There are always significant challenges in improving the safety culture by changing and adding additional safety protocols. The unknown impacts of COVID-19 and how it quickly spreads led the industry to institute essential safety protocols. This paper addresses two problem statements. The first problem statement is: what are the additional safety protocols for process safety, construction & maintenance, and personal protective equipment requirements? The second problem statement is: what are the cost and schedule impacts of industrial construction projects resulting from implementing safety protocols and process safety during construction with the added PPE? While complying with added safety protocols, the industrial construction industry cannot forget that it has a distinct reputation for high incident rates and less than desirable safety performance. In 2017, the construction industry suffered 971 fatalities. This alarming number is compared to 1123 total fatalities in 2017 for the Gulf Coast States. The objective is to share the rationale and practices of social distancing, required additional PPE, and personal hygiene practices to reduce spreading and outbreaks during a pandemic within an industrial construction environment. Before any construction work, the process safety teams must clear, isolate, and tag out process lines, equipment, and instruments to be repaired or replaced. The information presented demonstrates the significant cost and schedule impacts that industrial construction companies will encounter during a pandemic like COVID-19. This paper aims to improve safety processes, cost & schedule impacts, and prescribe additional personal protective equipment in industrial construction during a pandemic such as COVID-19. The COVID-19 pandemic spread globally in a very short period. The reactions in mitigating the spread were suggestive, with little to no data on safety protective equipment and practices. The contribution this paper addresses are how to employ efficient safety practices and policies during a pandemic in an industrial construction environment.

The control of the risks associated with major hazard events is critical to the safe and continuous operation of the process industry. Over the last decades, the process industry has been successful at establishing and implementing robust Process Safety Management (PSM) systems to prevent and mitigate the consequences of such major hazard events. While there exist some industry guidelines developed relatively recently for events initiated by natural disasters and security-related threats, for initiating events like outbreaks of pathogens and pandemics, there is currently a clear lack of understanding of the impact of the restrictions and disruption caused by a pandemic on the ability of companies operating major hazard facilities to keep controlling the risks associated to their hazardous operations. Moreover, there is no industry guideline on how to account for such an impact in PSM systems for process safety hazards. The recent COVID-19 outbreak caused serious disruptions to normal operations that have challenged industry in their ability to control risks. The objective of this paper is to perform an analysis of the impact of a pandemic situation on the implementation of selected elements of PSM systems related to the identification and evaluation of the risks of a major hazard and their control. The approach chosen involves the analysis of the root causes of the failure of the selected PSM elements using a Fault Tree Analysis method. The findings provide the first steps in the establishment of recommendations for the upgrade of PSM systems to face events such as pandemics.

The COVID-19 outbreak that began at the end of 2019 brought a crisis impact on the health sector and other sectors such as the economy, social and politics. Human resource problems that emerged as a result of the pandemic made every company strive to protect employee safety. The food and beverage sector is one of the industries maintained to continue operating despite the large-scale social restrictions imposed in several regions, including an instant food company in East Java. This study aims to identify and determine human resource risk control to support employee productivity during the COVID-19 pandemic. This research used qualitative data based obtained through interviews, observation, and documentation. The method in this study used a combination of Failure Mode Effect Analysis (FMEA) and Bow Tie to identify, measure, and anticipate the risk of COVID-19 transmission in the company. The output result of the Failure Mode Effect Analysis (FMEA) method is the Risk Priority Number (RPN) score. The three activities with the highest RPN value were health services at the polyclinic, employee meal activities in the canteen, and activities inside and outside the factory. This analysis's results were continued by using the Bow Tie method to identify the causes, prevention, impact, and recovery of these risks. Bow Tie analysis results formed the basis for the preparation of Corrective Action and Preventive Action (CAPA). The risk control of human resources is focused on increasing employee productivity by reducing days lost due to labor shortages. In the end, the study results are expected to become recommendations in the evaluation of risk control and preventive measures for COVID-19 in manufacturing companies.

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used. Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event "COVID-19 infection" was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.

Coronavirus disease (COVID-19) is an infectious disease that has dramatically spread worldwide. Regarding the safety issues of industries, there is a requirement of dealing with the emergency risk in the period of urgent situations. In this work, we proposed a systems-theoretic approach of the two-stage emergency risk analysis (ERA) based on the systems theory, that is the System-Theoretic Accident Model and Processes (STAMP). The two-stage ERA includes the normal to emergency risk analysis (N2E-RA) and emergency to normal risk analysis (E2N-RA). Besides N2E-RA, we advocate that E2N-RA is also an important and indispensable part of ERA. We elaborated the characteristics of N2E-RA and E2N-RA, separately. Eventually, based on our analysis, we provided recommendations for decision makers in preventing and controlling industrial accidents in the period of COVID-19.

The COVID-19 epidemic has caused a lack of data on highway transportation accidents involving dangerous goods in China in the first quarter of 2020, and this lack of data has seriously affected research on highway transportation accidents involving dangerous goods. This study strives to compensate for this lack to a certain extent and reduce the impact of missing data on research of dangerous goods transportation accidents. Data pertaining to 2340 dangerous goods accidents in the process of highway transportation in China from 2013 to 2019 are obtained with webpage crawling software. In this paper, the number of monthly highway transportation accidents involving dangerous goods from 2013 to 2019 is determined, and the time series of transportation accidents and an autoregressive moving average (ARMA) prediction model are established. The prediction accuracy of the model is evaluated based on the actual number of dangerous goods highway transportation accidents in China from 2017 to 2019. The results indicate that the mean absolute percentage error (MAPE) between the actual and predicted values of dangerous goods highway transportation accidents from 2017 to 2019 is 0.147, 0.315 and 0.29. Therefore, the model meets the prediction accuracy requirements. Then, the prediction model is applied to predict the number of dangerous goods transportation accidents in the first quarter of 2020 in China. Twenty-two accidents are predicted in January, 23 accidents in February and 27 accidents in March. The results provide a reference for the study of dangerous goods transportation accidents and the formulation of accident prevention and emergency measures.

History has taught us that quite disastrous events with much human loss, injuries and asset damage could have been prevented or at least mitigated, if top management had recognized early warning signals in some form as urgent and had decided to take timely preventative measures. It turns out to be a rather common phenomenon in various sectors of life and some process industry examples are presented. The problem is further analyzed from a leadership point of view, from organizational structure and culture aspect, and what modern technology developments can help to improve the situation. Research in the latter directions is encouraged.

The paper discusses the impact of the COVID-19 pandemic on the Italian chemical and process industries, where Directive 2012/18/EU Seveso III, for the control of Major Accident Hazard (MAH), is enforced. The Safety Management System (SMS) for the control of MAH, which has been mandatory for 20 years in Italian Seveso Establishments, has been highly stressed by the external pressure, related in some way to the COVID-19 pandemic. Fairly, most companies, in particular in oil and gas sectors, have demonstrated an adequate capability to reconcile operation continuity and health requirements. This experience is providing the establishment operators and the regulators with valuable suggestions for the improvements of the SMS-MAH. Within this framework, an innovative organisational resilience model is proposed, aiming at the development of a higher capability to face future new crisis. The current SMS-MAH already includes some basic pillars to enhance resilience, which were valuable during the pandemic crisis, but a full and rationale development is still needed. Starting from the first pandemic phase experience, this paper presents a novel tool to assess the degree of "resilience" of a SMS-MAH. It is based on a questionnaire, featuring 25 questions grouped into eight items, according to the typical SMS-MAH structure. A two level AHP model has been developed in order to define the weights to be assigned to each point. The AHP panel included industrial practitioners, regulators, authorities and researchers. The results are based on the COVID-19 experience and consequently the developed model is tailored to face health emergencies, but the approach may be easily transferred to other external crises.

The versatile and computationally attractive FATE™ facility software package for analyzing the transient behavior of facilities during normal and off-normal conditions is applied to the problem of SARS-CoV-2 virus transmission in single-and multi-room facilities. Subject to the justifiable assumptions of non-interacting virus droplets, room-wide spatially homogeneous virus droplet aerosols and droplet sedimentation in accordance with Stokes law; the FATE code tracks the virus aerosol from a human source through a facility with a practical ventilation system which reconditions, filters, and recycles the air. The results show that infection risk can be reduced by 50 percent for increased facility airflow, 70 percent for increased airflow and the inclusion of a HEPA filter on recirculated ventilation air, and nearly 90 percent for increased airflow, inclusion of a HEPA filter, and wearing a mask. These results clearly indicate that there are operational changes and engineering measures which can reduce the potential infection risk in multi-room facilities.

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.

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.

The Process Safety Management (PSM) systems at the operating facilities in the Oil & Gas and in Chemical manufacturing industries have matured over the years and have become, at most facilities, very robust and sophisticated. These programs are administrated by Process Safety (PS) teams at both the corporate business units and plant levels and have been effective in reducing the number and severity of PS events across the industries over the past 25 years or so. Incidents however are occurring at a regular interval and in recent times several noteworthy PS events have occurred in the United States which have brought into question the effectiveness of the PSM programs at play. These facilities have been applying their PSM programs with the expectation that the number and severity of PS events would decrease over time. The expected result has not been realized, especially in context to those facilities that have undergone the recent incidents. Current paper reviews a few publicly available PS performance reports of Oil & Gas and Chemical manufacturing industries. The authors identified a few factors at play that have led to these PS events based on their experience, literature review, and incident investigation reports. Most of the factors are intertwined with multiple PSM elements and it requires a holistic approach to address them. Each of the factors is described and the path forward is proposed to improve the effectiveness of PSM programs.

To quantify the pandemic specific impact with respect to the risk related to the chemical industry, a novel risk analysis method is proposed. The method includes three parts. Firstly, the two types of "window of opportunity" (WO) theory is proposed to divide an accident life cycle into two parts. Then, a qualitative risk analysis is conducted based on WO theory to determine possible risk factors, evolution paths and consequences. The third part is a quantitative risk analysis based on a complex network model, integrating two types of WO. The Fuzzy set theory is introduced to calculate the failure probabilities of risk factors and the concept of risk entropy is used to represent the uncertainty. Then the Dijkstra algorithm is used to calculate the shortest path and the corresponding probability of the accident. The proposed method is applied to the SCR denitrition liquid ammonia storage and transportation system. The results show that it is a comprehensive method of quantitative risk analysis and it is applicable to risk analysis during the pandemic.

Correctly applied rock dust can dilute, inert, and mitigate the explosive potential of float coal dust. Trickle dusters are one element of a comprehensive system to help prevent coal dust explosions in underground coal mines. Trickle dusters supply rock dust to inert fine float coal dust in areas where it is commonly deposited, such as the longwall tailgate returns, return airways, pillaring areas, and downwind of belt transfers. Dust deposition studies show that the effectiveness of trickle dusters depends on several key factors. Using multiple orifices, rock dust should be released near the mine roof in the direction of the airflow in order to spread the cloud cross the entry. The rock duster should have a mechanism to break up rock dust agglomerates as they leave the rock duster. The particle size distribution of the limestone rock dust and its airborne concentration should be proportional to the airborne size distribution and concentration of coal dust passing by the trickle duster. Specifically, rock dusts having a greater proportion of <74 µm material are more effective at minimizing downwind zones of explosible mixtures than mostly larger particles. In our testing, rock dusts having more than 95% of <74 µm sized particles were adequately dispersed by trickle dusters. Based on our results, the mass rate of rock dust discharge from the trickle duster should exceed the rate of float coal production by at least a factor of four in order to minimize accumulations of explosible dusts.

The process industry has made major advancements and is a leader in near-miss safety management, with several validated models and databases to track close call reports. However, organizational efforts to develop safe work procedures and rules do not guarantee that employees will behaviorally comply with them. Assuming that at some point, every safety management system will need to be examined and realigned to help prevent incidents on the job, it is important to understand how personality traits can impact workers' risk-based decisions. Such work has been done in the mining industry due to its characteristically high risks and the results can be gleaned to help the process industry realign goals and values with their workforce. In the current study, researchers cross-sectionally surveyed 1,334 miners from 20 mine sites across the United States, varying in size and commodity. The survey sought to understand how mineworkers' risk avoidance could impact their near miss incidents on the job - a common precursor to lost-time incidents. Multiple regressions showed that as a miner's level of risk avoidance increased by 1 unit in the 6-point response scale, the probability of experiencing a near miss significantly decreased by 30% when adjusting for relevant control variables. Additionally, a significant interaction between risk avoidance and locus of control suggested that the effect of risk avoidance on near misses is enhanced as a miner's locus of control increases. A one-unit increase in locus of control appends the base effect of risk avoidance on near misses with an additional 8% decrease in the probability. Findings are discussed from a near-miss safety management system perspective in terms of methods to foster both risk avoidance and locus of control in an effort to reduce the probability of near misses and lost time at the organizational level within the process industry and other high-hazard industries.

The shift from batch to continuous manufacturing, which is occurring in the pharmaceutical manufacturing industry has implications on process safety and product quality. It is now understood that fault-tolerant process control of critical process parameters (CPPs) and critical quality attributes (CQAs) is of paramount importance to the realization of safe operations and quality products. In this study, a systematic framework for fault-tolerant process control system design, analysis, and evaluation of pharmaceutical continuous oral solid dosage manufacturing is proposed. The framework encompasses system identification, controller design and analysis (controllability, stability, resilience, .), hierarchical three-level control structures (model predictive control, state estimation, data reconciliation, .), risk mapping, assessment and planning (Risk MAP) strategies, and control performance evaluation. The key idea of the proposed framework is to identify the potential risks associated with the control system design itself, the material property variations, and other process uncertainties, under which the control strategies must be evaluated. The framework is applied to a continuous direct compaction process, specifically the feeding-blending subsystem, wherein the major source of variance in the process operation and product quality arises. It is demonstrated, using simulations and experimentally, that the process operation failures and product quality variations in the feeding-blending system can be mitigated and managed through the proposed systematic fault-tolerant process control system design and risk analysis framework.

The relationship between the explosion inerting effectiveness of rock dusts on coal dusts, as a function of the specific surface area (cm/g) of each component is examined through the use of 20-L explosion chamber testing. More specifically, a linear relationship is demonstrated for the rock dust to coal dust (or incombustible to combustible) content of such inerted mixtures with the specific surface area of the coal and the inverse of that area of the rock dust. Hence, the inerting effectiveness, defined as above, is more generally linearly dependent on the ratio of the two surface areas. The focus on specific surface areas, particularly of the rock dust, provide supporting data for minimum surface area requirements in addition to the 70% less than 200 mesh requirement specified in 30 CFR 75.2.

Researchers with the National Institute for Occupational Safety and Health (NIOSH) studied the potential for lithium-ion cell thermal runaway from an internal short circuit in equipment for use in underground coal mines. In this third phase of the study, researchers compared plastic wedge crush-induced internal short circuit tests of selected lithium-ion cells within methane (CH)-air mixtures with accelerated rate calorimetry tests of similar cells. Plastic wedge crush test results with metal oxide lithium-ion cells extracted from intrinsically safe evaluated equipment were mixed, with one cell model igniting the chamber atmosphere while another cell model did not. The two cells models exhibited different internal short circuit behaviors. A lithium iron phosphate (LiFePO) cell model was tolerant to crush-induced internal short circuits within CH-air, tested under manufacturer recommended charging conditions. Accelerating rate calorimetry tests with similar cells within a nitrogen purged 353-mL chamber produced ignitions that exceeded explosion proof and flameproof enclosure minimum internal pressure design criteria. Ignition pressures within a 20-L chamber with 6.5% CH-air were relatively low, with much larger head space volume and less adiabatic test conditions. The literature indicates that sizeable lithium thionyl chloride (LiSOCl) primary (non rechargeable) cell ignitions can be especially violent and toxic. Because ignition of an explosive atmosphere is expected within explosion proof or flameproof enclosures, there is a need to consider the potential for an internal explosive atmosphere ignition in combination with a lithium or lithium-ion battery thermal runaway process, and the resulting effects on the enclosure.

Dispersible rock dust must be applied to the surfaces of entries in underground coal mines in order to inert the coal dust entrained or made airborne during an explosion and prevent propagating explosions. 30 CFR. 75.2 states that "… [rock dust particles] when wetted and dried will not cohere to form a cake which will not be dispersed into separate particles by a light blast of air …" However, a proper definition or quantification of "light blast of air" is not provided. The National Institute for Occupational Safety and Health (NIOSH) has, consequently, designed a dust dispersion chamber to conduct quantitative laboratory-scale dispersibility experiments as a screening tool for candidate rock dusts. A reproducible pulse of air is injected into the chamber and across a shallow tray of rock dust. The dust dispersed and carried downwind is monitored. The mass loss of the dust tray and the airborne dust measurements determine the relative dispersibility of the dust with respect to a Reference rock dust. This report describes the design and the methodology to evaluate the relative dispersibility of rock dusts with and without anti-caking agents. Further, the results of this study indicate that the dispersibility of rock dusts varies with particle size, type of anti-caking agent used, and with the untapped bulk density. Untreated rock dusts, when wetted and dried forming a cake that was much less dispersible than the reference rock dust used in supporting the 80% total incombustible content rule.

The Mine Safety and Health Administration (MSHA) specification for rock dust used in underground coal mines, as defined by 30 CFR 75.2, requires 70% of the material to pass through a 200 mesh sieve (<75 µm). However, in a collection of rock dusts, 47% were found to not meet the criteria. Upon further investigation, it was determined that some of the samples did meet the specification, but were inadequate to render pulverized Pittsburgh coal inert in the National Institute for Occupational Safety and Health (NIOSH) Office of Mine Safety and Health Research (OMSHR) 20-L chamber. This paper will examine the particle size distributions, specific surface areas (SSA), and the explosion suppression effectiveness of these rock dusts. It will also discuss related findings from other studies, including full-scale results from work performed at the Lake Lynn Experimental Mine. Further, a minimum SSA for effective rock dust will be suggested.