scholarly journals Simulation Model of Fragmentation Risk

2021 ◽  
Author(s):  
Mirko Djelosevic ◽  
Goran Tepic
Keyword(s):  
Risk Assessment ◽  
Simulation Model ◽  
Total Mass ◽  
Epistemic Uncertainty ◽  
Process Industry ◽  
Cylindrical Tank ◽  
Average Value ◽  
Accident Data ◽  
Fragmentation Probability ◽  
Launch Angle

In this chapter, a simulation model for fragmentation risk assessment due to a cylindrical tank explosion is presented. The proposed fragmentation methodology is based on the application of Monte Carlo simulation and probabilistic mass method. The probabilities of generating fragments during the explosion of the tank were estimated regardless of the available accident data. Aleatoric and epistemic uncertainty due to tank fragmentation has been identified. Generating only one fragment is accompanied by aleatoric uncertainty. The maximum fragmentation probability corresponds to the generation of two fragments with a total mass between 1200 kg and 2400 kg and is 17%. The fragment shape was assessed on the basis of these data and fracture lines. Fragmentation mechanics has shown that kinematic parameters are accompanied by epistemic uncertainty. The range of the fragments in the explosion of the tank has a Weibull distribution with an average value of 638 m. It is not justified to assume the initial launch angle with a uniform distribution, since its direction is defined by the shape of the fragment. The presented methodology is generally applicable to fragmentation problems in the process industry.

scholarly journals Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

2018 ◽  
Author(s):  
Michalis I. Vousdoukas ◽  
Dimitrios Bouziotas ◽  
Alessio Giardino ◽  
Laurens M. Bouwer ◽  
Evangelos Voukouvalas ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Flood Risk ◽  
Large Scale ◽  
Epistemic Uncertainty ◽  
Coastal Protection ◽  
Flood Risk Assessment ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policy-making and harmonization of climate change adaptation strategies. There is, however, limited insight on the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the Coastal Flood Risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea-level (ESL); (ii) inundation modelling; (iii) the underlying uncertainty in the Digital Elevation Model (DEM); (iv) flood defence information; (v) the assumptions behind the use of depth-damage functions that express vulnerability; and (vi) different climate change projections. The impact of these uncertainties to estimated Expected Annual Damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal and in the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, as well as their absolute/relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large-extent datasets with sufficient resolution and accuracy the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

scholarly journals Live wildlife trade in markets - a scoping review to inform risk assessment of emerging infectious diseases

2021 ◽  
Author(s):  
Victoria J Brookes ◽  
Okta Wismandanu ◽  
Etih Sudarnika ◽  
Justin A Roby ◽  
Lynne Hayes ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Infectious Diseases ◽  
Scoping Review ◽  
Epistemic Uncertainty ◽  
Mammalian Species ◽  
Emerging Infectious Diseases ◽  
World Health Organisation ◽  
World Health ◽  
Wildlife Trade ◽  
The Impact

Wet markets are important for food security in many regions worldwide but have come under scrutiny due to their potential role in the emergence of infectious diseases. The sale of live wildlife has been highlighted as a particular risk, and the World Health Organisation has called for the banning of live, wild-caught mammalian species in markets unless risk assessment and effective regulations are in place. Following PRISMA guidelines, we conducted a global scoping review of peer-reviewed information about the sale of live, terrestrial wildlife in markets that are likely to sell fresh food, and collated data about the characteristics of such markets, activities involving live wildlife, the species sold, their purpose, and animal, human, and environmental health risks that were identified. Of the 59 peer-reviewed records within scope, only 25% (n = 14) focussed on disease risks; the rest focused on the impact of wildlife sale on conservation. Although there were some global patterns (for example, the types of markets and purpose of sale of wildlife), there was wide diversity and huge epistemic uncertainty in all aspects associated with live, terrestrial wildlife sale in markets such that the feasibility of accurate assessment of the risk of emerging infectious disease associated with live wildlife trade in markets is limited. Given the value of both wet markets and wildlife trade and the need to support food affordability and accessibility, conservation, public health, and the social and economic aspects of livelihoods of often vulnerable people, there are major information gaps that need to be addressed to develop evidence-based policy in this environment. This review identifies these gaps and provides a foundation from which information for risk assessments can be collected.

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment

2021 ◽  
Author(s):  
Thomas Oscar
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Chicken Liver ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Serving Size ◽  
Monte Carlo Simulation Model

The first step in quantitative microbial risk assessment (QMRA) is to determine distribution of pathogen contamination among servings of the food at some point in the farm-to-table chain. In the present study, distribution of Salmonella contamination among servings of chicken liver for use in QMRA was determined at meal preparation. A combination of five methods: 1) whole sample enrichment; 2) quantitative polymerase chain reaction; 3) cultural isolation; 4) serotyping; and 5) Monte Carlo simulation were used to determine Salmonella prevalence (P), number (N), and serotype for different serving sizes. In addition, epidemiological data were used to convert serotype data to virulence (V) values for use in QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58/80) per 58 g. Four serotypes were isolated from chicken livers: 1) Infantis (P = 28%, V = 4.5); 2) Enteritidis (P = 15%, V = 5); 3) Typhimirium (P = 15%, V = 4.8); and 4) Kentucky (P = 15%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected ( P > 0.05) by serotype. The model predicted a non-linear increase ( P ≤ 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). The data acquired and model developed in this study fill an important data and modeling gap in QMRA for Salmonella and chicken liver.

scholarly journals Health Risk Assessment of Heavy Metals in Surface Water of the Lhasa River, China

2019 ◽  
Vol 98 ◽  
pp. 09011
Author(s):  
Liu He ◽  
Bai Gao ◽  
Huanhuan Qin ◽  
Junping Liu ◽  
Xiaoyan Ding ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Surface Water ◽  
Health Risk ◽  
River Water ◽  
Health Risk Assessment ◽  
Class Iii ◽  
Total Risk ◽  
Average Value ◽  
Lhasa River

This study was performed in Lhasa River Watershed to explore the contents of heavy metals in river water and assess the health risks. The test results show that the contents of arsenic (As), chromium (Cr) and uranium (U) are relatively low, reaching the Class III of the "Environmental quality standards for surface water (GB3838-2002)". Health risk assessment demonstrates that the average total risk value is 1.79×10-5 yr-1, which is lower than the maximum acceptable level of 5.05×10-5 yr-1 recommended by USEPA. Moreover, the average value of radionuclide risk (U) is calculated to be 9.47×10-9 yr-1, which is five orders of magnitude lower than the standard value of 5.0×10-4 yr-1. Therefore, the river water in the study area causes no significant health risk to people.

Sign in / Sign up

Export Citation Format

Share Document