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In the realm of chemical processing, particularly at the industrial scale, safety is of utmost importance. A predominant factor causing accidents within the chemical industry is runaway phenomena, primarily initiated by uncontrolled exothermic reactions. This review critically examines the often‐overlooked decomposition mechanisms as a significant contributor to thermal energy release, necessitating a comprehensive revision and understanding of both experimental and theoretical strategies for assessing thermal degradation. Key to this discourse is the explication of calorimetry as the principal experimental technique, alongside ab initio quantum chemistry simulations as a robust theoretical framework for quantifying the most relevant properties. However, more than mere cognisance of these methodologies is required for a meticulous thermal stability assessment. The review emphasizes identifying and quantifying fundamental parameters through experimental and theoretical investigations. Only upon acquiring these parameters, including kinetic, thermodynamic, onset, and peak characteristics of the exothermic decomposition reactions, can one effectively mitigate risks and hazards in designing and optimizing chemical processes and apparatus. Furthermore, this review delineates qualitative and quantitative methodologies for hazard assessment, proffering strategies for estimating safe operational conditions and sizing relief devices. The paper culminates in exploring future trajectories in thermal stability assessments, focusing on emerging applications in lithium‐ion batteries, electrolyzers, electrified reactors, ionic liquids, artificial intelligence and machine learning approaches. Thus, the paper underlines the evolving landscape of thermal risk management in contemporary and future chemical industries. [ABSTRACT FROM AUTHOR]

Process monitoring is pivotal in process system engineering for abnormal situation management and ensuring process safety. This paper presents a review of Professor Khan's works on process monitoring. It examines (i) the number of publications, (ii) the type of publications, (iii) key sources, (iv) focused areas and their evolvement, and (v) the research impact by Professor Khan in process monitoring. The results suggest that journals are the primary sources he has used to disseminate research results. Over the years, his research focus evolved from detection to root cause diagnosis, fault propagation pathway analysis, and failure prognosis. Professor Khan has immensely impacted his peers, evidenced by his theoretical contributions, a higher number of recognitions by other researchers, and diversified workforce development. [ABSTRACT FROM AUTHOR]

In 1997, a number of articles were co‐authored and published in leading process safety journals (e.g., Journal of Loss Prevention in the Process Industries, Process Safety Progress, and Process Safety and Environmental Protection) by a young and enthusiastic scientist, Faisal Khan—motivated by a drive to improve the safety practices of the time after the 1984 Bhopal Disaster—and a well‐known Professor and Scientist from the Centre for Pollution Control and Environmental Engineering at Pondicherry University, Dr. Shahid Abbas Abbasi. It began a new horizon in the process safety domain, as this young scientist continued to excel in this field and shed light with numerous inventions and by training several hundreds of next‐generation safety professionals as a Professor at Memorial University of Newfoundland, University of Tasmania, and Texas A&M University. This article is written in honour of Dr. Faisal Khan. Capturing the fullness of Dr. Khan briefly is an arduous task; however, the authors of the current article have attempted to describe his personality, background, and contributions based on their professional interactions with him. [ABSTRACT FROM AUTHOR]

Wood pellets, which are manufactured from sawmill and forest residues, are sold in bulk for biomass power generation or in bags for residential heating. Wood pellet production involves combustible dust, which presents the risk of fires and explosions. Process safety management (PSM) is a framework for preventing and mitigating process‐related incidents. While PSM has historically been integrated within the chemical process industries, there is a need to systematically manage process‐related hazards in other sectors, including wood pellet and wood product manufacturing. However, there is a need to identify an approach to PSM implementation that is reasonable and achievable based on the relative complexity of the production process, as well as onsite resources. The scope of this project was to develop an integration tool for wood pellet production to serve as the foundation for a long‐term strategy and implementation plan led by industry. This research resulted in a PSM integration tool consisting of a PSM survey for gap analysis, self‐assessment worksheets that include numerous PSM best practices, factsheets, and an implementation strategy. Using the CSA Z767 Process Safety Management standard as the basis, the research included the development of a phased approach to integrating PSM elements to help improve feasibility. The selection of PSM element phases was informed by surveys of operations and subject matter experts. This research recognizes that, while PSM is currently mostly voluntary in Canada, some organizations have adopted the CSA Z767 standard into regulations and proactively implementing a PSM framework will position companies well should regulations change. [ABSTRACT FROM AUTHOR]

In the framework of process safety studies, consequence assessment of accidental scenarios is a crucial step affecting the eventual risk profile associated with the facilities under analysis. Conventional models used for consequence assessment are based on integral models, and may not be adequate to cope with the dynamic evolution of accidental scenarios and their three‐dimensional features. On the other hand, consequence assessment models based on computational fluid dynamics (CFD) approaches are promising to cope with complex scenarios and environments, but setting the simulation introduces relevant uncertainties associated with both the input data, assumptions, and with the modelling of physical effects involved. In the present study, uncertainty quantification (UQ) techniques are applied to support advanced safety studies based on CFD simulations of hazardous gas dispersion. Firstly, the accidental scenarios are characterized by defining release scenarios and conditions and quantifying source terms using integral models. At the same time, input meteorological data are gathered. This enables the development of high‐fidelity CFD simulations of gas dispersion based on different input sets and eventually the implementation of UQ techniques. The generalized polynomial chaos (gPC) expansion is employed to obtain hazardous gas concentration based on the variation of wind direction and speed. The present method is applied for the analysis of a real plant featuring a complex layout. The results show the advantages of the present approach by quantifying the influence of meteorological conditions and providing indications for supporting the development of protection systems and emergency measures. [ABSTRACT FROM AUTHOR]

Static electricity is a phenomenon commonly encountered yet often misunderstood and underestimated in terms of its hazard potential; it continues to challenge the safety and reliability of industrial processes, particularly those involving flammable substances. This paper delves into the critical issue of electrostatic hazards in industrial settings by presenting two flash fire/explosion case studies, one involving biphenyl dust and the other gasoline vapor; both linked to electrostatic discharges. The first case study examines an explosion in a flaker and pack‐out hopper during biphenyl flake manufacturing. The investigation reveals the role of electrostatic charges in the incident and how well‐intentioned equipment changes created warning signs of increased risk that were missed. The second case study discusses a gasoline vapor flash fire, highlighting the common yet overlooked hazard of static electricity during the transfer of flammable liquids. It underscores how common activities involving people can generate sufficient electrostatic charge to ignite flammable vapor–air mixtures in industry. The outcomes of these studies highlight the importance of acting on early warning signs of static electricity and the crucial role of data‐driven diagnostic techniques in addressing and controlling those hazards. By dissecting the intricacies of electrostatic phenomena, safety professionals can formulate actionable strategies to adapt plant operations and prevent hazards from static electricity. The paper advocates for a proactive approach to identifying and mitigating electrostatic risks in industrial settings beginning with process hazard analyses that incorporate static electricity hazards analysis and continues through employee training to ensure that early warning signs are identified, understood, and acted upon. It stresses the need for comprehensive safety measures, including proper grounding and bonding, use of static dissipative materials, and regular maintenance of safety equipment, to prevent incidents. Through these case studies, the paper contributes to understanding of electrostatic hazards in industrial processes and highlights the importance of integrating electrostatic safety measures into routine industrial operations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become a vogue topic in the press, and descriptions of its potential impact range from apocalyptic to salvational. Interest in the topic will no doubt stimulate the search for applications to support both the technical and management systems aspects of process safety management. Within our industries, maintaining institutional memory and technical capability is made increasingly challenging by more frequent job movement among younger staff and the loss to the retirement of more senior staff. One would hope that AI could help fill the gaps caused by these factors. However, the author's sampling of current AI capabilities suggests that AI is not yet ready to do so. This paper provides some examples of errors and insufficiencies identified when seeking AI assistance in addressing process safety issues. It also suggests some existing challenges to better "training" of AI to support the needs of the process safety community. It concludes that caution should be applied, especially by less experienced personnel, when seeking AI assistance in addressing process safety–related technical matters. [ABSTRACT FROM AUTHOR]

Currently used machine diagnostic systems are based on very modern solutions based on the acquisition and recording of their operating parameters in real time. Increasingly available and high-tech sensor systems mean that the number of recorded parameters is increasing and their quality is improving. These data are mainly used to assess the technical condition of machines and the processes they perform. In mining, these data can also be used to assess and, at a later stage, improve the safety of the underground mining process. Referring to this issue, the paper presents examples of the use of diagnostic systems for powered roof supports and longwall shearers to assess the safety status of the underground hard coal mining process. In the case of the wall support, the focus was on measuring the pressures in the stands of its individual sections. Temporary changes in the values of these pressures constitute a valuable source of information regarding the interaction of the support with the rock mass. In particular, this concerns the identification of the effects of the informational impact of the rock mass on the longwall excavation protected by the support. The research results presented in the paper, especially in the case of very dangerous dynamic impacts, indicate the possibility of both diagnosing the operating condition of the section and identifying symptoms of exposure to such events. This undoubtedly significantly expands the possibilities of using the measured pressures. Diagnostic signals from a longwall shearer are also widely used. The current intensities drawn by its motors while cutting the rock mass, as well as the advance speed and its position in the wall make it possible to analyze these parameters and their changes before, during and after the occurrence of various types of events. These data enable the assessment of the effects of the rock mass on its operational efficiency and safety status. It also enables the identification of symptoms that precede the occurrence of such events. The presented examples indicate the need for a broader and more holistic approach to the use of diagnostic parameters of mining machines. In particular, this concerns the study of the cooperation between the support and the rock mass and its influence on the efficiency and safety of the rock mass mining process. The subject matter addressed relates to very important and current issues, and the developed methodology and obtained results should be applied in practice as soon as possible. [ABSTRACT FROM AUTHOR]

This paper outlines innovative pedagogical approaches in chemical engineering safety education, utilising three key strategies: (1) Integration of the direct experiences of experienced professional engineers to teach expertise-aligned process safety case studies; (2) Shifting from traditional short oral presentations to more comprehensive and dynamic 'engineering meeting' formats to drive constructive student learning; and (3) The use of Generative AI to enhance (1) and (2), with cost/time scalability, improved student access and by accommodating greater learning-style diversity. Evaluation of these educational innovations is performed through a mixed-methods approach and reveals positive impacts on student learning and engagement. The paper provides a detailed outline of classroom implementation, with supporting resources, for straightforward integration by other academics. • Process safety education can be significantly enhanced by including late-career professional industry consultants and semi-retired engineers. • Generative AI roleplaying as industry consultants, can provide greater scale, student access and cater to more diverse learning styles of students. • Generative AI chatbots can be used to simulate in viva style assessment formats of process safety incident case studies. Vivas encourage more comprehensive understanding and root cause analysis of process safety incidents, when compared to commonly used short oral presentations, which can drive reductive cause-effect explanations. • A small subset of students express a preference for AI teaching tools (over in-person meetings) because of social or performance anxieties. [ABSTRACT FROM AUTHOR]

Determining the laminar flame speed of dusts is far from straightforward. A strong dependency on the experimental setup and the data treatment's high complexity makes it a true challenge. This work compares three complementary experimental setups to measure the laminar flame speed of organic dust (here, cellulose): a modified Hartmann tube, a 20 L sphere, and a micro‐fluidized bed (MFB) burner. The first two consider the flame propagation phenomenon in its globality, which means that numerous steps are involved simultaneously (particle heating, pyrolysis, oxidation, radiative transfer, flame stretching), while the third one decouples pyrolysis and combustion, to focus mainly on the oxidation rate. An MFB was conceived to generate pyrolysis products and burn them in a laminar flame. Unstretched flame velocities determined with the first two setups were consistent and equal to 22.0 and 26.6 cm ∙ s−1, respectively. Using Silvestrini's equation, values ranging between 14.0 and 33.4 cm ∙ s−1 were obtained according to the dust concentration. With the MFB burner, the flame speed was much higher (135–155 cm ∙ s−1), due to the higher temperature of the fresh mixture and the fact that only the oxidation of the pyrolysis gases is considered. A numerical simulation (Chemkin) confirmed these results since the range 135 to 231 cm ∙ s−1 was obtained for equivalence ratios of 0.6 and 1.2, respectively. The discrepancy between the laminar flame speed determined in the sphere or in the tube and that obtained in the MFB highlights the significant influence of particle heating and pyrolysis during a dust explosion. [ABSTRACT FROM AUTHOR]

AbstractIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to study the influence of stretching process parameters on the safety performance of modified double base propellant molding process, the compression melting section, melt conveying section and die forming section are modeled as a whole, the density, rheological properties of the modified double base propellant slurry are determined, the mesh independ¬ence of the fluid domain is verified based on Polyflow software, and the safety characteristics of the stretch molding process of the modified double base propellant slurry under non-isothermal conditions are studied. The results show that the increase of screw speed increases the temperature, pressure and shear rate of the slurry, and there is a risk of ignition and explosion, in or¬der to ensure the safety, the appropriate screw speed in the stretching molding process is 10 r/min. With the increase of screw temperature, the shear rate decreases, the plasticization degree of the material becomes worse, and the increase of screw temperature is easy to decompose the material, combined with the composition characteristics of the sensitive material of mod¬ified double base propellant, the screw temperature is relatively safe at 85 °C . Compared with the screw temperature, the change of sleeve temperature has little influence on the material flow field, and combined with the actual production conditions of modified double base propellant, the sleeve holding temperature of 70 °C is suitable for the production of modified double base propellant. Based on the simulation results, it is concluded that the effects of rapid changes in pressure and temperature should be paid attention to in the propellant production process. [ABSTRACT FROM AUTHOR]

Process safety practices and management systems have been in place for many years and have been widely credited for reductions in major accident risk. However, many organizations today are challenged by inadequate management system performance, resource pressures, and stagnant or declining process safety performance. Systems for managing process safety have not been fully successful in some organizations due to a variety of issues; major, frequent contributors include a lack of senior management commitment and poor safety culture. Senior management commitment and safety culture are inextricably linked, as the quality of an organization's leadership and their commitment will drive or limit the culture. Changes to improve management systems are relatively easy to implement, but extremely difficult to sustain without strong leadership commitment and total line management support. It is not just a case of changing the systems, but also changing the safety culture of the organization. Safety culture tends to be the result of everything that happened or failed to happen and represents the organization's shared values, beliefs, attitudes, and behaviors with respect to safety (occupational safety and process safety). To change the safety culture and sustain the change, leadership must care and explicitly show that they care all of the time. Only then can an organization begin to improve its culture. The first step for any organization is identifying and understanding their existing culture. This paper will present the essential features of a sound safety culture and a methodology for identifying safety culture weaknesses based upon a culture, leadership, and accountability (CLA) review protocol used at different levels of the organization. Other key steps involved in changing and sustaining an improved safety culture will also be addressed. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive overview of the critical process safety considerations inherent in hydrometallurgical metal recovery within the lithium‐ion battery (LiB) recycling process. As hydrometallurgy application in LiB recycling is still in the early stages of development, it is crucial to identify the hazards and provide safety recommendations. Hazards related to hydrometallurgy are identified and categorized in process, toxic, fire, explosion, corrosion, environment, storage, and transport hazards. Risk reduction measures are suggested using the hierarchy of control methodology to eliminate and reduce risks to as low as reasonably practicable (ALARP), based on UK regulatory framework. [ABSTRACT FROM AUTHOR]

Introduction: For years, chemical process industries have reported the unexpected release of highly hazardous liquids and gases. A disaster could not be avoided when these materials were not adequately controlled. The issue of Process Safety Management (PSM) is still being discussed in developing and developed countries. PSM was established to manage the risks of a company’s personnel, properties, products, environment, and credit risks. This study aimed to review the challenges and achievements of PSM implementation and provide recommendations for improvement. Material and Methods: A collection of scholarly articles published from 2000 to 2023, such as Science Direct, WOS, PubMed, and Scopus, was chosen through a systematic, meticulous review. After thoroughly examining these abstracts, titles, and complete contexts, 49 articles were finally selected for inclusion in the study and classified based on different criteria, such as publication year, authors, achievements, and challenges. Results: PSM in the United States has been chiefly studied due to the high number of chemical process industries and its presence in the form of OSHA legislation in this country. The areas of operations, audits, and resources have the most challenges in implementing PSM. On the other hand, reducing the severity of incidents in chemical processes and increasing productivity are considered the most significant achievements of PSM implementation. Conclusion: PSM as an interdisciplinary field has improved and become more effective over the years.. However, incidents still exist, and the number can increase, considering the growth of industries’ chemical processes. Therefore, a deep look at the issues of risk-based regulations, competency, operational excellence, and learning from incidents is necessary to achieve excellence in PSM.

Despite the movement to improve process safety, incidents in chemical-related industries are still occurring around the world. Personnel process safety lack of knowledge is a typical cause of these events. Process safety is an integral part of a chemical engineer’s work. Employees’ discontent with chemical engineering graduates’ process safety understanding revealed deficiencies in undergraduate courses. Inadequate accident investigation and prevention training has also hampered graduates’ knowledge and preparation for high-risk jobs. To address the gaps, an online game platform was suggested and tested with 450 undergraduate students at different levels and disciplines. Virtual reality process safety game proven to improve chemical engineering graduates’ process safety competency while delivering industry experience. The approach is also believed to improve students’ problem-solving and decision-making skills and motivate engagement to study process safety. The main features of the game are desired to be online web-based with serious role-play integrated into real-life simulation activities. [ABSTRACT FROM AUTHOR]

The remote control ship is considered to be the most likely implementation of maritime autonomous surface ships (MASS) in the near-term future. With collaborative control from onboard controllers and operators ashore, ships may operate in three navigation control modes (NCMs), manual, autonomous, and remote control, based on different levels of control authority. The scientific selection of the appropriate NCM for MASS under multiple driving modes is crucial for ensuring ship navigation safety and holds significant importance for operators and regulatory authorities overseeing maritime traffic within specific areas. To aid in selecting the proper NCM, this study introduces a risk-based comparison method for determining optimal control modes in specific scenarios. Firstly, safety control paths and processes for MASS under different NCMs are constructed and analyzed using system-theoretic process analysis (STPA). By analyzing unsafe system control actions, key Risk Influencing Factors (RIFs) and their interrelationships are identified. Secondly, a Hidden Markov Model (HMM) process risk assessment model is developed to infer risk performance (hidden state) through measuring RIF states. Cloud modeling with expert judgments is utilized to parameterize the HMM while addressing inherent uncertainty. Lastly, the applicability of the proposed framework was verified through simulation case studies. Typical navigation scenarios of conventional ships in coastal waters were chosen, and real-time data collected by relevant sensors during navigation were used as simulation inputs. Results suggest that in the same scenario, process risks differ among the analyzed NCMs. Traffic complexity, traffic density, and current become the primary factors influencing navigation risks, and it is necessary to select the appropriate NCM based on their real-time changes. [ABSTRACT FROM AUTHOR]

The growth of artificial intelligence (AI) has allowed industries to automate and improve their efficiency in operations. Especially in process industries, AI helps to develop intelligent models and tools to proactively monitor and predict equipment or system failures, minimize downtime, and optimize maintenance schedules. With the advancements in AI and its ability to perform tasks, there is a growing belief that AI may eventually replace humans. However, the absence of human involvement in operations in the process industry raises safety concerns. Therefore, AI should collaborate with humans rather than replace them in processing facility operations. This technology is referred to as intelligence augmentation (IA). This article (i) presents a detailed comparison between AI and IA's potential in process systems, (ii) identifies the feasibility of using AI and IA in process safety, and (iii) identifies the risk associated with the implementation of AI or IA in process industries. [ABSTRACT FROM AUTHOR]

This research explores how process safety and process security risk management methods may be integrated into a unified framework, thereby considering the resilience engineering paradigm. Employing a systematic literature review and expert surveys with industrial practitioners alongside representatives from key regulatory bodies to understand the complexities and opportunities of such integration. The interactions that result from a mixed-method study ensure the gaining of insights into the practical challenges and opportunities of integration in diverse operational and regulatory environments. Utilizing Microsoft Forms analytics tools and ATLAS.ti for qualitative analysis, the research identifies patterns and conditions, as well as the context, which is pivotal for constructing a comprehensive, adaptable risk management framework. This paper introduces a novel, holistic risk management perspective, advocating for the amalgamation of process safety and process security within the chemical industry, adopting the System-Theoretic Accident Model and Processes (STAMP) model underscored by resilience principles. [ABSTRACT FROM AUTHOR]

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

According to historical statistical data, management and organizational factors (MOFs) contribute more to process accidents than technique factors. Under the umbrella of socio-tech system theory, human reliability analysis (HRA) has become a critical part of systemic probability risk analysis. In many HRA techniques, MOFs are among the performance shaping factors (PSFs). However, the interactions and causality of MOFs to human errors are still difficult to quantify and lack validation. To fill these gaps, a framework is proposed, considering data source selection, CBN construction algorithm comparison, and results validation. The case study employed the open access eMARS database as a data source. The optimized hybrid structure learning algorithm and Bayesian criteria parameter learning algorithm are employed to build a Causal Bayesian Network (CBN) of (MOFs) that lead to human error. The proposed kernel CBN is validated through prediction accuracy and sensitivity analysis. For theoretical contribution, the validated kernel BN could generally serve as the heart part of more specific CBNs as a basis for future works. For practical applications, an application shows the model's ability to quantify the contribution of MOFs to system reliability. The results show that human-machine interacting system reliability is most sensitive to organizational factors such as adequate training and procedures.

The purpose of the process safety management system is to identify hazard factors and develop a set of methods to prevent injuries at an airline, occupational illness, material costs in case of damage to property and the environment. The analysis of the structure of occupational pathology depending on the factors of the production environment and the working process for the period 2013–2022 shows that the percentage of diseases associated with the impact of production physical factors for this period remains at the same level. This fact, in turn, confirms the relevance of the chosen study. Analysis and identification of the current production situation is necessary to assess the impact of adverse production factors. In this study, a new approach to the mathematical model for a process safety management system is implemented. Mathematical modeling allows a deeper understanding of the nature of certain phenomena and to obtain information about the real situation, which in turn stimulates the development of new scientific problems and methods of solving them, and is also the basis for choosing specific solutions for the implementation of certain projects. The successful implementation of strategies in order to create a process safety system for a flexible monitoring and management structure depends on how effective its functional structure is; this provision is explained by the fundamental nature of the tasks that are solved at the management stage. The article discusses the theoretical statements concerning mathematical modeling. When creating the model, the apparatus of abstract algebra-set theory – was used. The model developed in the course of the study makes it possible to introduce a model of the process safety management system into the activities of aviation enterprises.

Several data‐driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data‐based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low‐quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low‐quality data issues which are crucial in the data‐driven modelling of processing systems. [ABSTRACT FROM AUTHOR]

Deep learning-based fault detection and diagnosis (FDD) methods have received considerable attention, and many methods based on convolutional neural network (CNN) have been applied to fault diagnosis for chemical processes. However, current fault diagnosis methods train and detect all faults using a single model and the same feature inputs, resulting in the neglect of correlations and difference between faults and inferior fault diagnosis performance. In this study, a novel fault diagnosis method named triage-based convolutional neural network (TrCNN) for fault diagnosis is proposed. Initially, the fault set is partitioned into distinct triage types. Subsequently, distinct models are formulated and applied to their respective triage types in the sub networks layer, while a triage network is developed in the triage layer. Ultimately, the models from the triage layer and sub networks layer come together to constitute the triage fault diagnosis system. The proposed method can adaptively select suitable models and features for different triage types, leading to improved diagnostic accuracy, especially for similar faults. When applied to the Tennessee Eastman (TE) chemical process the TrCNN demonstrates impressive performance, validating its effectiveness in fault diagnosis. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid expansion of the chemical process industry necessitates an increased emphasis on process safety. Inherently safer design strategies are one of the most reliable and robust methods for process safety and are generally considered cost-optimal for loss prevention. In this manuscript, a semi-quantitative risk-based index, named the Inherent Process Risk Index (IPRI), is proposed to evaluate a process's inherent safety from first-principle chemical engineering calculations. The strategy proposed relies on simulation data from Aspen Plus and helps quantify the risk associated with both individual unit operation and the process. Input to the risk calculations include process parameters, such as temperatures, pressure, and flow rates. More specifically, in this manuscript, the risk estimated is presented using two case studies to elaborate the functioning of IPRI, namely the production of formic acid using Kemira-Leonard process and methyl methacrylate production. In addition to benchmarking the process risk with IPRI, the results obtained were compared to those obtained using existing indexing methods to highlight the novelty of IPRI and also explain the differences between IPRI and other indexing methods. It is believed that integrating risk indexing with Aspen plus simulation will aid in teaching process safety to undergraduate engineering students. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the years that I have led process hazard analyses (PHA), I have repeatedly encountered team members who adamantly proclaimed that a scenario that I have proposed is invalid or too farfetched. Their reasoning is based on the idea that "It can't happen here" or "We have operated for twenty years and that's never happened." This paper will focus on three examples of the above reasoning. The first and second will examine the "it can't happen here" attitude and the third will explore the "it hasn't happened so it won't happen" mindset. Through analyzing both of these scenarios, this paper will delve into the development of human bias that leads to these determinantal attitudes. Each example from an actual PHA Team will illustrate the prevalence of these type of human bias. It will also provide recommendations for addressing these two particular types of bias in a process hazard analysis. Failure is an inherent product of these human biases and this paper will seek to address why people fail to learn from prior incidents at their own company and those from industry. Each example from an actual PHA Team will illustrate these types of human bias. It will also provide some recommendations for addressing these two types of bias in a process hazard analysis. Using the examples from real‐life PHAs to illustrate the concepts of human bias and how they can affect the ability of a PHA team to adequately assess risk, will provide insight on how this can occur and how it can be addressed. The overall objective of this paper is to help develop a better understanding of how human bias can lead to failure to learn from prior incidents and is reflective of Process Safety Culture. Overcoming and addressing human bias supports learning from other incidents and improve the assessment of risk. [ABSTRACT FROM AUTHOR]

Process safety incidents in the oil and gas industry can have serious consequences. In the United States and Malaysia, standards for process safety management (PSM) have been established by US OSHA and PETRONAS, respectively. However, these standards are not always properly understood or followed by employers, resulting in ineffective safety programs and uncontrolled hazards. Plug and abandonment (P&A) activities in the upstream oil and gas industry require high control of well barriers. To assess the gap between PSM industrial standards and crew awareness in P&A activities, a study was conducted throughout one cycle of the project campaign. The study found that 70% of offshore crews had a high awareness level of the health, safety, and environment management system (HSEMS) and PSM, and 96% compliance was achieved from audit activities. However, two proposed PSM elements scored low in the assessment, indicating a need for improvement in site implementation. The study provides valuable information for offshore crews and readers seeking to improve and assess PSM elements in process safety. The results can help identify and control potential hazards related to technical safety, operational safety, and personnel safety. Furthermore, the study aims to support the development and implementation of PSM standards in Malaysia. [ABSTRACT FROM AUTHOR]

During the COVID‐19 pandemic, while China blocked the spread of the coronavirus, its process safety status still maintained a positive trend. Hence, an in‐depth exploration of China's experience and lessons in coordinating epidemic control and process safety is an urgent need to exchange knowledge with other countries to prevent similar catastrophes. This paper comprehensively traces China's national strategies in coordinating epidemic control and safety regulation since the end of 2019 through information collection, literature surveys, and investigative interviews. According to the characteristics of these strategies, China's anti‐pandemic process is divided into three stages: outbreak, concentrated resumption of work, and normalization of epidemic prevention. On this basis, the main risks of each stage and the response measures taken by the Chinese government are further subdivided. Furthermore, the changes brought about by COVID‐19 on Chinese safety regulators are also discussed. [ABSTRACT FROM AUTHOR]

Language matters for effective leadership and culture. And the language embedded in the term "operational discipline" is not helpful. This term is inconsistent with decades of learning on cause analysis, systems thinking, and human performance. Humans typically do not fail because they are not disciplined enough. Failures occur for other reasons that stem from the systems within which the humans are working. For many people, the term "discipline" has a negative or punitive connotation. And the term "operational" focuses the spotlight on operations. The two terms taken together may imply to some people that willful operator deviations are the root cause of some incidents. Failures typically involve a range of management processes and functional groups. Thorough failure analysis can identify systemic causes, and follow‐up corrective actions may be targeted at the upstream sources. Healthy culture requires a positive, inclusive, and curious environment that seeks to continuously learn and improve without casting blame. Even if the term "operational discipline" is not intended to place blame, the choice of words defeats that aspiration. When seeking to improve process safety culture, choose language that supports a positive learning environment of ownership and empowerment by the workforce. And support those words through management actions. [ABSTRACT FROM AUTHOR]

This work explores the integration of dynamic simulation with the concept of protection layers to assess the dynamic safety of an extractive distillation operation. This particular study concentrates on the separation process of acetonitrile from water using ethylene glycol as the entrainer. It appraises the efficacy of different independent protection layers — ranging from basic process control systems, critical alarms, and operator interventions, to safety instrumented systems and pressure relief valves — against various potential risk scenarios. The study considers scenarios leading to overpressure situations, such as failures in the condenser system, surges in hot steam supply, or severe disturbances in feed. Using Aspen Dynamics for detailed dynamic simulation and safety evaluation, the study examines the response and effectiveness of each protection layer meticulously. It employs a scenario-based safety analysis to gauge the dynamic safety performance and the effectiveness of the protection layers. The research underscores the value of scenario analysis in grasping the dynamic responses of the distillation process and the spread of irregularities through the system. This understanding is crucial for estimating the process safety time and for crafting an efficient safety system. [Display omitted] • Dynamic rigorous simulation delves into the extractive distillation process. • Faults are categorized by protection layers for dynamic scenario simulation. • Analysis reveals condenser malfunctions as the main overpressure source. • Protection layers effectively mitigate these overpressure risks. [ABSTRACT FROM AUTHOR]

In Occupational Safety and Health (OSH) and operational safety, confined spaces are high-risk working areas, where frequent serious and fatal incidents occur. However, there is a limited use of data-driven approaches based on Machine Learning (ML) techniques for learning from such incidents. In this context, our study proposes a systematic approach to develop a structured database suitable for ML-based analyses, considering various data sources with unstructured or semi-structured data. The approach was applied to 1346 incidents that happened in confined spaces mainly containing chemicals and located in manufacturing and process industries. This allowed building models predicting the fatal or non-fatal consequences and direct causes of incidents. The recognition of the approach potentialities and difficulties stimulated the development of an improved version of it to be adapted to different safety-related undesired events. The generalised systematic approach supports safety analysts during the process of learning from undesired events to maximise the lessons learned. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.

Large-scale distillation is a challenge in many respects. Particularly difficult is the purification by distillation of a compound with limited thermal stability. This article describes various aspects of these difficulties with some possible solutions. Special emphasis is placed on the collaboration of different disciplines to find pragmatic solutions to these challenges. The purification of diketene in quantities of several 1000 ta–1 is an excellent example to illustrate the different requirements. Although the distillation of diketene has been carried out by several companies for many years, there are still some aspects that deserve special attention.

As the world energy problem becomes more and more prominent, offshore wind farms have made remarkable progress in the last few years. With the advent of the rush to install offshore wind turbines around the world, offshore wind turbine (OWT) installation accidents have gradually increased. The process safety associated with offshore wind turbine installations has been a growing concern in offshore wind industry. To address this issue, this study aims to identify the factors affecting offshore wind turbine installation process safety, and to evaluate the correlations among these factors. The study involved an investigation into the causes and procedures of accidents that occurred during offshore wind turbine installation, leading to the development of customized safety factors. A questionnaire survey was developed and administered by a team of offshore wind industry professionals, including scholars, managers, and workers, and the data collected was analyzed using a theoretical model based on structural equation modeling. The results show that environment and weather condition, organization and management, and OWT installation technology have a significant impact on OWT installation technology. Furthermore, OWT installation technology has the largest total effect on the process safety of OWT installation. [ABSTRACT FROM AUTHOR]

Exothermic reactions carried out in batch reactors need a lot of attention to operate because any insufficient condition can lead to thermal runaway causing an explosion in the worst case. Therefore, a well-designed intervention action is necessary to avoid non-desired events. For this problem, we propose to use resilience-based reinforcement learning, where the artificial agent can decide whether to intervene or not based on the current state of the system. One of our goals is to design resilient systems, which means designing systems that can recover after a disruption. Therefore, we developed the resilience calculation method for reactors, where we suggest the use of dynamic predictive time to failure and recover to better resilience evaluation. Moreover, if the process state is out of the design parameters, then we do not suggest calculating with the adaptation and recovery phase. We suggest using Deep Q-learning to learn when to intervene in the system to avoid catastrophic events, where we propose to use the resilience metric as a reward function for the learning process. The results show that the proposed methodology is applicable to develop resilient-based mitigation systems, and the agent can effectively distinguish between normal and hazardous states. [ABSTRACT FROM AUTHOR]

In recent decades, machine learning (ML) techniques have been effectively applied for industrial process monitoring to assure safety and high-quality yield. Traditional process fault detection, identification, and diagnosis (FDI&D) approaches are insufficiently smart to address the modern complex challenges of real-time industrial chemical processes. The detection and diagnostic resolution of the traditional monitoring approach are less robust, inefficient, and produce the wrong interpretation of actual fault information. These approaches are based on a single-scale fault illustration and cannot effectively address multiple fault depiction roots. This study introduces a novel ML-aided methodological framework for industrial and manufacturing monitoring systems. The proposed ML framework is developed using Principal component analysis (PCA), Shannon information entropy (IE), wavelet transformation (WT), and signed directed graph (SDG). It includes fault detection, identification, and diagnostic propagation root-path interpretation to address the safety challenges of modern, real-time industrial chemical processes. The proposed methodological framework is validated using the Tennessee Eastman process (TEP) benchmark to highlight their performance and efficiency. The results of this study determined that the new proposed approach is more efficient in terms of accuracy, robustness, and actual propagation root cause than traditional techniques. It has a high fault detection rate (FDR), low fault alarm rate (FAR) that identifies and recognizes the actual faulty-correlated variables to establish the SDG model framework for determining the actual diagnostic propagation root path. It initially enables operators to react to unusual incidents, ensuring industrial safety, minimizing economic loss, and avoiding disasters. [Display omitted] [ABSTRACT FROM AUTHOR]

With the growing environmental concern, alternative processes and products are must for sustainable development of processing industries. Leather processing industries conventionally employ sodium sulfide (Na2S) for hair loosening step, which is unsafe as it produces hydrogen suflide. Thiogycolic acid is the key component of hair treatment formulations used in cosmetics. The use of alkali salt of thioglycolic acid known as calcium thioglycolate for hair loosening has been studied in this paper as an alternative to Na2S. The spent effluent liquor of liming process was analyzed for pollution load through biochemical oxygen demand (BOD), chemical oxygen demand (COD) and total dissolved solids (TDS) analysis, which indicated significant reduction in pollution load: BOD (50%) and COD (fivefold) for calcium thioglycolate process as compared to Na2S-based control process. Physical strength characteristics such as tensile strength, tear strength, grain crack and distention have also been analyzed for both experimental and control leathers, which revealed that strength properties of processed leathers from calcium thioglycolate process were either improved or on par with control leathers, whereas the potential danger of H2S formation at work place due to the use of Na2S has been avoided. In addition, cost analysis indicates substantial reduction in pollution load and related effluent treatment costs could compensate for the higher cost of calcium thioglycolate process. [ABSTRACT FROM AUTHOR]