In oil/gas and chemical industries, dynamics is one of the most essential characteristics of any process. Time-dependent response is involved in most steps of both the physical/engineering processes and the equipment performance. The conventional Quantitative Risk Assessment (QRA) is unable to address the time dependent effect in such dynamic processes. In this dissertation, a methodology of Dynamic Operational Risk Assessment (DORA) is developed for operational risk analysis in oil/gas and chemical industries. Given the assumption that the component performance state determines the value of parameters in process dynamics equations, the DORA probabilistic modeling integrates stochastic modeling and process dynamics modeling to evaluate operational risk. The stochastic system-state trajectory is modeled based on the abnormal behavior or failure of the components. For each of the possible system-state trajectories, a process dynamics evaluation is carried out to check whether process variables, e.g., level, flow rate, temperature, pressure, or chemical concentration, remain in their desirable regions. Monte Carlo simulations are performed to calculate the probability of process variable exceeding the safety boundaries. Component testing/inspection intervals and repair time are critical parameters to define the system-state configuration; and play an important role for evaluating the probability of operational failure. Sensitivity analysis is suggested to assist selecting the DORA probabilistic modeling inputs. In this study, probabilistic approach to characterize uncertainty associated with QRA is proposed to analyze data and experiment results in order to enhance the understanding of uncertainty and improve the accuracy of the risk estimation. Different scenarios on an oil/gas separation system were used to demonstrate the application of DORA method, and approaches are proposed for sensitivity and uncertainty analysis. Case study on a knockout drum in the distillation unit of a refinery process shows that the epistemic uncertainty associated with the risk estimation is reduced through Bayesian updating of the generic reliability information using plant specific real time testing or reliability data. Case study on an oil/gas separator component inspection interval optimization illustrates the cost benefit analysis in DORA framework and how DORA probabilistic modeling can be used as a tool for decision making. DORA not only provides a framework to evaluate the dynamic operational risk in oil/gas and chemical industries, but also guides the process design and optimization of the critical parameters such as component inspection intervals.

Dynamic Risk Analysis in the Chemical and Petroleum Industry focuses on bridging the gap between research and industry by responding to the following questions: What are the most relevant developments of risk analysis? How can these studies help industry in the prevention of major accidents? Paltrinieri and Khan provide support for professionals who plan to improve risk analysis by introducing innovative techniques and exploiting the potential of data share and process technologies. This concrete reference within an ever-growing variety of innovations will be most helpful to process safety managers, HSE managers, safety engineers and safety engineering students. This book is divided into four parts. The Introduction provides an overview of the state-of-the-art risk analysis methods and the most up-to-date popular definitions of accident scenarios. The second section on Dynamic Risk Analysis shows the dynamic evolution of risk analysis and covers Hazard Identification, Frequency Analysis, Consequence Analysis and Establishing the Risk Picture. The third section on Interaction with Parallel Disciplines illustrates the interaction between risk analysis and other disciplines from parallel fields, such as the nuclear, the economic and the financial sectors. The final section on Dynamic Risk Management addresses risk management, which may dynamically learn from itself and improve in a spiral process leading to a resilient system. Helps dynamic analysis and management of risk in chemical and process industry Provides industry examples and techniques to assist you with risk- based decision making Addresses also the human, economic and reputational aspects composing the overall risk picture

Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry provides insights into emerging and state-of-the-art methods for the dynamic assessment of risk and safety barrier performance in the framework of domino effect risk management. The book presents methods and tools to manage the risk of cascading events involving the chemical and process industry. It is an ideal reference for both safety and security managers, industrial risk stakeholders, scientists and practitioners. In addition, laymen may find the state-of-the-art methods concerning domino effects (large-scale accidents) and how to prevent their propagation an interesting topic of study. Includes dynamic hazard and risk assessment methods Presents methods for safety barrier performance assessment Addresses the effect of harsh environment on domino risk assessment Relates physical security in relation to domino effects Includes innovative methods and tools

Oil/gas and petrochemical plants are complicated and dynamic in nature. Dynamic characteristics include ageing of equipment/components, season changes, stochastic processes, operator response times, inspection and testing time intervals, sequential dependencies of equipment/components and timing of safety system operations, all of which are time dependent criteria that can influence dynamic processes. The conventional risk assessment methodologies can quantify dynamic changes in processes with limited capacity. Therefore, it is important to develop method that can address time-dependent effects. The primary objective of this study is to propose a risk assessment methodology for dynamic systems. In this study, a new technique for dynamic operational risk assessment is developed based on the Bayesian networks, a structure optimal suitable to organize cause-effect relations. The Bayesian network graphically describes the dependencies of variables and the dynamic Bayesian network capture change of variables over time. This study proposes to develop dynamic fault tree for a chemical process system/sub-system and then to map it in Bayesian network so that the developed method can capture dynamic operational changes in process due to sequential dependency of one equipment/component on others. The developed Bayesian network is then extended to the dynamic Bayesian network to demonstrate dynamic operational risk assessment. A case study on a holdup tank problem is provided to illustrate the application of the method. A dryout scenario in the tank is quantified. It has been observed that the developed method is able to provide updated probability different equipment/component failure with time incorporating the sequential dependencies of event occurrence. Another objective of this study is to show parallelism of Bayesian network with other available risk assessment methods such as event tree, HAZOP, FMEA. In this research, an event tree mapping procedure in Bayesian network is described. A case study on a chemical reactor system is provided to illustrate the mapping procedure and to identify factors that have significant influence on an event occurrence. Therefore, this study provides a method for dynamic operational risk assessment capable of providing updated probability of event occurrences considering sequential dependencies with time and a model for mapping event tree in Bayesian network.

Risk Analysis and Control for Industrial Processes - Gas, Oil and Chemicals provides an analysis of current approaches for preventing disasters, and gives readers an overview on which methods to adopt. The book covers safety regulations, history and trends, industrial disasters, safety problems, safety tools, and capital and operational costs versus the benefits of safety, all supporting project decision processes. Tools covered include present day array of risk assessment, tools including HAZOP, LOPA and ORA, but also new approaches such as System-Theoretic Process Analysis (STPA), Blended HAZID, applications of Bayesian data analytics, Bayesian networks, and others. The text is supported by valuable examples to help the reader achieve a greater understanding on how to perform safety analysis, identify potential issues, and predict the likelihood they may appear. Presents new methods on how to identify hazards of low probability/high consequence events Contains information on how to develop and install safeguards against such events, with guidance on how to quantify risk and its uncertainty, and how to make economic and societal decisions about risk Demonstrates key concepts through the use of examples and relevant case studies

Risk Assessment Explore the fundamentals of risk assessment with references to the latest standards, methodologies, and approaches The Second Edition of Risk Assessment: A Practical Guide to Assessing Operational Risks delivers a practical exploration of a wide array of risk assessment tools in the contexts of preliminary hazard analysis, job safety analysis, task analysis, job risk assessment, personnel protective equipment hazard assessment, failure mode and effect analysis, and more. The distinguished authors discuss the latest standards, theories, and methodologies covering the fundamentals of risk assessments, as well as their practical applications for safety, health, and environmental professionals with risk assessment responsibilities. “What If”/Checklist Analysis Methods are included for additional guidance. Now in full color, the book includes interactive exercises, links, videos, and online risk assessment tools that can be immediately applied by working practitioners. The authors have also included: Material that reflects the latest updates to ISO standards, the ASSP Technical Report, and the ANSI Z590.3 Prevention through Design standard New hazard phrases for chemical hazards in the Globally Harmonized System, as well as NIOSH’s new occupational exposure banding tool The new risk-based approach featured in the NAVY IH Field Manual New chapters covering business continuity, causal factors analysis, and layers of protection analysis and barrier analysis An indispensable resource for employed safety professionals in a variety of industries, business leaders and staff personnel with safety responsibilities, and environmental engineers Risk Assessment: A Practical Guide to Assessing Operational Risks is also useful for students in safety, health, and environmental science courses.

This book derives from the Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) that has been launched as a joint issue of the journals Materials and Applied Sciences. The 29 contributions published in this Special Issue of Materials present cutting-edge advances in the field of manufacturing engineering focusing on additive manufacturing and 3D printing; advances and innovations in manufacturing processes; sustainable and green manufacturing; manufacturing of new materials; metrology and quality in manufacturing; industry 4.0; design, modeling, and simulation in manufacturing engineering; and manufacturing engineering and society. Among them, the topic "Additive Manufacturing and 3D Printing" has attracted a large number of contributions in this journal due to its widespread popularity and potential.