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.

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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.

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Safety instrumented systems(SIS) have been widely used in petroleum and chemical plants to detect and respond to dangerous events and prevent them from developing into accidents. The in-service time of SIS does not exceed its useful life is one of the crucial assumptions of IEC functional safety standards. The testing method recommended in the IEC standard is essentially a chi-square testing, where the testing effect is proportional to the sample size and, therefore, not suitable for testing the type of data distribution under small samples. In this paper, a rapid inference method of useful life (RIUL) is proposed to: i) determine whether the distribution type of failure data is exponential under small samples with the help of Anderson-Darling testing, and ii) use the Bayesian sequential testing method for estimating the useful life. The sequential posterior odds ratio testing is introduced to test the equipment failure rate one by one. The proposed RIUL approach is applied to the liquid-level protection circuit of the hot high-pressure separator. The engineering simulation results show that compared with IEC standard methods, the proposed method can be performed with fewer failure data, providing a theoretical basis for reasonable maintenance and replacement of equipment.@en

Chemical process systems (CPSs) involve complex dynamic processes. Besides, the emergent and uncertain hazards and disruptions cannot be identified entirely and prevented by conventional methods. In those situations, resilience for CPSs plays an essential role in absorbing, adapting to disruptions, and restoring from damages. Systemic modeling plays a vital role in assessing resilience. A system-based analysis model, system-theoretic accident model, and process (STAMP) can provide a robust framework. This paper develops a comprehensive methodology to systematically model and assess system resilience. The STAMP is employed to model and analyze the system safety of a process system. A new method of dynamic resilience assessment is then proposed to quantify the resilience of the system. The proposed method is applied to the diesel oil hydrogenation system. The results show that it quantifies the resilience of complex process systems considering human and organizational factors in a dynamic manner.@en

Chemical process systems involve complex dynamic processes, and the state of the system often fluctuates during the production process. To ensure the continuation of production, these fluctuations are often ignored or processed online instead of shutting down the unit. However, the interdependence between components in the system is strong, and small fluctuations or faults will be propagated to downstream nodes in turn if the fluctuation is omitted or processed online. A large number of accident investigations prove that the system risk increments as the failure propagates. This may eventually cause the entire system to collapse, causing severe casualties, property losses, and environmental damage. However, little attention has been paid to this type of risk. To measure the dynamic risk profile considering the fluctuation of the production process, this paper proposes a new risk assessment model that integrates the system-theoretic accident model and process (STAMP) and the failure propagation model. Firstly, the STAMP is used to model and analyze the system safety of a process system. An approach is then developed to quantify the risk accumulation of the model based on the failure propagation model. The process of the Chevron Richmond refinery crude unit and its associated upstream process is used to demonstrate the application of the proposed approach.@en

Resilience is an emergent property of a system, which changes with various internal and external factors. Resilience is also a hidden property of a system that cannot be observed. Thus, experiments should be performed for a given system to measure its resilience. However, physical experiments are practically impossible. Inspired by the tensile test for the stress-strain curve in Material Science, this paper proposes a virtual experiment for measuring system resilience and applies it to a chemical process system. The physical parameters of system resilience of a process system are mapped to those of material resilience. A process system is viewed as a 'specimen' in this experiment. The system performance variation caused by disruptions is seen as the displacement of the specimen caused by the applied load. In absorption phase, the decrease speed of system performance is determined by the failure rate of components under disruptive condition. Response time, including fault diagnosis time and resource allocation time, is used to represent adaptation ability. Restoration ability depends on repair rate of components. For simplicity purpose, the proposed method is applied to resilience assessment of a release prevention barrier system used in the Chevron Richmond refinery crude unit and its associated upstream process.@en

Reliability-based and risk-based methods for directing maintenance activities play a critical role in ensuring system safety and reducing unnecessary downtime. Those methods focus on preventive maintenance to avoid component failures and are applicable before unexpected disruptions occur. However, when disruptions are unavoidable, more attention should be paid to systems’ recovery from unwanted changes. As a remedy of preventive maintenance, improving system restoration capacity of resilience through optimizing the system's maintenance asset and operational cost is an efficient way to help system restore from disruption conditions within an optimal cost. In this paper, a resilience-based approach is proposed to optimize maintenance asset and operational cost. A novel resilience metric is developed and utilized to quantify system resilience under various restoration capacities. The minimal acceptable resilience level (MARL) and maximal acceptable restoration time (MART) are proposed to determine the optimal maintenance cost. The proposed approach is applied to the Chevron Richmond refinery crude unit and its upstream process. The results show that it can help practitioners identify the optimal cost to ensure a system is resilient to respond to uncertain disruptions and provide a dynamic resilience profile to support decision-making.@en

Due to the rapid development of technology, process systems become dynamic, automated, and complex, resulting in the strong interdependence and interaction among components and ensuring system safety by conventional methods a challenge. Compared with traditional risk assessment methods, resilience assessment is a more appropriate method for ensuring the safety of process systems under uncertain disruptions. Resilience refers to absorbing and adapting to changing conditions and recovering from disruptions. This paper presents a comprehensive assessment model that combines the catastrophe theory (CT) with the dynamic Bayesian network (DBN) to measure dynamic resilience. Firstly, the CT is employed to quantify the intensity of disruptions. Subsequently, the performance response function (PRF) of the system is determined by DBN. A resilience metric is then introduced to measure system resilience under uncertain disruptions. The method is demonstrated through a release prevention barrier system.@en

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.

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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.

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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.

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To quantify the pandemic specific impact with respect to the risk related to the chemical industry, a novel risk analysis method is proposed. The method includes three parts. Firstly, the two types of “window of opportunity” (WO) theory is proposed to divide an accident life cycle into two parts. Then, a qualitative risk analysis is conducted based on WO theory to determine possible risk factors, evolution paths and consequences. The third part is a quantitative risk analysis based on a complex network model, integrating two types of WO. The Fuzzy set theory is introduced to calculate the failure probabilities of risk factors and the concept of risk entropy is used to represent the uncertainty. Then the Dijkstra algorithm is used to calculate the shortest path and the corresponding probability of the accident. The proposed method is applied to the SCR denitrition liquid ammonia storage and transportation system. The results show that it is a comprehensive method of quantitative risk analysis and it is applicable to risk analysis during the pandemic.@en

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.

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