Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

The alarm system given in industrial plants are massive and complex. Under such condition, critical alarms are overwhelmed by false and unnecessary alarms and thus result in severe safety issues. To address the problem, this paper proposes a probabilistic signed digraph (PSDG) based alarm signal selection method that requires achieving maximal system reliability. In this method, a PSDG model is firstly constructed to visualize the causal relations between process variables. Then the criteria of observability and identifiability are imposed to determine the candidate alarm variables that can qualitatively distinguish all assumed faults. Instead of selecting the minimum number of combinations of candidate variables, the alarm variables are optimized by a reliability formulation that takes into account the missed alarm and false alarm probabilities of the system; this formulation is solved by the receiver operating characteristic (ROC) graph. Finally, the developed methodology is illustrated using a Tennessee Eastman process.     

A data similarity based analysis to consequential alarms of industrial processes

The analysis of consequential alarms is beneficial to avoiding alarm flooding and finding out root alarms in an industrial process. In this context, a novel similarity computation method taking into account of correlation delays between process alarms is introduced firstly. Subsequently, the Granger causality method is suggested to further clarify mutual impacts of similar alarm variables based on process data. Through the combination of alarm data similarity analysis and process data causality analysis, the consequential alarms can be effectively identified along with their evolution paths. An industrial case is employed to illustrate the benefits of the contribution.     

Alarm management optimization in chemical installations based on adapted HAZOP reports

Most current alarm systems used in chemical installations show poor performance due to alarm flooding. This study focuses on alarm management systems optimization using the deviation propagation relationship hidden in the hazard and operability study (HAZOP) report, which can be transformed into a critical information source for alarm optimization management. More concretely, this means matching the alarm tag number with the process deviations in the deviation column, possible cause column, and consequence column. Furthermore, a backtracking method and a reasoning method were established to identify the initial alarm and associated alarms. Besides, a root fault diagnosis was carried out. A method of detecting hardware faults and unreasonable alarm thresholds is established using alarm causality corresponding to the deviation causality and associated alarm generation-skipping tracing method. According to the severity of the consequence corresponding to the deviation, a determined alarm priority method is constructed. The results show that the deviation propagation relationship in the HAZOP report is clear, and the topological relationship is easy to build based on the deviation propagation relationship. With comprehensive and in-depth HAZOP analysis reports in China, the alarm management optimization technology based on adapted HAZOP reports shows good prospects for application and promotion.     

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.     

Comprehensive alarm information processing technology with application in petrochemical plant

During the abnormal plant conditions, too much information is produced due to momentary plant excursions above alarm limits. This flood of information impedes correct interpretation and correction of plant conditions by the operator. Existing techniques for the design of alarm systems mostly have weak ability to handle complex hazard scenarios and increase the probability of larger safety issues. In this paper, a comprehensive alarm information processing (AIP) technology is introduced, called multi-round alarm management system (MRAMS), including several processing strategies: AIP based on single sensor, AIP based on sensor group, root cause diagnosis based on Bayesian network, sensor fault judgment method and false alarm inhibition method. In case studies, both simulation experiment and pilot application on a real petrochemical plant are presented. Results indicate the MRAMS is helpful in improving the accuracy of correctly diagnosing the root causes and hence avoiding false and redundant alarms. By adopting this new technology, the safe and reliable operation of the plant can be achieved, and the economic loss brought by improper alarms can be reduced.     

基于数据驱动的石化装置静态报警抑制策略研究

由生产状态变化引起的误报警频发,为解决现有针对基于状态报警的抑制方法缺乏完整性与定量分析的问题,提出了新的报警抑制策略。通过分析报警记录等数据库,对引起报警的生产条件进行结构化整定并量化,利用分类筛选与数据过滤找出基于状态的报警,再结合关联性分析和概率判断,建立了基于数据驱动的静态报警抑制策略。使用现场数据的试验证明了该方法的有效性。     

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.     

液碳生产企业安全监控与自控系统开发

液态二氧化碳生产企业快速发展,规模逐年增大,安全监控与自动化水平不高导致事故风险日益升高。通过对液碳企业危险和风险分析,利用数据采集、实时数据库、参数监控、视频监控、预警报警、通讯和网络等技术,构建安全监控与自控系统,完成对液碳生产流程、有毒、有害、易燃易爆物品以及关键设备设施的动态监测、安全管理、智能分析、联锁控制、报警、应急辅助决策和统计查询等功能。安全监控与自控系统建设和实施可实现液碳生产企业安全、稳定、长周期、经济运行。     

Combining FAP,MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process

In the real industrial process, alarm threshold optimization is an important part of alarm system rationalization. If the design of alarm threshold is unreasonable, it would result in nuisance alarms, among which the critical alarms are overwhelmed. In order to alleviate this phenomenon, we propose a method of multivariate alarm thresholds optimization to reduce the nuisance alarms. Firstly, causal relationship between process variables is constructed based on the time delay estimation method, thus we can determine the alarms propagation path and then select the optimized variables. Secondly, in order to guarantee both the process safety and correlation consistency, three factors - false alarm probability (FAP), missed alarm probability (MAP), and the correlation between the alarm information and process information – are combined to establish the objective function of the optimization process for the first time. Then, the optimal thresholds are obtained by the genetic algorithm. Finally, the validity and effectiveness of the developed method are illustrated by the Tennessee Eastman process.     

A design method of a plant alarm system for first alarm alternative signals using a modularized CE model

Management of a plant alarm system has been identified as one of the key safety issues because of disasters caused by alarm floods. When a chemical plant is at abnormal state, an alarm system must provide useful information to operators as the third layer of an independent protection layer (IPL). Therefore, a method of designing a plant alarm system is important for plant safety. Because the plant is maintained in the plant lifecycle, the alarm system for the plant should be properly managed through the plant lifecycle. To manage changes, the design rationales of the alarm system should be explained explicitly. This paper investigates a logical and systematic alarm system design method that explicitly explains the design rationales from know-why information for proper management of changes through the plant lifecycle. In the method, the module structure proposed by Hamaguchi et al. (2011) to assign a fault origin to be distinguished is extended. Using modules to investigate the sets of alarm sensors and the alarm limits setting for first alarm alternative signals to distinguish the fault origin, an alarm system design method is proposed. Also, the completeness of fault propagation for a branch of the cause–effect model as the plant model is explained. Using the modules and the set of fault origins to be distinguished by the alarm system, we try to explicitly explain the design rationales of the alarm system.     

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.     

Evaluation and comparison of alarm reset modes in advanced control room of nuclear power plants

An automation function has been widely applied in main control room of nuclear power plants (NPPs). The alarm system of fourth nuclear power plant (FNPP) in Taiwan is also going to be developed with automatic technology that is expected to support the operators’ performance and reduce the number of alarms. In this study, an experiment with a training simulator as an advanced alarm system was conducted to compare the effects of different alarm reset modes on performance and subjective ratings. The objective was to evaluate the practicability of alarm system with only auto-reset function in FNPP. Results revealed that, using the auto-reset mode, participants had lower task load index (TLX) on effort in the first test trial and was more satisfied under multi-task condition. In contrast, using manual reset mode, participants were more satisfied on alarm handling, monitoring, and decision making. In other words, both reset modes are necessary to assist the operator in different aspects, but with only single reset mode is insufficient. The reset function in advanced alarm system therefore should be very flexible.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

一种基于层次分析法的危险化学品源安全评价综合模型

危险化学品源的安全评价是安全生产管理中的一项重要内容.目前用于危险化学品源安全评价与分级的常用方法有后果分析法、道指数法、蒙德指数法以及使用临界系数判别重大危险源的方法等.在实际应用中,单独使用某一方法时,由于存在各种片面性问题而得不到满意的评价结果; 几种方法同时使用时,其评价指标、评价结果以及结果的形式又会互相冲突.为解决上述问题,建立了基于层次分析法的综合评价模型.首先,根据安全评价要求构建3层次的评价体系,在各层次中构造判断矩阵,并计算4种常用方法相对综合评价模型的置信度; 其次,对各方法统一危险分级标准,均采用危险分数划定危险级别,并取各危险分数的加权平均值--综合危险分数作为综合评价模型下的危险源分级标准.采用综合评价模型可消除单一方法进行评价时的片面性和偏差,同时,评价结果的一致化使得判断危险化学品源的危险级别以及由此采取相应级别的管理措施成为可能,将更有利于实际安全生产管理指导.     

Near-Miss定义及管理流程分析

作为事故金字塔的基座,Near-Miss管理在企业HSE管理乃至整个企业管理中起着非常重要的作用。通过消除底部的Near-Miss,可以最大限度地消除隐患,避免顶端意外事件的发生,提升企业HSE业绩,提高企业的运行效率。通过比较分析,可将Near-Miss翻译为"虚惊事件"。一个完整的Near-Miss管理可分解为八个流程,每个流程缺一不可,且前面的流程决定了后面流程的完成情况。通过八个流程的充分运行,可以获得Near-Miss管理效益的最大化,最终提升企业的HSE管理水平。     

基于贝叶斯估计的多探测器火警判定方法研究*

为应对感烟探测器的大量误报对消防应急响应带来的挑战,考虑目前以感烟探测器为主的火警设施误报率高且短期内难以全部更换的特点,提出基于贝叶斯估计的多探测器火警判定方法,通过多个探测器的报警时间间隔计算火源位置的后验概率分布,并提出火警真实度概念,为火警判定提供依据。结果表明:使用多探测器耦合模型时每增加1个探测器可将误报率降低约4个数量级,该方法在探测器正常、部分失效、误报的情景下均能有效判别火警。     

Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology

A safety analysis was performed to determine possible accidental events in the storage system used in the liquefied natural gas regasification plant using the integrated application of failure modes, effects and criticality analysis (FMECA) and hazard and operability analysis (HAZOP) methodologies. The goal of the FMECA technique is the estimation of component failure modes and their major effects, whereas HAZOP is a structured and systematic technique that provides an identification of the hazards and the operability problems using logical sequences of cause-deviation-consequence of process parameters. The proposed FMECA and HAZOP integrated analysis (FHIA) has been designed as a tool for the development of specific criteria for reliability and risk data organisation and to gain more recommendations than those typically provided by the application of a single methodology. This approach has been applied to the risk analysis of the LNG storage systems under construction in Porto Empedocle, Italy. The results showed that FHIA is a useful technique to better and more consistently identify the potential sources of human errors, causal factors in faults, multiple or common cause failures and correlation of cause-consequence of hazards during the various steps of the process.     

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.     

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.