Consequence risk analysis using operating procedure event trees and dynamic simulation

Faults due to human errors cost the petrochemical industry billions of dollars every year and can have adverse environmental consequences. Unquantified human error probabilities exist during process state transitions performed each day by process operators using standard operating procedures. Managing the risks associated with operating procedures is an essential part of managing the overall safety risk. Additional operator training and safety education cannot eliminate all such faults due to human errors; therefore, we propose an operating procedure event tree (OPET) like analysis with branches and events specifically designed to perform risk analysis on operating procedures. The OPET method adapts event trees to analyze the risk due to human error while performing operating procedures. We consider human error scenarios during the procedure and determine the likely consequences by applying dynamic simulation. The modified event tree provides an estimate of the error frequencies.Operating procedure steps were developed, and potential operator faults were determined for two typical equipment switching procedures found in chemical plant operations. Then, dynamic simulation using Aspen HYSYS software was applied to determine the overpressure related consequences of each fault. Finally, the error frequencies resulting from those scenarios were analyzed using operating procedure event trees. We found that a typical ethylene plant gas header would overpressure with 0.6% frequency per manual dryer switch. Since dryer switches occur from every few days up to once per shift, these results suggest that dryer switching should be automated to ensure safe and environmentally friendly operation. Process dryer switching performed manually by operators opening and closing gate valves can be automated with control valves and a distributed control system. A sample distillation column was found to overpressure with 0.85% frequency per manual reflux pump switch.     

Shelter-in-place risk assessment for high-pressure natural gas wells with hydrogen sulphide and its application in emergency management

This paper provides a risk assessment method of sheltering in-place for high-pressure natural gas wells with hydrogen sulphide. In this paper, the shelter-in-place risk is estimated by integrating the health consequences of an individual taking one kind of emergency response to the emergency orders of sheltering in place from the emergency decision makers and the probability of the corresponding emergency response action. The probability of the corresponding emergency response action in the proposed method is estimated through the accident probability analysis and the probability analysis of taking a certain response action. The health consequence estimation is based on air exchange rate test of the shelter buildings as well as accident consequence calculation. The evaluation of shelter-in-place risks based on “as low as reasonably practicable (ALARP)” guidelines was employed to provide suggestions for emergency management under both normal conditions and off normal conditions. A case study of risk assessment of sheltering in the local residential houses in Xuanhan County of Sichuan Province, China was taken as an example to illustrate the proposed risk assessment process of shelter-in-place and its application in the decision-making process for emergency management.     

Risk analysis of Chongqing urban rail transit network

Urban rail network safety is a critical sector of urban public safety. However, there is no uniform standard for the safety evaluation of the urban rail network. This paper presents a novel methodology by integrating a multilevel decision tree with a fuzzy analytical approach to enhance urban rail network safety. The proposed methodology overcomes serious limitations such as subjectivity in the data and independence of the variables in decision-making processes. The proposed methodology is applied to the risk evaluation of the selected Chongqing rail transit lines and the Expo Line. The risk analysis is considered using the field data collected from these transit lines. The applied case studies confirm the general applicability of the methodology and the multilevel decision tree network. The main risk factors identified for the Chongqing rail traffic system are the terrorist threat, emergency management, and aging infrastructure which need to be investigated as a priority to mitigate risk associated with these infrastructures.     

Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.     

Allocation of performance shaping factors in the risk assessment of an offshore installation

Offshore oil production is one of the most important human productive activities. There are many risks associated with the process of constructing a subsea well, pumping oil to the platform, and transporting it to refineries via underwater pipes or oil tankers. All actions performed by workers in those operations are influenced by specific working conditions, involving the use of complex systems. Contextual factors such as high noise, low and high temperatures and hazardous chemicals are considered to be contributors to unsafe human actions in accident analysis and also give a basis for assessing human factors in safety analysis. Some failure modes are particularly dangerous and can result in severe accidents and damage to humans, the environment and material assets. Fires and explosions on oil rigs are some of the most devastating types of offshore accidents and can result in long-term consequences. The most typical root causes related to accidents include equipment failure, human error, environmental factors, work organization, training and, communication, among others. The principal objective of this study is to propose a methodological framework to identify the factors that affect the performance of operators of an offshore unit for oil processing and treatment. In this phase, an ergonomics approach based on operators' work analysis is used as a supporting tool. After identification of factors that affect the performance of operators, a decision-making model based on AHP (analytic hierarchy process) is applied to rank and weight the principal performance shaping factors (PSFs) that influence safe operations. The next step involves the use of the SHELLO model to group the main PSFs in elements named software, hardware, environment, liveware and organization. In the last phase, a relevant accident that occurred aboard a floating production storage and offloading (FPSO) vessel is analyzed. The allocation process of the factors that affect the operator's performance in risk assessment was developed through fuzzy logic and the ISO 17776 standard.     

Critical aspects for collision induced oil spill response and recovery system in ice conditions: A model-based analysis

The recovery effectiveness for oil spills in ice conditions depends on a complex system and has not been studied in depth, especially not from a system risk control perspective. This paper aims to identify the critical aspects in the oil spill system to enable effective oil spill recovery. First, a method is developed to identify critical elements in a Bayesian Network model, based on an uncertainty-based risk perspective. The method accounts for sensitivity and the strength of evidence, which are assessed for the different Bayesian Network model features. Then, a Bayesian Network model for the mechanical oil spill recovery system is developed for the Finnish oil spill response fleet, contextualized for representative collision accident scenarios. This model combines information about representative sea ice conditions, ship-ship collisions and their associated oil outflow, the oil dispersion and spreading in the ice conditions, and the oil spill response and recovery of the fleet. Finally, the critical factors are identified by applying the proposed method to the developed oil spill response system model. The identified most critical system factors relates collision aspect: Forcing Representative Scenario, Representative Accident Location, Impact Speed, Impact Location, Impact Angle and response aspect: Response Vessel Operability.     

On the application of the window of opportunity and complex network to risk analysis of process plants operations during a pandemic

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.     

论特种设备事故(突发事件)的应急响应与科学处置

为满足基于大数据的特种设备事故推演和预防技术及平台的研发与应用,本文研究了特种设备事故(突发事件)应急处置现状和存在的问题,归纳总结出特种设备安全共性风险,设计和完善了事故响应、控制、预防等应急处置方案与应急救援体系,并进行了实例验证.同时,本文还对在应急处置平台系统上的部署和应用提出了期望.     

南京水体中抗抑郁类药物的赋存及风险评估

采集南京不同水体9个点位的水样,经固相萃取后采用LC-MS/MS方法测定样品中10种抗抑郁类药物,结合风险熵值法评估其对不同营养级水生生物的风险。结果表明,南京水体中存在不同程度的抗抑郁类药物污染,质量浓度范围为未检出～9.4 ng/L;舍曲林、氟伏沙明和西酞普兰的检出率为100%,氟西汀的检出率为44.4%。风险评估结果表明,南京水体中抗抑郁类药物对不同营养级水生生物表现出低—中等风险;舍曲林和氟西汀对总风险的贡献率最高,需要重点关注。