Multilevel Flow Modeling Research Articles

Model-based and rule-based synthesis of operating procedures for planning severe accident management strategies

When severe accidents happen in nuclear power plants (NPPs), although there are candidate high-level actions (CHLAs) that can be selected from the severe accident management guidance (SAMG) to mitigate consequences, operators may still have cognitive challenges to identify available plant sources and then formulate executable action plans to implement the chosen actions. The difficulties faced by humans may be resolved by a decision support system taking advantages of computers’ knowledge representation and inferential capability.This article presents a systematic approach for synthesizing operating procedures that can achieve operational goals with currently available plant resources. Multilevel flow modeling (MFM), whose representation has been proved to be consistent with the general emergency procedure development, is used as knowledge basis for the action planning. A rule reasoning method is proposed to infer about actions from perspective of functions’ state transition. A model-based and rule-based operating procedure synthesis (OPS) system is developed with a rule engine. The OPS system is applied to elaborate existing high level strategies for dealing with station blackout (SBO) in a BWR plant as multiple executable action plans. The results shows some untypical ways of implementing available systems for achieving current safety goals, which may be considered in the further SAM. Moreover, what critical plant resources should be well prepared in advance is also indicated from the case study. Finally, limitation of MFM and the develop OPS system on planning SAM strategies are discussed.

System-Level Fault Prognosis for High-Speed Railway On-Board Systems

The on-board system is the core part of a train control system that ensures traffic safety and improves operational efficiency. At present, fault data processing methods for on-board systems do not show the degradation of components, subsystems, or the system over time, but only focus on identifying faults after a failure has occurred, so fault prognosis cannot be realized. This paper applies a multilevel flow model based on system-level failure mode and effect analysis. The model defines the primary and secondary alarms, divides the failure modes into different levels, and ranks the risk priority numbers at different levels. The Balise transmission system is then used as an example to demonstrate the system-level fault prognosis methodology, and the variables from different levels are combined through the component model, subsystem model, and system model. The results are verified according to the actual operation manual.

MODELING OF OPERATOR’S ACTIONS ON A NUCLEAR EMERGENCY CONDITION USING MULTILEVEL FLOW MODELING

In nulear emergency condition, after determining the initiating event and the type of the anomaly, operators should take counteractions to control the reactor to mitigate the accident and to bring back the plant to the safe condition. The actions should based on emergency operating procedures. In order to minimize the human error related to the actions, some necessary information is needed. Such kind of information is the consequence of the actions, which can be derived by modeling the counteractions. Multilevel flow modeling (MFM), a functional modeling, is chosen to model the counteraction with the consideration that it is based on cause-effect relations and consequence reasoning, it provides realization relationship which corresponds physical components with their functions, and it provides comprehensive diagnosis based on human perspective of the system objectives. The counteractions are represented by the control functions in the MFM. This paper discusses how to model the counteractions and the consequences of the actions to the system components, which are necessary to enhance situation awareness and to reduce human errors.Keywords: Operator actions, emergency operating procedures, multilevel flow modeling, control function, nuclear safety, human error

Generation of Signed Directed Graphs Using Functional Models

Abstract Intelligent fault diagnosis systems can be a major aid to human operators charged with the high-level control of industrial plants. Such systems aim for high diagnostic accuracy while retaining the ability to produce results that can be interpreted by human experts on site. Signed directed graphs have been shown to be a viable method for plant-wide diagnosis that can incorporate both quantitative information about the process condition as well as qualitative information about the system topology and the functions of its components. Their range of application in industrial settings has been limited due to difficulties regarding the interpretation of results and consistent graph generation. This contribution addresses these issues by proposing an automated generation of signed directed graphs of industrial processes in the chemical, petroleum and nuclear industries using Multilevel Flow Modeling; a functional modeling method designed for operator support. The approach is demonstrated through a case study conducted on the Tennessee Eastman Process, showing that Multilevel Flow Modeling can facilitate a consistent modeling process for signed directed graphs. Finally, the resulting benefits regarding qualitative reasoning for plant-wide diagnosis are discussed.

Toward Comprehensive Decision Support Using Multilevel Flow Modeling

Abstract The complexity of modern industrial plants poses significant challenges for the design of effective operator interfaces. Although established practices can significantly reduce the frequency of alarms, operators often cannot resolve the failure cascades commonly occurring during emergency situations. Automating control rooms by incorporating design and operation knowledge about the systems can significantly improve operator efficacy. Intelligent support systems should reduce the amount of information and provide more context to the operators. The operators focus should be shifted from information acquisition to taking informed decisions about mitigation steps. This contribution gives a brief review of the development of Multilevel Flow Modeling (MFM) and its application to provide operators with decision support and situation awareness, focusing on implementations directly utilising the knowledge represented in MFM. Finally, current efforts toward a comprehensive intelligent human machine interface for operators are outlined.

Synthesis of Valve and Pump Operations in Complex Plants by Using Functional Modeling

It is crucial to provide operators supports for operation planning in complex process plants. This paper presents an operation searching approach used for synthesis of valve and pump operations to establish specific pipe routes. Instead of modeling pipeline fragments, a functional modeling methodology called Multilevel Flow Modeling (MFM) is adopted to represent plant knowledge. Relying on causality feature of MFM, one can identify required state changes of specific functions in the model, and accordingly operations. Since relationships between functions, components, and operations are independent to the modeling object, these generic principles can be implemented into a rule-based reasoning system for inferring operational conditions of pipe routes, namely, operations. An example in the literature has been used to demonstrate the presented approach and application of the software.

Application of functional modelling for monitoring of WTG in a cyber‐physical environment

Decentralisation of generation and increasing utilisation of information communication systems bring challenges to present power system modelling approaches. This work applies functional modelling for monitoring and modelling of distributed energy resources, with wind turbine generator (WTG) application as a case study. First, the authors established a functional model of a generic WTG through the multilevel flow modelling approach. The model acts as basis of a state estimator (SE) for monitoring the WTG. Afterwards, the application of the SE is extended for wind power plant monitoring and control. The case study results show that the SE can efficiently limit the impact of information errors from different data integrity attacks during active power curtailment.

Functional Information of System Components Influenced by Counteractions on Computer-Based Procedure

In an emergency condition of nuclear power plant, operators have to mitigate the accident in order to remove the decay heat and to prevent the release of radioactive material to the environment following the emergency operating procedures (EOPs). The action of operators on a component, for example, changing the parameter level of a component, which is described in a procedure step, will impact other components of the plant and the plant behavior. Nowadays, the advanced main control rooms have been equipped with the computer-based procedures (CBPs) which provide some features and benefits which are not available in paper-based procedures. However, most of CBPs do not provide information of the impact of the counteractions on each procedure step (components influenced and future plant behavior) although it is useful for operators to understand the purpose of the procedure steps before making decisions and taking the actions. This paper discusses the functional information and the method to generate the information using multilevel flow modeling (MFM) model of operator actions on some procedure steps of a simplified EOP of pressurized water reactor (PWR) plant, as an example.

Extended multilevel flow model-based dynamic risk assessment for cybersecurity protection in industrial production systems

Cybersecurity protection becomes an essential requirement for industrial production systems, while industrial production systems are moving from isolation to interconnection with the development of information and communication technology. Dynamic risk assessment plays an important role in cybersecurity protection, providing the real-time security situation to the industrial production systems managers. Currently, few researches in this domain focus on the physical process of industrial production systems, let alone considering the combination of attack propagation in cyber space and the abnormal events happening in physical space for risk assessment. In this article, an extended multilevel flow model-based dynamic risk assessment approach for industrial production systems is proposed, where the extended multilevel flow model models the production process graphically and describes the relationships among devices, functions, and flows quantitatively. Based on the extended multilevel flow model of industrial production systems, a Bayesian network is built to analyze the attack propagation over time, and the consequences of cyber attack in production process are assessed quantitatively. Some simulations on a chemical process system are carried out to verify the effectiveness of the proposed approach. The results demonstrate that this approach can assess the dynamic cybersecurity risk of industrial production systems in a quantitative way.

Enhanced reasoning with multilevel flow modeling based on time-to-detect and time-to-effect concepts

Abstract To easily understand and systematically express the behaviors of the industrial systems, various system modeling techniques have been developed. Particularly, the importance of system modeling has been greatly emphasized in recent years since modern industrial systems have become larger and more complex. Multilevel flow modeling (MFM) is one of the qualitative modeling techniques, applied for the representation and reasoning of target system characteristics and phenomena. MFM can be applied to industrial systems without additional domain-specific assumptions or detailed knowledge, and qualitative reasoning regarding event causes and consequences can be conducted with high speed and fidelity. However, current MFM techniques have a limitation, i.e., the dynamic features of a target system are not considered because time-related concepts are not involved. The applicability of MFM has been restricted since time-related information is essential for the modeling of dynamic systems. Specifically, the results from the reasoning processes include relatively less information because they did not utilize time-related data. In this article, the concepts of time-to-detect and time-to-effect were adopted from the system failure model to incorporate time-related issues into MFM, and a methodology for enhancing MFM-based reasoning with time-series data was suggested.

Planning of alternative countermeasures for a station blackout at a boiling water reactor using multilevel flow modeling

Abstract Operators face challenges to plan alternative countermeasures when no procedure exists to address the current plant state. A model-based approach is desired to aid operators in acquiring plant resources and deriving response plans. Multilevel flow modeling (MFM) is a functional modeling methodology that can represent intentional knowledge about systems, which is essential in response planning. This article investigates the capabilities of MFM to plan alternatives. It is concluded that MFM has a knowledge capability to represent alternative means that are designed for given ends and a reasoning capability to identify alternative functions that can causally influence the goal achievement. The second capability can be applied to find originally unassociated means to achieve a goal. This is vital in a situation where all designed means have failed. A technique of procedure synthesis can be used to express identified alternatives as a series of operations. A case of station blackout occurring at the boiling water reactor is described. An MFM model of a boiling water reactor is built according to the analysis of goals and functions. The accident situations are defined by the model, and several alternative countermeasures in terms of operating procedures are generated to achieve the goal of core cooling.

A water treatment case study for quantifying model performance with multilevel flow modeling

Abstract Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water.

Preliminary Investigation of Time Remaining Display on the Computer-based Emergency Operating Procedure

One of the important thing in the mitigation of accidents in nuclear power plant accidents is time management. The accidents should be resolved as soon as possible in order to prevent the core melting and the release of radioactive material to the environment. In this case, operators should follow the emergency operating procedure related with the accident, in step by step order and in allowable time. Nowadays, the advanced main control rooms are equipped with computer-based procedures (CBPs) which is make it easier for operators to do their tasks of monitoring and controlling the reactor. However, most of the CBPs do not include the time remaining display feature which informs operators of time available for them to execute procedure steps and warns them if the they reach the time limit. Furthermore, the feature will increase the awareness of operators about their current situation in the procedure. This paper investigates this issue. The simplified of emergency operating procedure (EOP) of steam generator tube rupture (SGTR) accident of PWR plant is applied. In addition, the sequence of actions on each step of the procedure is modelled using multilevel flow modelling (MFM) and influenced propagation rule. The prediction of action time on each step is acquired based on similar case accidents and the Support Vector Regression. The derived time will be processed and then displayed on a CBP user interface.

Techniques to derive additional information of operation actions for computer-based operating procedure

ABSTRACT Most of the modern main control rooms of nuclear power plants are equipped with computer-based operating procedures (CBPs), which make it easier for operators to operate and control the reactor compared with paper-based operating procedure (PBP). However, most of the CBPs do not provide necessary information which is useful for operators, especially in an emergency situation. In addition, proper decisions and actions of the procedure steps are needed to prevent human errors in mitigating the accident. The additional information, which is the information of the impact of a counter action (by automatic system and human actions) such as the components influenced and future plant behavior will be very helpful for operators to understand the effects of the counter action. The aim of the study is to develop techniques to generate the additional information. Multilevel flow modeling (MFM) is applied to model a nuclear power plant and the counter actions described in CBPs. A simple emergency operating procedure of steam generator tube rupture accident of a pressurized water reactor (PWR) plant is used as a case study. The algorithm to generate the additional information is based on the influence propagation rules and cause–effect relations expressed in the MFM model of the PWR plant.

On-line Fault Diagnosis of Produced Water Treatment with Multilevel Flow Modeling

Making sense of alarms can be difficult on oil and gas platforms. Multilevel Flow Modeling provides a structure for modelling plant functionality and inferring causes for alarms and predicting consequences. Currently, Multilevel Flow Modeling has limited application for on-line fault diagnosis. Based on a fault emulated on a pilot plant for offshore produced water treatment, Multilevel Flow Modeling is used for reasoning about causes for triggered alarms. The inferred causes are analysed to investigate the current maturity of Multilevel Flow Modeling for on-line diagnosis.

A technique to generate plausible counter-operation procedures for an emergency situation based on a model expressing functions of components

ABSTRACTIn large-scale engineering plants like nuclear power plants, various anomalies and accidents are usually predicted and efficient measures to minimize the damage from them have been implemented. However, when an unexpected anomaly or accident occurs, operators are asked to take flexible responsive actions to avoid its development to a catastrophic situation and to minimize the influence and damage by the accident. Considering the lessons learned from Fukushima Daiichi accidents, it is important to develop a system to support counter activities by operators and plant staff in an accidental plant condition that is not covered by emergency operating procedures prepared. The purpose of this study is to develop a technique for generating plausible operation procedures under such unexpected emergency situations. The multilevel flow modeling (MFM), a method to represent the functions of components and their relations, is applied as a framework to model functions of components. The technique uses some additional data and knowledge for deriving operation procedures. The applicability of the technique is confirmed by trial operation procedure generations for pressurized water reactor (PWR) loss-of-coolant accident cases with the conditions of some failures of safety systems.

MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method.

Failure Analysis Using Functional Model and Bayesian Network

Abstract A class of functional model known as multilevel flow model (MFM) is used to represent a pilot scale heat exchanger system. MFM is effective in representing chemical process qualitatively through graphical representation, but lacks the ability to quantify the impact of successes or failures of process events, and is not able to quantitatively distinguish between steps in a goal and their contributions towards achieving the main goal. To address this issue, the MFM is converted into its equivalent fault tree (FT) model to accommodate logical sequence of events along with the needed quantifications. The FT model is then converted into Bayesian network (BN) model to facilitate updates of probabilities. Using Hugin 8.1 software, the BN model is simulated to investigate the response of the process when subjected to various faults. The results highlight the capability of the model in detecting process faults and in identifying the associated root causes, thus pointing to the potentials of the proposed strategy in modeling complex chemical processes for higher level functions in plant operations such as facilitating alarm system and fault diagnosis.

The Desirable Features of Computer Based Emergency Operating Procedure for Nuclear Power Operation

Abstract: Operating procedures are guidance for operators to monitor, make decision and take related actions in normal, abnormal and emergency condition of nuclear power plants. The initial form of operating procedures, paper based procedures (PBP), have some drawbacks such as high operators’ workload and necessity of much time to find and execute procedures related to the events. Therefore, computer based procedures (CBP) were developed to overcome the problems. However, some operators are not familiar with the CBP and may refuse to use the CBP because they used to use the PBP without any problems. This research investigates some features to the CBP to make it easier for operator to use and execute procedures and reducing human error. The proposed features are providing functional information of the procedures steps and related components using multilevel flow modeling (MFM), displaying remaining time for operators to complete each step or group of steps and implementing dynamic operation permission to reduce commission error by operators. The features, integrated with operator support systems, will make the CBP more intelligent and easy to use, especially in emergency condition of nuclear power plants

Development of a rule-based diagnostic platform on an object-oriented expert system shell

This paper presents the development and implementation of a real-time rule-based diagnostic platform. The knowledge is acquired from domain experts and textbooks and the design of the fault diagnosis expert system was performed in the following ways: (i) establishing of corresponding classes and instances to build the domain map, (ii) creating of generic fault models based on events, and (iii) building of diagnostic reasoning based on rules.Knowledge representation is a complicated issue of expert systems. One highlight of this paper is that the Multilevel Flow Model has been used to represent the knowledge, which composes the domain map within the expert system as well as providing a concise description of the system.The developed platform is illustrated using the pressure safety system of a pressurized water reactor as an example of the simulation test bed; the platform is developed using the commercial and industrially validated software G2. The emulation test was conducted and it has been proven that the fault diagnosis expert system can identify the faults correctly and in a timely way; this system can be used as a simulation-based training tool to assist operators to make better decisions.