Dynamic simulation for risk analysis: Application to an exothermic reaction

This report concentrates on dynamic probabilistic risk analysis of optical elements for complex characterizationof damages using physical model of solid state lasers and predictable level of ionizing radiation and space weather.The following main subjects will be covered by our report: (a) a solid-state laser model; (b) mathematical modelsfor dynamic probabilistic risk assessment; and (c) software for modeling and prediction of ionizing radiation. Aprobabilistic risk assessment method for solid-state lasers is presented with consideration of some deterministicand stochastic factors. Probabilistic risk assessment is a comprehensive, structured, and logical analysis methodaimed at identifying and assessing risks in solid-state lasers for the purpose of cost-eectively improving theirsafety and performance. This method is based on the Conditional Value-at-Risk measure (CVaR) and theexpected loss exceeding Value-at-Risk (VaR). We propose a new dynamical-information approach for radiationdamage risk assessment of laser elements by cosmic radiation. Our approach includes the following steps: (a)lasermodeling, modeling of ionizing radiation in”uences on laser elements, (b) probabilistic risk assessment methods,and (c) risk minimization. For computer simulation of damage processes at microscopic and macroscopic levelsthe following methods are used: (a) statistical; (b) dynamical; (c) optimization; (d) acceleration modeling, and(e) mathematical modeling of laser functioning. Mathematical models of space ionizing radiation in”uence onlaser elements were developed for risk assessment in laser safety analysis. This is a so-called black box orinput-output model, which seeks only to reproduce the behaviour of the systems output in response to changesin its inputs. The model inputs are radiation in”uences on laser systems and output parameters are dynamicalcharacteristics of the solid laser.Keywords: risk analysis, laser elements, damage, global optimization, prediction

The need to develop an integrated dynamic safety and risk analysis model for decommissioning offshore jacket structures is driven by the risky, expensive and complex nature of the operation. Many of the existing risk analysis techniques applicable to offshore assets failed to recognise and capture evolving risks during different stages of the decommissioning operation. This paper describes risk-based safety model to conduct quantitative risk analysis for offshore jacket decommissioning failure. First, a bow-tie technique is developed to model the accident cause-consequence relationship. Subsequently, a Bayesian belief network is used to update the failure probabilities of the contributing elements and thus, provides a more case-specific and realistic safety analysis when compared to the static nature of a bow-tie. This paper also presents the application of experiential learning in the dynamic safety analysis. The proposed technique is tested using a real-life case study from the Shell Brent Alpha platform. An algorithm to limit the effect of generic failure data was also developed. It is observed that the proposed technique helps to identify hazards shortly before they occur and sensitivity analysis revealed the most critical elements of the operation that must be managed to prevent catastrophe and consequently, reduce associated costs of remediation.

A dynamic risk analysis model of offshore fire and explosion is proposed in this paper. It considers the effect of human and organizational factors in a more explicit way than current traditional risk analysis methods. This paper begins with exploring the recent advances on offshore fire and explosion risk analysis theories, followed by briefly introducing the research techniques employed in the proposed hybrid causal logic model which consists of event tree, fault tree, Bayesian network, and system dynamics. Thereafter, it proposes a quantitative risk analysis framework. At last, the applicability of this model to the offshore platform is also discussed. It aims to provide guideline for risk analysis of offshore fire and explosion.

Safety in tunnels is being contemplated through a holistic approach due to accidents that occurred through years. Technological innovations have led to the tunnel concept evolution from civil to technological infrastructure, where technology overcomes the limits of the geometric design, increasing its operating capacity. This paper aims to illustrate SCADRA principles (Supervisory Control Acquisition and Dynamic Risk Analysis), developed thanks to EURAM (EUropean Risk Analysis Method) methodology, which assessed the risk of over 600 road tunnel tubes, focusing on the following values: users and managers' needs (Human-centricity), usage of green technologies and application of energy saving strategy (Sustainability), and improvement of tunnels resilience (Resilience). SCADRA is used in monitoring continuously the tunnel state by gathering the safety influence variables and performing a dynamic risk analysis, quantitative and probabilistic, in real-time. The system was installed in three Italian road tunnels, executing over 130,000 instantaneous risk analyses. The applications’ results and the future developments will be presented according to tunnels’ safety improvement, maintenance, and management.

Purpose Supply chain managers must balance accuracy, timeliness and accessibility of information to make good decisions on risk, preventing or mitigating un-acceptable risks. Thus, any risk assessment method must strike a practical balance between the three. The risk matrix is popular in Supply Chain Risk Management (SCRM) as it reduces complexity of more quantitative risk analyses, by categorising likelihood and impact into qualitative constructs or semi-quantitative scores. This makes SCRM much more realistic and understandable to non-quantitative risk managers. However, while it is simple and easy to use, it is also, according to a now substantial literature, not fit for purpose due to numerous mathematical flaws which are discussed in this article. Design/methodology/approach This paper reassesses the SCRM domain when it comes to making good decisions based on increasingly complex amounts of qualitative and quantitative data. Findings The advantages and disadvantages of both the risk matrix and Quantitative Risk Analysis (QRA) are discussed, providing some solutions for overcoming the majority of the mathematical flaws of the risk matrix. Practical implications A more appropriate, fit-for-purpose approach toward the supply chain risk management matrix is offered. Originality/value Often poor implementation, rather than a structural issue with the risk matrix approach is evident in practice. The criticisms of risk matrices can be overcome, producing a more reliable way to assess and manage supply chain risk.

IT Governance is the structure of process relationships that guide and control an organization to achieve its vision and mission by adding value that will balance risks with IT and its processes. The analytical method used in this research is quantitative and qualitative risk analysis. The Quantitative Risk Analysis (QRA) approach focuses on analyzing the maintenance of IT resources to find risk factors that need serious consideration and treatment. For the qualitative risk analysis method, NIST SP 800-30 is used to analyze the various threat and risk attributes to provide guidelines for managing IT installations at XYZ Campus. Based on the QRA risk assessment, Internal HR who has access to the server is calculated as the highest potential campus loss. This can be seen in the risk aspect where losses caused by Internal HR who play the role of server admins have the greatest potential for losses. Qualitative assessment of risk management finds sources of threats with high risk are Internal HR and IT Infrastructure Systems. This level of risk can be detected during the hazard source classification process. The presentation of all risk analysis results can provide risk recommendations that will be communicated to campus IT management. To then be able to assist the campus in making a decision that includes policies, procedures, budgets, system operations, and change management.

Chapter II - Evaluation of Hazard and Risk Analysis

The evaluation of ignition probabilities is a fundamental step within the calculation of hazardous phenomena frequencies associated to a loss of containment of a flammable product (explosion, pool fire, jet fire). An error on this step can substantially change quantitative risk analysis conclusions.It is important to keep in mind that quantitative risk analysis are increasingly used to design facilities. In other words, facilities (equipment, structures and buildings) are designed to withstand accidental loads up to down a certain frequency. Three main approaches are commonly used for ignition probabilities calculation: tabulated values ??from databases, semi-quantitative methods or more or less sophisticated mathematical models.Nowadays, time dependent models are implemented to predict the evolution of the ignition probability as function of time. These models are particularly useful when combined with Computational Fluid Dynamics (CFD) calculations to provide flammable gas volume build up as function of time. Thus, it is possible to predict the evolution of the delayed ignition probability as function of time. Main interest of these models is to propose a discretization by time intervals of the delayed ignition probability. This approach is particularly relevant for performing explosion risk analysis.

Risk analysis of well blowout scenarios during managed pressure drilling operation

Particle classification: K. Heiskanen Chapman & Hall, London, 1993. Price £40. ISBN 0 412 49300 4

Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model

The article proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called supervisory control and dynamic risk analysis (SCADRA). SCADRA achieves operation and management benefits for safety, functionality, and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface) for real-time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements could not be realized.

The paper proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called SCADRA (Supervisory Control and Dynamic Risk Analysis). SCADRA achieves operation and management benefits for safety, functionality and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface HMI) for real time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements couldn't be realized.

Construction projects are inherently associated with various risks that can increase the project costs. Identifying and analyzing such risks are essential to successfully managing construction budgets at state Departments of Transportation (DOTs). While many state DOTs have developed and/or adopted risk assessments methodologies and tools, Tennessee DOT (TDOT) lacks a comprehensive methodology and tool to analyze construction project risks. This study reviewed state DOT practices of analyzing various risks associated with large infrastructure construction projects. The comparison of risk assessment practices among state DOTs are summarized in three sections: a) risk management, b) major categories affecting project costs, and c) risk estimating tools and practices. The review finds that many state DOTs have similar practices of analyzing project risks and have developed spreadsheet-based tools to automate such analysis. While some risk analysis tools are used primarily for qualitative risk analysis, other tools are developed for qualitative and quantitative risk analysis. Such tools with qualitative and quantitative risk analysis can be used to quantify contingencies for various projects for budgeting purposes.

Incidents in highly hazardous process industries (HHPI) are a major concern for various stakeholders due to the impact on human lives, environment, and potentially huge financial losses. Because process activities, location and products are unique, risk analysis techniques applied in the HHPI has evolved over the years. Unfortunately, some limitations of the various quantitative risk analysis (QRA) method currently employed means alternative or more improved methods are required. This research has obtained one such method called Big Data QRA Method. This method relies entirely on big data techniques and real-time process data to identify the point at which process risk is imminent and provide the extent of contribution of other components interacting up to the time index of the risk. Unlike the existing QRA methods which are static and based on unvalidated assumptions and data from single case studies, the big data method is dynamic and can be applied to most process systems. This alternative method is my original contribution to science and the practice of risk analysis The detailed procedure which has been provided in Chapter 9 of this thesis applies multiple change-point analysis and other big data techniques like, (a) time series analysis, (b) data exploration and compression techniques, (c) decision tree modelling, (d) linear regression modelling. Since the distributional properties of process data can change over time, the big data approach was found to be more appropriate. Considering the unique conditions, activities and the process systems use within the HHPI, the dust fire and explosion incidents at the Imperial Sugar Factory and the New England Wood Pellet LLC both of which occurred in the USA were found to be suitable case histories to use as a guide for evaluation of data in this research. Data analysis was performed using open source software packages in R Studio. Based on the investigation, multiple-change-point analysis packages strucchange and changepoint were found to be successful at detecting early signs of deteriorating conditions of component in process equipment and the main process risk. One such process component is a bearing which was suspected as the source of ignition which led to the dust fire and explosion at the Imperial Sugar Factory. As a result, this this research applies the big data QRA method procedure to bearing vibration data to predict early deterioration of bearings and final period when the bearing’s performance begins the final phase of deterioration to failure. Model-based identification of these periods provides an indication of whether the conditions of a mechanical part in process equipment at a particular moment represent an unacceptable risk. The procedure starts with selection of process operation data based on the findings of an incident investigation report on the case history of a known process incident. As the defining components of risk, both the frequency and consequences associated with the risk were obtained from the incident investigation reports. Acceptance criteria for the risk can be applied to the periods between the risks detected by the two change-point packages. The method was validated with two case study datasets to demonstrate its applicability as procedure for QRA. The procedure was then tested with two other case study datasets as examples of its application as a QRA method. The insight obtained from the validation and the applied examples led to the conclusion that big data techniques can be applied to real-time process data for risk assessment in the HHPI.