Operational Risk Assessment of Routing Flare Gas to Boiler for Cogeneration

Flaring is a controlled combustion process in which unwanted or excess hydrocarbon gases are released to flare stack for disposal. Flaring has a significant impact on environment, energy and economy. Flare gas integration to cogeneration plant is an alternative to mitigate flaring, benefiting from utilizing waste flare gas as a supplemental fuel to boilers and or gas turbines. Earlier studies have shown the energy and economic sustainability through integration. However, the impact of flare gas quality on cogeneration plants are yet to be identified. This paper studies the effect of flare gas composition and temperature from an ethylene plant to an existing boiler during abnormal flaring. The study proposes a unique framework which identifies the process hazards associated with variation in fuel conditions through process simulation and sensitivity analysis. Then, a systematic approach is used to evaluate the critical operational event occurrences and their impacts through scenario development and quantitative risk assessment, comparing a base case natural gas fuel with a variable flare gas fuel. An important outcome from this study is the identification of critical fuel stream parameters affecting the fired boiler operation through process simulation. Flare stream temperature and presence of higher molecular weight hydrocarbons in flare streams showed minimal effect on boiler condition. However, hydrogen content and rich fuel-air ratio in the boiler can affect the boiler operating conditions. Increase in the hydrogen content in flare to fuel system can increase the risk contour of cogeneration plant, affecting the boiler gas temperature, combustion mixture and flame stability inside the firebox. Quantitative risk analysis through Bayesian Network showed a significant risk escalation. With 12 hours of flare gas frequency per year, there is a substantial rise in the probability of occurrence of boiler gas temperature exceeding design limit and rich fuel mixture in the firebox due to medium and high hydrogen content gas in flare. The influence of these events on flame impingement and tube rupture incidents are noteworthy for high hydrogen content gas. The study also observed reduction in operational time as the hydrogen content in flare gas is increased from low to high. Finally, to operate fire tube steam boiler with flare gas containing higher amount of hydrogen, the existing cogeneration system needs to update its preventive safeguards to reduce the probability of loss control event

Operational Risk Assessment of Routing Flare Gas to Boiler for Cogeneration

Flaring is a controlled combustion process in which unwanted or excess hydrocarbon gases are released to flare stack for disposal. Flaring has a significant impact on environment, energy and economy. Flare gas integration to cogeneration plant is an alternative to mitigate flaring, benefiting from utilizing waste flare gas as a supplemental fuel to boilers and or gas turbines. Earlier studies have shown the energy and economic sustainability through integration. However, the impact of flare gas quality on cogeneration plants are yet to be identified. This paper studies the effect of flare gas composition and temperature from an ethylene plant to an existing boiler during abnormal flaring. The study proposes a unique framework which identifies the process hazards associated with variation in fuel conditions through process simulation and sensitivity analysis. Then, a systematic approach is used to evaluate the critical operational event occurrences and their impacts through scenario development and quantitative risk assessment, comparing a base case natural gas fuel with a variable flare gas fuel. An important outcome from this study is the identification of critical fuel stream parameters affecting the fired boiler operation through process simulation. Flare stream temperature and presence of higher molecular weight hydrocarbons in flare streams showed minimal effect on boiler condition. However, hydrogen content and rich fuel-air ratio in the boiler can affect the boiler operating conditions. Increase in the hydrogen content in flare to fuel system can increase the risk contour of cogeneration plant, affecting the boiler gas temperature, combustion mixture and flame stability inside the firebox. Quantitative risk analysis through Bayesian Network showed a significant risk escalation. With 12 hours of flare gas frequency per year, there is a substantial rise in the probability of occurrence of boiler gas temperature exceeding design limit and rich fuel mixture in the firebox due to medium and high hydrogen content gas in flare. The influence of these events on flame impingement and tube rupture incidents are noteworthy for high hydrogen content gas. The study also observed reduction in operational time as the hydrogen content in flare gas is increased from low to high. Finally, to operate fire tube steam boiler with flare gas containing higher amount of hydrogen, the existing cogeneration system needs to update its preventive safeguards to reduce the probability of loss control event

Operational risk assessment for shipping in Arctic waters

Arctic navigation has many complexities due to its particular features such as ice, severe weather conditions, remoteness, low temperatures, lack of crew experience, and extended period of darkness or daylight. For these reasons, vessels, such as oil tankers, dry cargo ships, offshore supply vessels, research vessels, and passenger ships operating in the Arctic waters may pose a high risk of collision with ice and other ships causing human casualties, environmental pollution and the loss of assets. This thesis presents a conceptual framework that is focused on collision modelling. In order to understand the process of risk escalation and to attempt a proactive approach in constituting the collision models for Arctic navigation, the present thesis identifies various risk factors that are involved in a collision. Furthermore, the thesis proposes the probabilistic framework tools that are based on the identified risk factors to estimate the risks of collision in the Arctic. The proposed frameworks are used to model the collision based risk scenarios in the region. They are developed with the use of Bayesian Networks, the Nagel-Schreckenberg (NaSch), and Human Factor Analysis and Classification (HFACS) models. In the present thesis, the proposed models are theoretical in nature, but they can be useful in developing a collision monitoring system that provides a real time-estimate of collision probability that could help avoid collisions in the Arctic. Further, the estimated probabilities are also useful in decision making concerning safe independent and convoy operations in the region. The proposed frameworks simplifies maritime accident modeling by developing a practical understanding of the role of physical environment, navigational and operational related aspects of ships, and human errors, such as individual lapses, management failures, organizational failures, and economic factors in the collision related accidents in the Arctic. This research also identifies the macroscopic properties of maritime traffic flow and demonstrates how these properties influence collision properties. The thesis also presents an innovative accident model for ice-covered waters that estimates the collision probability and establishes the relationship between the macroscopic properties of the traffic flow with the contributory accidental risk factors in the region. The main focus of the present thesis is, to better understand, communicate, and incorporate specific risk factors into the maritime risk assessment processes, involve shipping organizations to agree on best practice methodologies and make the data sources easily available, and modify the Arctic risk management processes by implementing effective risk assessment techniques and appropriate risk treatment

THE AVIATION RISK MANAGEMENT SOLUTIONS (ARMS) METHODOLOGY FOR OPERATIONAL RISK ASSESSMENT IN AVIATION ORGANISATIONS

 There is a fundamental conceptual problem with the risk assessment of (historical) events which needs to be recognized

Dynamic Operational Risk Assessment with Bayesian Network

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

DFT modeling approach for operational risk assessment of railway infrastructure

Reliability engineering of railway infrastructure aims to understand failure processes and to improve the efficiency and effectiveness of investments and maintenance planning such that a high quality of service is achieved. While formal methods are widely used to verify the design specifications of safety-critical components in train control, quantitative methods to analyze the service reliability associated with specific system designs are only starting to emerge. In this paper, we strive to advance the use of formal fault-tree modeling for providing a quantitative assessment of the railway infrastructure's service reliability in the design phase. While, individually, most subsystems required for route-setting and train control are well understood, the system's reliability to globally provide its designated service capacity is less studied. To this end, we present a framework based on dynamic fault trees that allows to analyze train routability based on train paths projected in the interlocking system. We particularly focus on the dependency of train paths on track-based assets such as switches and crossings, which are particularly prone to failures due to their being subject to weather and heavy wear. By using probabilistic model checking to analyze and verify the reliability of feasible route sets for scheduled train lines, performance metrics for reliability analysis of the system as a whole as well as criticality analysis of individual (sub-)components become available. The approach, which has been previously discussed in our paper at FMICS 2019, is further refined, and additional algorithmic approaches, analysis settings and application scenarios in infrastructure and maintenance planning are discussed

The Present, Future and Imperfect of Financial Risk Management

Current research on financial risk management applications of econometrics centres on the accurate assessment of individual market and credit risks with relatively little theoretical or applied econometric research on other types of risk, aggregation risk, data incompleteness and optimal risk control. We argue that consideration of the model risk arising from crude aggregation rules and inadequate data could lead to a new class of reduced form Bayesian risk assessment models. Logically, these models should be set within a common factor framework that allows proper risk aggregation methods to be developed. We explain how such a framework could also provide the essential links between risk control, risk assessments and the optimal allocation of resources.Financial risk assessment; risk control, RAROC, economic capital; regulatory capital; optimal allocation of resources