Dynamic risk assessment and fault detection in process facilities

Zadakbar, Omid (2014) Dynamic risk assessment and fault detection in process facilities. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

A new multivariate risk-based fault detection and diagnosis technique targeting the safety issues of a process system is being proposed. In contrast to typical fault detection methods which only aim to detect operational faults that affect the control objectives of the process, this method targets the safety of the process. Typical fault detection and diagnosis methods are inadequate as none of the methods considers the consequences of the fault on process safety, integrity and the environment. However the proposed method provides a dynamic process risk indication based on the probability of occurrence of a fault and its consequences. In this method, the consequence is expressed in economic value that demonstrates the potential economic impact of the fault on the process, equipment, workers and the environment. Through this approach, warning system and risk management strategies may be activated when the risk of operation exceeds the acceptable threshold. This is an important concept because it can direct the attention and effort of operators to the faults which poses the most operational or safety risk. Both model based and history based fault detection and diagnosis techniques have been extended to a risk-based fault detection and diagnosis framework. Application of this new risk-based approach provides early warnings and early activation of safety systems prior to the fault impacting the system. This multivariate technique provides much early warning compared to the univariate methods. It has more power in discerning between operational changes and abnormal conditions which have potential to cause accidents. The main benefits of this approach are improved safety, minimum interruption of operation, better alarm management or early warning system and higher availability of process. The novelties and contributions of this work are development of multivariate dynamic risk assessment methodology using history based and model based methods for linear and nonlinear models combined with a newly developed economic consequence analysis methodology. This methodology makes the severity of the faults more sensible by quantifying consequences in economic terms. This new economic consequence methodology helps to integrates real time process state to accident scenarios via loss functions. The proposed framework when implemented on a process could serve as a real-time process risk monitor. This would help to take preventive actions in order to minimize process risks.

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