An approach for risk reduction (methodology) based on optimizing the facility layout and siting in toxic gas release scenarios

In this work, a new approach to optimize facility layout for toxic release is presented. By integrating a risk analysis in the optimization formulation, we propose a safer assignment for facility layout and siting. Accompanying with the economical concepts used in a plant layout, the new model considers the cost of willing to avoid a fatality, i.e. the potential injury cost due to accidents associated with toxic release near residential areas. The proposed formulation incorporates a real meteorological data to calculate the injury risk through the probit model and Monte Carlo simulation using dense gas dispersion modeling (DEGADIS). The overall problem was initially modeled as a disjunctive program where the coordinates of each facility and cost-related variables are the main unknowns. Then, the convex hull approach was used to reformulate the problem as a Mixed Integer Non-Linear Program (MINLP) that identifies potential layouts by minimizing overall costs. This approach gives the coordinates of each facility, and estimates for the total length of pipes, the land area, and the selection of safety devices. Finally, the 3D-computational fluid dynamics (CFD) was used to compare between the initial layout and the final layout. Moreover, analyses of separation distances fr2om hazard facilities and hindrance effects will be discussed based on the approach used in this work.     

Process design optimization and risk analysis

Hazard identification and risk assessment are key aspects in process plant design. They are often applied in the final stages of the process at whatever the cost, unless financial constraints are imposed. However, a much better solution would be to introduce risk analysis earlier by including it in earlier stages of the design process, such as when the cost of a plant and the cost of any accidents that may occur are estimated. In this paper, an optimization methodology is proposed, in which both cost and risk (with a deterministic approach) are taken into account, to improve on the current situation. If a decision variable is chosen, an objective function will be established that makes it possible to analyze variations in overall costs, including the cost of the investment and the cost of accidents. This leads to an optimum situation in which costs are kept to a minimum. Of course, this optimization is subject to constraints, the greatest of which is the fact that risk must not exceed tolerated threshold levels. The procedure is explained and two examples, one involving a toxic release and the other a BLEVE/fireball, are used to illustrate it.     

Safety investment optimization in process industry: A risk-based approach

Process plant safety is a critical indicator of organizational performance. Adequate investment into safety practices to avoid future accident cost is therefore a beneficial strategy. The current approach to such investments in the process industry is driven largely by simple risk-based heuristics, insurance market premiums, organizational culture and management judgment. There is, however, an absence of an overarching methodology to assist such an effort. Therefore, there is a need for developing a robust decision-making framework for enabling systematic and optimal allocation of financial resources across all significant risk elements within a process plant.The present work proposes a safety investment optimization (SIO) framework for a typical process plant. Such an optimization approach targets maximal reduction of risk values across all potential hazards within the constraint of a given safety investment budget at the incipient stage of establishing a plant such that it saves future cost to company by reducing the risk from accidents. At the same time the framework takes into account the need to comply with the regulatory requirements imposed by the government. Additionally, access to insurance market as a strategy to transfer risk is also integrated. Finally, the residual risks are managed through investments in selective safeguards while ensuring that the benefits over-weigh the cost of such an exercise. For illustrating the application of the framework, a representative process plant with a select number of risk scenarios is chosen and all steps suggested by the framework are demonstrated quantitatively. It is anticipated that the proposed SIO framework will help optimal resource allocation for managing the risks implicit in a typical process plant.     

Development of a risk-based maintenance (RBM) strategy for a power-generating plant

The unexpected failures, the down time associated with such failures, the loss of production and, the higher maintenance costs are major problems in any process plant. Risk-based maintenance (RBM) approach helps in designing an alternative strategy to minimize the risk resulting from breakdowns or failures. Adapting a risk-based maintenance strategy is essential in developing cost-effective maintenance policies.The RBM methodology is comprised of four modules: identification of the scope, risk assessment, risk evaluation, and maintenance planning. Using this methodology, one is able to estimate risk caused by the unexpected failure as a function of the probability and the consequence of failure. Critical equipment can be identified based on the level of risk and a pre-selected acceptable level of risk. Maintenance of equipment is prioritized based on the risk, which helps in reducing the overall risk of the plant.The case study of a power-generating unit in the Holyrood thermal power generation plant is used to illustrate the methodology. Results indicate that the methodology is successful in identifying the critical equipment and in reducing the risk of resulting from the failure of the equipment. Risk reduction is achieved through the adoption of a maintenance plan which not only increases the reliability of the equipment but also reduces the cost of maintenance including the cost of failure.     

基于风险指引的核电厂设计改进评价研究

核电厂概率安全评价(PSA)可以论证核电厂的风险满足安全目标,也是对运行核电厂进行风险管理的有效工具,例如核电厂的在役检查、安全分级、技术规格书优化等。核电厂的风险指引管理是在确定论的基础上,充分利用概率安全评价的结果进行风险影响评价,以此来论证决策的合理性。核电厂的重要设计改进通常基于传统的工程分析结果,没有分析其对核电厂整体风险的影响。重点探讨风险指引决策的基本原则以及方法,以核电厂设计改进实例探讨如何在分析时引入风险指引方法,并提出相关建议。     

Safety analysis and risk assessment of a Micro-GtL Plant

Laboratory hydrogen generators, medical oxygen, and micro-breweries are examples of modular and micro technologies that are commercial successes. Researchers, patients, and unskilled workers operate these facilities but more complex processes require highly qualified personnel to ensure they operate safely. Modular-micro processes in isolated locations meet economic objectives when operated remotely thereby minimizing labor costs. Mitigating the risk requires a comprehensive hazards analysis with advanced control systems particularly for explosive and toxic compounds. Here, we propose a method called Failure Mode Risk Decision (FMRD) to review the inherent hazards of a micro-refinery unit (MRU) that converts flared and wasted natural gas to long chain hydrocarbons. This approach combines the Process Flow Failure Mode (PFFM) methodology as a systematic and reliable technique with a novel numerical risk assessment to improve the analytical evaluation of hazardous conditions. The objective is to combine causes and consequences in a single metric, where scaled probability of evident causes and severity of consequences are used to derive a risk level measure. With the proposed metric, the magnitude of a potential hazard is directly correlated with the risk level. This mechanism identifies extra risk scenarios compared to the classical hazard analysis method and provides a straightforward comprehensive numerical assessment to represent the inherent and residual risks to facilitate justifying the hazardous scenarios. Accordingly, we design a safety loop and supply all the required facilities to remove the potential risks at the process plant. Not only the proposed methodology clarifies the risks of the MRU presented in this study, but can be extended to review the hazards of other chemical process plants.     

Cost-based fire risk assessment in natural gas industry by means of fuzzy FTA and ETA

Fire is the most prevalent accident in natural gas facilities. In order to assess the risk of fire in a gas processing plant, a fault tree analysis (FTA) and event tree analysis (ETA) has been developed in this paper. By utilizing FTA and ETA, the paths leading to an outcome event would be visually demonstrated. The framework was applied to a case study of processing plant in South Pars gas complex. All major underlying causes of fire accident in a gas processing facility determined through a process hazard analysis (PHA). Fuzzy logic has been employed to derive likelihood of basic events in FTA from uncertain opinion of experts. The outcome events in event tree has been simulated by computer model to evaluate their severity. In the proposed methodology the calculated risk has the unit of cost per year which allows the decision makers to discern the benefit of their investment in safety measures and risk mitigation.     

Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning

The overall objective of the maintenance process is to increase the profitability of the operation and optimize the total life cycle cost without compromising safety or environmental issues. Risk assessment integrates reliability with safety and environmental issues and therefore can be used as a decision tool for preventive maintenance planning. Maintenance planning based on risk analysis minimizes the probability of system failure and its consequences (related to safety, economic, and environment). It helps management in making correct decisions concerning investment in maintenance or related field. This will, in turn, result in better asset and capital utilization.

This paper presents a new methodology for risk-based maintenance. The proposed methodology is comprehensive and quantitative. It comprises three main modules: risk estimation module, risk evaluation module, and maintenance planning module. Details of the three modules are given. A case study, which exemplifies the use of methodology to a heating, ventilation and air-conditioning (HVAC) system, is also discussed.     

Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality.     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

Development of a risk-based maintenance strategy using FMEA for a continuous catalytic reforming plant

Petrochemical facilities and plants require essential ongoing maintenance to ensure high levels of reliability and safety. A risk-based maintenance (RBM) strategy is a useful tool to design a cost-effective maintenance schedule; its objective is to reduce overall risk in the operating facility. In risk assessment of a failure scenario, consequences often have three key features: personnel safety effect, environmental threat and economic loss. In this paper, to quantify the severity of personnel injury and environmental pollution, a failure modes and effects analysis (FMEA) method is developed using subjective information derived from domain experts. On the basis of failure probability and consequence analysis, the risk is calculated and compared against the known acceptable risk criteria. To facilitate the comparison, a risk index is introduced, and weight factors are determined by an analytic hierarchy process. Finally, the appropriate maintenance tasks are scheduled under the risk constraints. A case study of a continuous catalytic reforming plant is used to illustrate the proposed approach. The results indicate that FMEA is helpful to identify critical facilities; the RBM strategy can increase the reliability of high-risk facilities, and corrective maintenance is the preferred approach for low-risk facilities to reduce maintenance expenditure.     

不确定环境下危险废弃物物流网络优化研究

研究危险废弃物物流网络构建的各个不确定因素,构建基于成本和风险最优的危险废弃物物流网络多目标多周期优化模型,然后设计了相应的遗传算法,并通过算例验证了模型及算法的有效性。从不同置信水平导致的优化结果不同可知,由于不确定性因素的存在,导致所设计的物流网络留有一定的容错空间,对设施、设备和人力资源造成浪费。故进行多周期规划,降低网络中各个因素的不确定性,可有效降低成本,减少风险。     

大型公共场所多源疏散的控制流模型研究

以现有的单源最快流控制算法为基础,考虑人员在大型公共场所中的实际分布状况,遵循最大限度利用各出口的原则,提出针对多源疏散的全局最优化算法。根据此算法可以得出各疏散源点经过各出口的疏散人员数量以及人员的行走路径、从源点出发和完成疏散的时刻。将算法应用于某百货公司的案例研究表明,在多源疏散的情况下,各源点依次按照单源最快流控制算法进行独立疏散,能够保证每个源点的最优疏散,但整体的疏散效率并非最优;而应用全局最优化算法进行疏散,可以充分利用疏散过程中不同出口的疏散时间差,取得减少整体疏散时间、提高疏散效率的效果。     

Developing a chaotic pattern of dynamic risk definition for solving hazardous material routing-locating problem

In the case of determining routes and locations for constructing distribution centers on hazardous materials (Hazmat) transportation, risk and cost are considered as the main attributes for developing mathematical models. Since, Hazmat transport risk may be defined as a chaotic factor, using dynamic risk changes the selected routes and optimized locations for constructing distribution centers.In the present paper, an iterative procedure has been proposed to determine the best routes and optimized locations of distribution centers for transporting hazardous materials based on the concept of chaos theory in which hazmat transport risk is defined as a dynamic variable. A mathematical model has been developed for solving Hazmat routing and locating problems, simultaneously. Daily transport risk, defined as a chaotic variable, is iteratively updated using one-dimensional logistic map equation over the time period (year). An experimental road network, consists of eighty nine nodes and one hundred and three two-way edges, has been selected for analytical process and model validation. Results revealed that although different amounts of risk and cost priorities change optimized locations of distribution centers and their associated supplies, but the most frequent set of optimized centers remains independent. Therefore, the proposed procedure is capable to determine the best routes and optimized locations for distributing hazardous materials. While risk is iteratively updated over a specific time period, results show that the main property of chaos theory known as dependency upon initial condition would not be a serious concern for decision makers who are dealing with Hazmat management.     

城市重大危险源区域风险评价方法研究

针对城市应急管理的特点,提出了一种城市重大危险源区域风险评价方法。在ARAMIS方法的基础上,对严重度计算进行了修订,引入了急性暴露指南（AEGL）标准对事故后果进行分区;同时,增加了与应急救援效率相关的指标进行风险目标的脆弱性评估;利用GIS技术,对严重度和脆弱性进行叠加分析,绘制出城市重大危险源区域风险地图。以液氨储罐毒气泄漏事故为例,对文中提出的方法进行了验证。实例分析表明,此方法为快速获取城市重大危险源区域风险的空间分布格局提供了新思路,对于降低城市突发环境污染事故的影响和辅助决策者制定科学的城市公共安全管理决策具有一定的现实意义。     

危险废物管理系统的优化

危险废物管理系统的规划和设计包括处理和处置设施的选择、危险废物的分配、废物残渣从产生地运往处理处置点运输路线的选择等。在考虑了不同危险废物的不同特征、不同废物间的相容性、废物与处理技术的相容性、废物处置设施产生的废物残渣和选址问题等,基于多目标整数规划方法,提出了危险废物管理系统的优化模型。其中,经济目标包括运输费用、处理与处置成本、处理废物经济效益,环境目标包括处理设施总的环境影响、运输过程的环境影响,并从经济和环境影响方面,提出了一个综合考虑了费用和环境影响相关目标的复合效用函数。所提出的危险废物管理系统优化模型对我国区域性危险废物管理提供理论参考。     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

Fuzzy dynamic risk-based maintenance investment optimization for offshore process facilities

A methodology for maintenance planning is developed which helps in improving the reliability of the components and safety performance in process facilities. This methodology helps design an optimum safety maintenance investment plan by integrating the optimization techniques and a fuzzy dynamic risk-based method. Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) is applied to deal with uncertain data. The proposed approach employs multi-experts’ knowledge which helps to optimize the maintenance investments. A separator system in an offshore process facility platform is selected as a case study to demonstrate the application of the proposed methodology. A practical example in the separator system is surveyed and potential failures and Basic Events (BEs) are identified. Finally, a risk-based maintenance plan is provided for future safety investment analysis. The results indicate that the developed methodology estimates the risk more accurately, which enhances the reliability of future process operations.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.