To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

Chemical process systems are becoming more automated and complex, which leads to increased interaction and interdependence between the human and technical elements of process systems. This urges the need for updating the safety assessment method by treating “safety” as an emergent property of a system. Uncertainty comes together with complexity. To enhance system ability of dealing with uncertain disruptions, this paper proposes a quantitative resilience assessment method by modeling the failure propagation (initiated by a disruption) across the functional units of a system. The Functional Resonance Analysis Method (FRAM) is utilized to model the system operation to represent the relationship among its function units and to consider the interactions among human-technical factors. Then, a Cascading Failure Propagation Model (CFPM) is developed to quantify the fault propagation process and reflect the system functionality changes over time for resilience assessment. The proposed method is applied to a propane-feeding control system. The results show that it can help practitioners understand the process of fault propagation and risk increase, identify potential ways to design a more resilient system to respond to uncertain disruptions/attacks, and provide a real-time dynamic resilience profile to support decision-making.

An incident may propagate to an accident with different severity dependent on its propagation scenarios. Since the accident propagation is a two-way process, the current research is focusing on the one-way analysis. This paper aims to analyze the combined effect of multi-units sources and their interactions during the accident propagation. The bi-directional connectivity diagram (BDCD) is applied to visualize the interactions between multiple process units as hazardous sources. The deployed safety barriers interrupt the connection between the hazardous sources and thus minimize the influence of one BDCD node on another. Through which, the accident propagation is reduced. The proposed method can be suitable to the general accidents, and it is applied to a case study of the LNG terminal station to assess the potential consequences of explosion caused by the leakage, in which the cost of the safety barrier is also considered. The BDCD approach is found more effective than traditional single-hazardous source methods for analyzing the accident propagation of multi-units sources in the chemical plant and achieving intrinsic safety.

The explosion load is a significant escalation factor possibly influencing the potential occurrence of domino accidents in chemical plants. It is not economical to install explosion isolation systems (e.g., extinguishing barrier) for all equipment or process units across a chemical plant. Although shutting down all equipment or process unit can prevent an explosion, it may also cause further economic losses. To prevent domino accidents, the process unit that needs to be shut down accurately should be selected, and the normal operation of other units needs to be ensured. A method to select the process unit to be isolated based on the Dimensioning Accidental Load (DAL) is proposed. By calculating the occurrence probability and consequences of the accident scenarios, the DAL of the surrounding units is determined. DAL is used as the impact intensity of the accident unit on the surrounding units. The probit model is used to calculate the damage probability of surrounding units. The case analysis results show that the method of selecting the process unit to be isolated based on DAL quantifies the impact intensity of the exploded unit on surrounding units from probability and consequence. Under the premise of meeting the acceptable risk criteria, the method can determine which units should be shut down and which units can operate normally when a release accident occurs. While preventing domino accidents, economic losses caused by the shutdown of all process units are reduced and a theoretical basis for accident prevention and safe operation of the plant is provided.

The performance assessment of safety barriers is essential to find vulnerable elements in a safety barrier system. Traditional performance assessment approaches mainly focus on using several static indicators for quantifying the performance of safety barriers. However, with the increasing complexity of the system, emerging hazards are highly uncertain, making it challenging for the static indicators to assess the performance of safety barriers. This paper proposes a resilience−based performance assessment method for safety barriers to overcome this problem. Safety barriers are classified according to their functions first. The dynamic Bayesian network (DBN) is then introduced to calculate the availability function under normal and disruption conditions. The ratio of the system's availability, when affected by the disruption, to the initial availability, is used to determine the absorption capacity of the system. The ratio of the quantity of availability recovery to the total quantity of system represents the adaptation and restoration capacity of the system. The system's resilience is represented by the sum of absorption, adaptation, and restoration capacities. The wax oil hydrogenation process is used to demonstrate the applicability of the proposed methodology.

Quantitative risk assessment is a crucial step in the safety analysis of process systems. The advancement of modern process systems has made a large volume of process data and information available for process safety analysis. This tendency urges the need for developing new risk assessment approaches. Fault tree (FT), a conventional risk analysis method, is found to be ineffective in dynamic risk analysis and data analytics due to its static nature and reliance on experts' judgment. Artificial Neural Network (ANN) is a structured model built upon data samples and learning algorithms to process complex input/output data in the way that it is being trained. The application of ANN can help to overcome some of the limitations of FT. The data-driven nature, independency on prior information on events relationships, and less reliance on experts’ judgment are the advantages of ANN over FT. The use of ANN in risk assessment is not a new concept. However, there is limited work on the development of ANN-based risk assessment models using conventional methods such as FT as an informative base. This study proposes a methodology for mapping FT into ANN to support the convenient and practical application of ANN in risk assessment. The proposed method is demonstrated through its application to the analysis of a system failure in the Tesoro Anacortes Refinery accident. The results have shown that the ANN model mapped from the FT is an effective risk assessment technique.