, extensive expertise and experience in the field of process safety engineering are required. There have been several attempts by different research groups to (semi-)automate HAZOP studies in the past. Within this research, a knowledge-based framework for the automatic generation of HAZOP worksheets was developed. Compared to other approaches, the focus is on representing semantic relationships between HAZOP relevant concepts under consideration of the degree of abstraction. In the course of this, expert knowledge from the process and plant safety (PPS) domain is embedded within the ontological model. Based on that, a reasoning algorithm based on semantic reasoners is developed to identify hazards and operability issues in a HAZOP similar manner. An advantage of the proposed method is that by modeling causal relationships between HAZOP concepts, automatically generated but meaningless scenarios can be avoided. The results of the enhanced causation model are high quality extended HAZOP worksheets. The developed methodology is applied within a case study that involves a hexane storage tank. The quality and quantity of the automatically generated results agree with the original worksheets. Thus the ontology-based reasoning algorithm is well-suited to identify hazardous scenarios and operability issues. Node-based analyses involving multiple process units can also be carried out by a slight adjustment of the method. The presented method can help to support HAZOP study participants and non-experts in conducting HAZOP studies.

HAZOP Methodology Based on the Health, Safety, and Environment Engineering

In existing risk analysis techniques like the hazard and operability study (HAZOP) and the safety integrity level (SIL), design for operator safety is not considered. The health, safety, and environment (HSE) engineering depicts a detailed design directly related to the operator safety. However, the human risk had not been comprehensively analyzed. This paper proposes HSE-HAZOP as a technique for examining the systematic and efficient application of HSE engineering by exploiting the HAZOP systematic risk analysis technique and a quantitative risk derivation method, which is an advantage of the SIL. The analysis consists of four steps: the HSE-HAZOP preparation phase, risk analysis phase, risk assessment phase, and risk reduction phase. One part of a solution styrene butadiene rubber (SSBR) plant was used for a case study. In this case study, the items that handle with heptanoic acid were the study scope. After the risk assessment, we introduced the HSE engineering technique that should be applied for the risk reduction. Since there is no existing risk analysis method for HSE engineering, this proposed HSE-HAZOP is meaningful because it suggests systematic analysis method of the operator safety.

An End-to-End Reliability Framework of the Internet of Things

The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.

A HAZOP with MCDM Based Risk-Assessment Approach: Focusing on the Deviations with Economic/Health/Environmental Impacts in a Process Industry

A joint-analysis by the use of (i) the multi-criteria decision-making (MCDM) technique of Typical-Analytical-Hierarchy-Process (T_AHP) and Fuzzy-Analytical-Hierarchy-Process (F_AHP) with (ii) the Hazard and Operability (HAZOP) method respectively, was conducted in a sour-crude-oil process industry (SCOPI), focusing on the deviations with economic/health/environmental impact. Consequently, the intention of this paper is dual; that means the first one is the presentation and application of the traditional HAZOP method on a SCOPI, and the second one the illustration and usage of the combined HAZOP_TAHP/FAHP framework in the previous referred industry, via the resulted hazards with economical, health and also environmental impacts, unveiling the sustainable dimension of this approach. The choice of the particular SCOPI for executing the HAZOP process is greatly powerful for the implementation of this approach, due to the plentiful hazards that the process includes, and due to its sophisticated design, as well. Originally, the conventional HAZOP study was carried out for finding out the potential destructive causes of abnormal circumstances in the SCOPI’s establishment. Subsequently, the defined (by HAZOP) hazards with particular economic, health and environmental impacts were elaborated by the T_AHP and F_AHP techniques in order to broaden the HAZOP results and prioritize the risks in the worksite of the SCOPI. It is worthwhile mentioning that this joint-analysis can afford a reliable base to enhance procedure safety and upgrade the occupational health safety’s level (OHS). Likewise, it might be a constructive means for the decision-makers to: (i) evaluate the urgent situation of the restricted resources’ investment, in measures of preventing particular deviations (like the ones with economical, health and environmental consequences), and (ii) to endorse the sustainable growth of this industry, taking into account that a significant part of literature utilized the issue of sustainability as a frame for the development of OHS RAA techniques.

Application of human HAZOP technique adapted to identify risks in Brazilian waste pickers' cooperatives

Waste pickers' cooperatives are an important part of curb side collection, presenting different types of structures and responsible for the sorting and separation of recyclable materials. Within these places, the lack of resources, structure and hygiene are intimately related to the great part of the work accidents and the exposure of these workers to risky situations. Several tools in Security Engineering are being studied to minimize and control risks. Among these techniques, HAZOP (Hazard and Operability study) stands out as one that has proven efficient in managing risks in industrial processes and other areas of study. For the applicability of the technique in this study, interviews were conducted with the co-workers. The data were used to prepare a Preliminary Risk Analysis (PRA) and later to create new guiding words for the modification of HAZOP in order to consider the risk from human aspects in the activities. This work aimed to evaluate the HAZOP applied to evaluate errors related to human aspects in three waste pickers cooperatives in Campinas/SP and to compare the data obtained with the PRA. With the application of the PRA, 189 different deviations were identified and the pre-screening step was the one that most violated workers' risks. With HAZOP and using the content analysis tool to evaluate employees' perceptions, it was possible to identify 209 deviations by having the personal question (45%), followed by the managerial question (29%) as the main causes.

Geospatial Methods and Tools for Natural Risk Management and Communications

In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties.

Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication

Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication.

Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in the process industry

This study aimed at establishing fault tree analysis (FTA) using expert opinion to compute the probability of an event. To find the probability of the top event (TE), all probabilities of the basic events (BEs) should be available when the FTA is drawn. In this case, employing expert judgment can be used as an alternative to failure data in an awkward situation. The fuzzy analytical hierarchy process as a standard technique is used to give a specific weight to each expert, and fuzzy set theory is engaged for aggregating expert opinion. In this regard, the probability of BEs will be computed and, consequently, the probability of the TE obtained using Boolean algebra. Additionally, to reduce the probability of the TE in terms of three parameters (safety consequences, cost and benefit), the importance measurement technique and modified TOPSIS was employed. The effectiveness of the proposed approach is demonstrated with a real-life case study.