Dynamic operational risk assessment in process safety management

Abstract

Quantitative risk assessment (QRA) plays a fundamental role in ensuring the safety of process operations, and it was widely used in process design, implementation of safety system, and integrity of process installations. One of QRA's major disadvantages is its inability to update risk during the life of process systems when new observations are available. Recently, conventional process systems are being automatized and digitalized in the Industry 4.0 environment. Developing dynamic risk assessment technologies to support the digitalized safety management and decision-making are becoming an imperative trend. This chapter presents two dynamic risk assessment methods of process operations, which have the ability to handle different type of dynamic process data. The first is a Bayesian approach that is established by integrating Bayesian network (BN) and Hierarchical Bayesian analysis (HBA). The Bayesian approach can perform dynamic risk assessment using precursor data. The second is a data-driven approach that is developed in the framework of machine learning technique. The data-driven approach can carry out dynamic risk assessment using monitoring data of process system. These two approaches are illustrated by two specific cases, namely, dynamic risk assessment of subsea pipeline leak and corrosion degradation of subsea oil pipelines.