Textual data transformations using natural language processing for risk assessment.

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

How can process safety and a risk management approach guide pandemic risk management?

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.

Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning.

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well-established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents' relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor-based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.

Risk analysis of dust explosion scenarios using Bayesian networks.

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow-tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.

Risk management of domino effects considering dynamic consequence analysis.

Domino effects are low-probability high-consequence accidents causing severe damage to humans, process plants, and the environment. Because domino effects affect large areas and are difficult to control, preventive safety measures have been given priority over mitigative measures. As a result, safety distances and safety inventories have been used as preventive safety measures to reduce the escalation probability of domino effects. However, these safety measures are usually designed considering static accident scenarios. In this study, we show that compared to a static worst-case accident analysis, a dynamic consequence analysis provides a more rational approach for risk assessment and management of domino effects. This study also presents the application of Bayesian networks and conflict analysis to risk-based allocation of chemical inventories to minimize the consequences and thus to reduce the escalation probability. It emphasizes the risk management of chemical inventories as an inherent safety measure, particularly in existing process plants where the applicability of other safety measures such as safety distances is limited.

Domino effect analysis using Bayesian networks.

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier-studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.

Application of Process Hazard Analysis and Inherently Safer Design in Wood Pellet Production.

Wood pellets are a fuel used for heat and power generation. Wood pellets are manufactured from forest residues and byproducts of other sectors in the wood processing industry, such as sawmilling. Wood pellet production generates combustible wood dust, which presents the risk of fire and explosion. The objective of this research was to incorporate the principles of inherently safer design (ISD) for the management of combustible dust hazards associated with wood pellet production. Using bow tie analysis to explicitly consider ISD within process hazard analysis (PHA), ISD barriers were successfully identified, including the use of paved surfaces to store feedstock to minimize rocks entering the process and presenting a risk of ignition sources, the use of reduced-size silos to minimize the inventory and increase the turnover frequency, the removal of unnecessary or hazardous equipment, such as fans, following a redesign, and the relocation of hazardous equipment, such as cyclones, outside and away from personnel. A summary of example-based guidance for combustible dust hazards was collected to support additional ISD implementation within PHA as part of the process safety management (PSM). The research also highlights learnings for conducting virtual PHA workshops, as well as identifying opportunities for incorporating ISD within operating wood processing facilities through the incident investigation and risk assessment elements of PSM.

Assessment of COVID-19 barrier effectiveness using process safety techniques.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory illness called the novel coronavirus 2019 (COVID-19). COVID-19 was declared a pandemic on March 11, 2020. Bow tie analysis (BTA) was applied to analyze the hazard of SARS-CoV-2 for three receptor groups: patient or family member at the IWK Health Centre in acute care, staff member at a British Columbia Forest Safety Council (BCFSC) wood pellet facility, and staff member at the Suncor refinery in Sarnia, Ontario. An inherently safer design (ISD) protocol for BTA was used as a guide for evaluating COVID-19 barriers, and additional COVID-19 controls were recommended. Two communication tools were developed from the IWK bow tie diagram to disseminate the research findings. This research provides lessons learned about the barriers implemented to protect people from contracting COVID-19, and about the use of bow tie diagrams as communication tools. This research has also developed additional example-based guidance that can be used for the COVID-19 pandemic or future respiratory illness pandemics. Recommended future work is the application of BTA to additional industries, the consideration of ISD principles in other control types in the hierarchy of controls (HOC), and further consideration of human and organizational factors (HOF) in BTA.

Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.

Application of bow tie analysis and inherently safer design to the novel coronavirus hazard.

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

Pandemic risk management using engineering safety principles.

The containment of infectious diseases is challenging due to complex transmutation in the biological system, intricate global interactions, intense mobility, and multiple transmission modes. An emergent disease has the potential to turn into a pandemic impacting millions of people with loss of life, mental health, and severe economic impairment. Multifarious approaches to risk management have been explored for combating an epidemic spread. This work presents the implementation of engineering safety principles to pandemic risk management. We have assessed the pandemic risk using Paté-Cornell's six levels of uncertainty. The susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD), an advanced mechanistic model, along with the Monte Carlo simulation, has been used to estimate the fatality risk. The risk minimization strategies have been categorized into hierarchical safety measures. We have developed an event tree model of pandemic risk management for distinct risk-reducing strategies realized due to natural evolution, government interventions, societal responses, and individual practices. The roles of distinct interventions have also been investigated for an infected individual's survivability with the existing healthcare facilities. We have studied the Corona Virus Disease of 2019 (COVID-19) for pandemic risk management using the proposed framework. The results highlight effectiveness of the proposed strategies in containing a pandemic.

Effects of dust dispersibility on the suppressant enhanced explosion parameter (SEEP) in flame propagation of Al dust clouds.

This work presents an overview about the suppressant enhanced explosion parameter (SEEP) phenomenon in aluminum dust explosion moderation. The SEEP phenomenon can be attributed to either the flammable gas produced by decomposition of insufficient chemical suppressant so as to form an explosible hybrid mixture, or to the improvement in dust dispersibility caused by small amounts of thermal inhibitor so as to form better dispersed dust clouds. Aluminum (Al) and four particle sizes of alumina (Al2O3) were used to confirm a physically caused SEEP phenomenon by performing flame propagation experiments. Higher flame spread velocities (FSVs) in Al dust clouds were found in the presence of 5 or 10% <150 and <45-µm Al2O3 powder. Adding micro-sized Al2O3 disrupted inter-particle contact in combustible dusts, decreased inter-particle forces, and formed dust clouds with better dispersibility, thereby decreasing the effective particle size distribution (PSD) of Al dust. A strong thermal effect brought about by 2.5 µm Al2O3 overcame the negative effect of improved dispersion, preventing SEEP from occurring. The addition of 50 nm Al2O3 increased cohesion of powder mixtures, and decreased dust dispersibility. With benefits from both dispersion suppression and the thermal effect, Al flame propagation was well quenched.

Moderation of Al dust explosions by micro- and nano-sized Al2O3powder.

Adding solid inertants to combustible dust is one measure to prevent and mitigate dust explosions. Al2O3 at four particle size distributions was used to determine the minimum ignition energy (MIE) and maximum explosion pressure (Pmax) of aluminum dust and thus examine the effect of particle size on the inerting efficiency. It was interesting to observe that nano-sized Al2O3 powder showed excellent promise as a solid inertant, having inerting efficacy superior to that of micro-sized Al2O3. In addition to thermal inhibition, nano Al2O3 contributed to explosion moderation by binding Al particles together forming larger-sized aggregates that reduce dispersion in the dust clouds, and thus alleviate explosion hazards. Ignition sensitivity increased when micro-sized Al2O3 was admixed at 5 or 10% with 1000-1500 g/m3 Al mixtures, an effect apparently caused by a 20% decrease in effective particle size distribution brought on by the Al2O3 addition. Generally, increasing the amount of admixed Al2O3 increased MIE and decreased Pmax of Al dust clouds, and decreasing the particle size of Al2O3 resulted in better inerting performance on moderating both the likelihood of the ignition and the consequence of the explosion.

Ignition hazard of non-metallic dust clouds exposed to hotspots versus electrical sparks.

Non-metallic combustible dusts contribute to more than 50% of dust explosion accidents. Almost 38% of dust explosion accidents relate to mechanical malfunction. Compared to electric sparking as an ignition source, the ignition hazard of non-metallic dust clouds exposed to simulated hotspots during mechanical malfunction has received little attention in the literature. Minimum ignition temperature of hotspots (MITH) for corn starch, wood dust, and polymethyl methacrylate (PMMA) dust fall within a narrow range from 710 to 745 °C although large differences in minimum ignition energy (MIE) were evident. A much narrower dust concentration range (around 1500 g/m3) was observed for MITH than for MIE. A longer ignition delay time when exposed to hotspots also indicated lower ignition hazard compared to ignition by electric sparking. Whether exposed to hotspots or electric sparks, average flame spread velocity (FSV) of PMMA dust was much higher than that of corn starch and wood dust. Once a dust cloud was ignited, pulsating flame propagation was similar for hotspots and electric sparking, but average FSV was higher for hotspots than for electric sparks, due to continuous radiation from the ignition source. At higher dust loadings, layer fires could occur due to sedimentation of many ignited and unburned particles exposed to hotspots.

Subsea release of oil from a riser: an ecological risk assessment.

This study illustrates a newly developed methodology, as a part of the U.S. EPA ecological risk assessment (ERA) framework, to predict exposure concentrations in a marine environment due to underwater release of oil and gas. It combines the hydrodynamics of underwater blowout, weathering algorithms, and multimedia fate and transport to measure the exposure concentration. Naphthalene and methane are used as surrogate compounds for oil and gas, respectively. Uncertainties are accounted for in multimedia input parameters in the analysis. The 95th percentile of the exposure concentration (EC(95%)) is taken as the representative exposure concentration for the risk estimation. A bootstrapping method is utilized to characterize EC(95%) and associated uncertainty. The toxicity data of 19 species available in the literature are used to calculate the 5th percentile of the predicted no observed effect concentration (PNEC(5%)) by employing the bootstrapping method. The risk is characterized by transforming the risk quotient (RQ), which is the ratio of EC(95%) to PNEC(5%), into a cumulative risk distribution. This article describes a probabilistic basis for the ERA, which is essential from risk management and decision-making viewpoints. Two case studies of underwater oil and gas mixture release, and oil release with no gaseous mixture are used to show the systematic implementation of the methodology, elements of ERA, and the probabilistic method in assessing and characterizing the risk.

Safety assessment in plant layout design using indexing approach: implementing inherent safety perspective. Part 2-Domino Hazard Index and case study.

The design of layout plans requires adequate assessment tools for the quantification of safety performance. The general focus of the present work is to introduce an inherent safety perspective at different points of the layout design process. In particular, index approaches for safety assessment and decision-making in the early stages of layout design are developed and discussed in this two-part contribution. Part 1 (accompanying paper) of the current work presents an integrated index approach for safety assessment of early plant layout. In the present paper (Part 2), an index for evaluation of the hazard related to the potential of domino effects is developed. The index considers the actual consequences of possible escalation scenarios and scores or ranks the subsequent accident propagation potential. The effects of inherent and passive protection measures are also assessed. The result is a rapid quantification of domino hazard potential that can provide substantial support for choices in the early stages of layout design. Additionally, a case study concerning selection among various layout options is presented and analyzed. The case study demonstrates the use and applicability of the indices developed in both parts of the current work and highlights the value of introducing inherent safety features early in layout design.

Safety assessment in plant layout design using indexing approach: implementing inherent safety perspective. Part 1 - guideword applicability and method description.

Layout planning plays a key role in the inherent safety performance of process plants since this design feature controls the possibility of accidental chain-events and the magnitude of possible consequences. A lack of suitable methods to promote the effective implementation of inherent safety in layout design calls for the development of new techniques and methods. In the present paper, a safety assessment approach suitable for layout design in the critical early phase is proposed. The concept of inherent safety is implemented within this safety assessment; the approach is based on an integrated assessment of inherent safety guideword applicability within the constraints typically present in layout design. Application of these guidewords is evaluated along with unit hazards and control devices to quantitatively map the safety performance of different layout options. Moreover, the economic aspects related to safety and inherent safety are evaluated by the method. Specific sub-indices are developed within the integrated safety assessment system to analyze and quantify the hazard related to domino effects. The proposed approach is quick in application, auditable and shares a common framework applicable in other phases of the design lifecycle (e.g. process design). The present work is divided in two parts: Part 1 (current paper) presents the application of inherent safety guidelines in layout design and the index method for safety assessment; Part 2 (accompanying paper) describes the domino hazard sub-index and demonstrates the proposed approach with a case study, thus evidencing the introduction of inherent safety features in layout design.

Fire hazard of titanium powder layers mixed with inert nano TiO2 powder.

Metallic dust layers are highly sensitive to ignition from common ignition sources, even when mixed with high percentages of inert solids. In turn, dust layer fires are a potential ignition source for dust explosions or other damaging fires. Flame spread velocity (FSV), as a potential parameter for evaluating fire hazard, was investigated for titanium powder layers mixed with inert nano TiO2 powder in both natural convection and in forced airflow conditions. Increased mass percentage of nano TiO2 powder decreased FSV of Ti powder mixtures as expected. The mixing ratio of nano TiO2 to fully suppress layer fires was 80% and 90% for micro and nano Ti powder, respectively. Mechanisms governing flame spread across a layer of nano Ti powder differed from those of a layer of micro Ti powder. FSV in no airflow conditions was higher than in aided airflow for micro Ti powder because conduction was the dominant heat transfer mechanism. However, FSV in no airflow was lower than in opposed airflow for nano Ti powder because convection/radiation was the dominant heat transfer mechanism. A fly fire phenomenon contributed to greater FSVs and higher fire hazard with nano Ti powder mixtures under aided airflow conditions.

Dust explosion hazard of pulverized fuel carry-over.

This paper reports the results of experiments done to examine the explosibility of the waste products (fly ash and bottom ash) from pulverized fuels (coal and petroleum coke). Tests were conducted for the fly and bottom ashes alone and also for selected fly ashes blended with the fuels. The explosion parameters of interest were explosion pressure and rate of pressure rise. The fly ashes showed no propensity to explode, whereas one of the bottom ashes did show limited explosibility. Both findings can be explained with reference to the volatile matter content of the ashes. Admixture of either coal or petroleum coke with fly ash resulted in explosible mixtures at volatile contents in the range of 7-13%, with the value being dependent on the composition of the mixture components and their particle sizes.

An investigation of iron sulphide dust minimum ignition temperatures.

An investigation of the ignition behaviour of iron sulphide dusts has been undertaken. Commercial samples of FeS and FeS(2) and mine samples of pyrrhotite and pyrite were tested for minimum ignition temperature (MIT) using a device known as the BAM oven. The mine samples were found to undergo a decrease in MIT as the mass mean particle diameter became smaller. Using available theoretical treatments, this experimental observation was interpreted as providing further evidence of the importance of heterogeneous combustion in the ignition of iron sulphide dusts. A dense cloud state was proven for the experimental apparatus used, and an alternate criterion for the boundary between a dilute and a dense dust cloud was proposed in terms of the number of dust particles present in the cloud.