Chemical process industries are complex environments prone to accidents with potentially drastic consequences. This ever-growing sector is becoming increasingly complex to satisfy the needs of global energy markets and consumer supply chains. Parallel to this growth and increased complexity, the number of accidents where conventional safety practices have failed to prevent them rises. Many relate such shortcomings in the preventive approach to the unattainability of imagining all failure scenarios. Moreover, conventional safety's reliance on linear causal chains impairs its ability to comprehend the hazardous conditions arising from human, societal, and organizational factors' interaction with the technical system. A further complicating factor adding to the ambiguity of these interactions is the digitization of chemical process industries. Rapid technology integration, application of novel tools, and associated methods could increase the possibility of deviating from normal operating conditions and result in hazardous conditions. Many believe resilience-oriented concepts, if not a paradigm shift in safety, could improve the shortcomings of conventional safety practices. Resilience's approach to safety recognizes that all expected deviations and unknown hazards cannot be prevented; therefore, it increases system readiness and strengthens the capacity to absorb, adapt, and recover from adverse events to prevent catastrophic failures. This chapter aims to provide inclusive yet brief insights into resilience, why the chemical process industries should strive to become resilient, how resiliency is achieved, and how it may be measured by reviewing the state-of-the-art published literature concerned with resilience assessment.@en

Modeling potential domino scenarios in process plants includes the prediction of the most probable sequence of events and the calculation of respective probabilities, so-called escalation probabilities, so that appropriate prevention and mitigation safety measures can be devised. Domino effect modeling, however, is very challenging mainly due to uncertainties involved in estimation of escalation probabilities (parameter uncertainty) and prediction of the sequence of events during a domino effect (model uncertainty). In the present study, a methodology based on dynamic Bayesian network is developed for identification of the most likely sequence of events in domino scenarios while accounting for model uncertainty. Verifying the accuracy of the methodology based on a comparison with previous studies, the methodology is applied to model single-primary-event and multiple-primary-event domino scenarios in process plants.@en

Digital technologies have been reshaping how the process industries operate. The extensive use of physical and information digitalization called by ambitious revolution to redesign process plants significantly transforms the process safety landscape in the process industries. Digitalization depends on the reliable use of data. This becomes a new focus of process safety in digitalized process systems. Digitalization brings the opportunity to generate and collect digital operational data and reduce human operation for effective process monitoring and control for safety assurance. The system's capability of processing massive data becomes essential for process safety. Meanwhile, digitalization increases the complexity of human-computer interaction and consequently leads to new research and practical problems. With standalone processes connected to the Internet of Things, process plants become more attractive to terrorists. This inevitably invites cybersecurity concerns to process safety solutions. This chapter provides a brief overview of these benefits and issues.@en