Multilevel Flow Modeling Research Articles

Fault Diagnosis of HCCB TES for ITER Based on Functional Modeling

China has designed a test blanket system (TBS) based on helium cooled ceramic breeder (HCCB) test blanket module (TBM) for international thermonuclear experimental reactor (ITER). Since the system is complicated, it is so difficult to achieve the safety and stability requirement of TBS under operating condition that a real time fault diagnosis system need to be developed. This survey proposes a novel fault diagnosis method based on functional modeling, which can satisfy the safety and stability requirement of TBS. After giving a brief description of HCCB tritium extraction system (TES) that is one of the TBM’s ancillary systems, the paper presents the fault diagnosis method based on functional modeling by using multilevel flow modeling in detail. Then the TES is modeled and analyzed using this novel method. At last, a conclusion is drawn illustrating that this novel method is suitable for HCCB TBS and ITER.

THE ROLE OF SAVING IN THE U.S. TWIN DEFICITS

Multilevel flow modeling (MFM) is a modeling method for complex technical systems in which the goals and functions of the system are explicitly described. MFM can be used as a basis for root cause analysis, where primary root causes are separated from consequential faults, in complex fault situations. Model representations for use in diagnostic reasoning usually describe causality, between parameters, faults, or process states. However, the causality of a system may vary depending on details in the construction, as well as over time with the process state. One contribution of this paper is a general method of describing varying causality in a simple and efficient way. The method has been tested using multilevel flow models. Causality is visible in measurements and can be used to increase process understanding. The standard cross-correlation technique is insufficient for causality detection in industrial processes. Another contribution of this paper is a new method that can detect causality in industrial signals, and thus be used to validate the design of multilevel flow models.

A Systematic Modeling of Fault Interdependencies in Petroleum Process System for Early Warning

Process systems in petroleum industries such as oil refining systems have been becoming increasingly large and automatic. There can be strong interdependencies between various facilities and components. To reduce the probability and mitigate consequences of equipment failures, these interdependencies have to be assessed. The objective of this paper is to present a systematic modeling method for representing the interdependencies of process infrastructures in petroleum industries, as part of the risk early warning system. The proposed systematic modeling method involves several modeling steps. Firstly, Multilevel Flow Modeling (MFM) is used to represent a system in terms of goals, objectives, functions and components, each of which can be described at different levels of part-whole decomposition. Secondly, HAZOP study is carried out based on the MFM, by which all of the possible deviations and their corresponding potential fault causes and consequences are analyzed carefully. Thirdly, dynamic Bayesian Network (DBN) is used to build the fault causal relationships that represent the fault interdependencies in the complex system. Finally, by the inference mechanism of DBN, the most possible initial reason(s) happened in the fault interdependency network with multi-reasons and consequences can be found accurately for risk early warning. Examples from a case study are included to illustrate the effectiveness and accuracy of the approach.

Research of a New Reliability Analysis Method Based on Multilevel Flow Model and Its Application on the Gas Turbine Compressor

Research of a New Reliability Analysis Method Based on Multilevel Flow Model and Its Application on the Gas Turbine Compressor

FUNCTIONAL MODELLING FOR FAULT DIAGNOSIS AND ITS APPLICATION FOR NPP

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse.

An integrated qualitative and quantitative modeling framework for computer‐assisted HAZOP studies

The article proposes a novel practical framework for computer‐assisted hazard and operability (HAZOP) that integrates qualitative reasoning about system function with quantitative dynamic simulation in order to facilitate detailed specific HAZOP analysis. The practical framework is demonstrated and validated on a case study concerning a three‐phase separation process. The multilevel flow modeling (MFM) methodology is used to represent the plant goals and functions. First, means‐end analysis is used to identify and formulate the intention of the process design in terms of components, functions, objectives, and goals on different abstraction levels. Based on this abstraction, qualitative functional models are constructed for the process. Next MFM‐specified causal rules are extended with systems specific features to enable proper reasoning. Finally, systematic HAZOP analysis is performed to identify safety critical operations, its causes and consequences. The outcome is a qualitative hazard analysis of selected process deviations from normal operations and their consequences as input to a traditional HAZOP table. The list of unacceptable high risk deviations identified by the qualitative HAZOP analysis is used as input for rigorous analysis and evaluation by the quantitative analysis part of the framework. To this end, dynamic first‐principles modeling is used to simulate the system behavior and thereby complement the results of the qualitative analysis part. The practical framework for computer‐assisted HAZOP studies introduced in this article allows the HAZOP team to devote more attention to high consequence hazards. © 2014 American Institute of Chemical Engineers AIChE J 60: 4150–4173, 2014

Design of multilevel flow modelling-based decision support system by using multiagent platform

For complex engineering systems, there is an increasing demand for safety and reliability. Decision support system (DSS) is designed to offer supervision and analysis about operational situations. A proper model representation is required for DSS to understand the process knowledge. Multilevel flow modelling (MFM) represents complex system in multiple levels of means-end and part-whole decompositions, which is proposed in this paper as basis for developing decision support systems. The aim of this paper is to explore the different possible functionalities by applying MFM to DSS, where both currently available techniques of MFM reasoning and less mature yet relevant MFM concepts are considered. It also offers an architecture design of task organisation for MFM software tools by using the concept of agent and technology of multiagent software system.

Hazard identification by extended multilevel flow modelling with function roles

HAZOP studies are widely accepted in chemical and petroleum industries as the method for conducting process hazard analysis related to design, maintenance and operation of the systems. In this paper, a HAZOP reasoning method based on function-oriented modelling, multilevel flow modelling (MFM) is extended with function roles to complete HAZOP studies in principle. A graphical MFM editor, which is combined with the reasoning engine (MFM Workbench) developed by DTU is applied to automate HAZOP studies. The method is proposed to support the ‘brain-storming’ sessions in traditional HAZOP analysis. As a case study, the extended MFM-based HAZOP methodology is applied to an offshore three-phase separation process. The results show that the cause-consequence analysis in MFM can infer the cause and effect of a deviation used in HAZOP and used to fill HAZOP worksheet. This paper is the first paper discussing and demonstrating the potential of the roles concept in MFM to supplement the completeness of HAZOP analysis in theory.

Consequence Reasoning in Multilevel Flow Modelling

Consequence reasoning is a major element for operation support system to assess the plant situations. The purpose of this paper is to elaborate how Multilevel Flow Models can be used to reason about consequences of disturbances in complex engineering systems. MFM is a modelling methodology for representing process knowledge for complex systems. It represents the system by using means-end and part-whole decompositions, and describes not only the purposes and functions of the system but also the causal relations between them. Thus MFM is a tool for causal reasoning. The paper introduces MFM modelling syntax and gives detailed reasoning formulas for consequence reasoning. The reasoning formulas offers basis for developing rule-based system to perform consequence reasoning based on MFM, which can be used for alarm design, risk monitoring, and supervision and operation support system design.

A novel failure mode analysis model for gathering system based on Multilevel Flow Modeling and HAZOP

In complex industrial system, such as gathering system, the high complex failure coupling relation among separate production process sections, personnel operation and equipment leads to a high complex potential hazard, which induces huge economic losses, environmental contamination, or human injuries. In order to insure system intrinsic safety and simplify failure mode analysis, this study proposes a novel failure mode analysis model (NFMA).NFMA is developed based on Multilevel Flow Modeling (MFM) and Hazard Operability Study (HAZOP). A graphical MFM model is introduced in NFMA by decomposing goals, functions and components, to descript flows of mass and energy of process system as basis of this model. According to the MFM reasoning rules, HAZOP investigates function nodes and deviations to identify the failure modes. Finally, the benefits and feasibility of NFMA are investigated with a case study of gathering system.

Safety Analysis Approach of MFM-HAZOP and Its Application in the Dehydration System of Oilfield United Station

Abstract United station is the core of the oil and gas field surface engineering, which plays an important role in the oil and gas production. Although the work of safety analysis on the united station equipment has been developed for a long time, the safety analysis on the production process is less and the systematic safety analysis model has never been formed. In this paper, a new safety analysis approach of MFM-HAZOP is proposed and applied to the oilfield united station. The united station is separated to many subsystems according to the function. Then the subsystem of oil dehydration is analyzed by the approach of MFM-HAZOP, which includes the steps of multilevel flow model construction, causal tree establishment and the variance analysis of the individual nodes.

Accident prevention by control system reconfiguration

The control system of a process plant, as other physical systems, can break up or fail to function satisfactorily, which means that control objectives are not achieved. In order to avoid the shut downs or accidents related to the control objectives unachieved, an analysis of both the process and control system must be performed. This paper introduces the use of functional modeling for control system reconfiguration. An extension of the Multilevel Flow Modeling (MFM) methodology is presented; this extension provides the functionality that analyzes the control system status as well as possible reconfigurations in a semiautomatic and consistent way, which means to enhance systems’ autonomy and robustness. In order to be able to have an integrated model an open, neutral, domain and platform independent language is needed. The Systems Modeling Language (SysML) complies with these requirements and can be applied to develop MFM models.

A Goal-Function Approach to Analysis of Control Situations

The concept of situations plays a central role in all theories of meaning and context. and serve to frame or group events and other occurrences into coherent meaningful wholes. Situations are typed, may be interconnected and organized into higher level structures. In operation of industrial processes situations should identify operational aspects relevant for control agent's decision making in plant supervision and control. Control situations can be understood as recurrent and interconnected patterns of control with important implications for control and HMI design. Goal-Function approaches to systems modeling like Multilevel Flow Modeling can be used to represent control situations. The paper will describe an action theoretical foundation for MFM and its use for the development of a theory of control situations.

Integrated Analysis and Evaluation Support System on Global Energy Systems for Sustainable Development-Its System Development with a Case Study for Chinese Energy Policy-

An Integrated Analysis and Evaluation Support System for Sustainable Development (IAESSD) has been developed for analyzing and assessing energy systems from the aspect of sustainable development of regional, national even the global levels. The developed IAESSD consists of three major components: Multilevel Flow Model (MFM), Geographic Information System (GIS) and Driver Force, State and Response (DSR) model. The three components are linked together through the Energy Indicators for Sustainable Development (EISD). By using the IAESSD, appropriate options of energy policies can be evaluated by the three steps; (i) calculation of EISD parameters based on the MFM and GIS, (ii) causal analysis for the time changes of the EISD parameters based on the DSR model, and (iii) recommendation and investigation new policy options in qualitative analysis way based on the causal analysis results. The tools and technologies of computer science and software engineering were applied to materialize the three modeling components to develop a workable software platform. As a case study, the whole energy system of China was simulated and evaluated by the developed IAESSD to reduce viable policy in her future from the aspect of sustainable development.

A hierarchical approach for the redesign of chemical processes

An approach to improve the management of complexity during the redesign of technical processes is proposed. The approach consists of two abstract steps. In the first step, model-based reasoning is used to generate automatically alternative representations of an existing process at several levels of abstraction. In the second step, process alternatives are generated through the application of case-based reasoning. The key point of our framework is the modeling approach, which is an extension of the Multimodeling and Multilevel Flow Modeling methodologies. These, together with a systematic design methodology, are used to represent a process hierarchically, thus improving the identification of analogous equipment/sections from different processes. The hierarchical representation results in sets of equipment/sections organized according to their functions and intentions. A case-based reasoning system then retrieves from a library of cases similar equipment/sections to the one selected by the user. The final output is a set of equipment/sections ordered according to their similarity. Human intervention is necessary to adapt the most promising case within the original process.

Generation of process alternatives using abstract models and case-based reasoning

An approach to manage the complexity involved in the redesign/retrofit of chemical processes is proposed in this paper. This approach uses model-based reasoning (MBR) to generate automatically alternative representations of an existing chemical process at several levels of abstraction. The hierarchical representation results in sets of equipments and sections organised according to their functions and purposes inside the overall process. Our approach is an extension of the multimodelling and the multilevel flow modelling methodologies. These, together with the methodology of Douglas and Turton, are used to represent a process hierarchically, thus improving the identification of analogous units (equipment) or meta-units (process sections) from different chemical processes. The hierarchical representation results in sets of units and meta-units organised according to their functional and teleological model. The implemented tool includes a CBR system to retrieve from a library of cases similar units and/or meta-units to the one selected by the user (similarity is measured according to the function and teleology of a unit and its neighbours). As an output of the CBR system we obtain a set of cases (units and meta-units) ranked according to their similarity. In the case of meta-units, the replacement of process sections may result in changes to the process topology while keeping the same functionality (e.g. a reactive-distillation unit replacing a reactor and separation system). The CBR operates at every level of the abstraction hierarchy both for the problem case and the library cases, opening for the first time the possibility to identify similar solutions at different levels of abstraction and thus widening the scope and the quality of search for similar solutions in the library of cases.

Modeling Goals and Functions of Micro Gas Turbine System by Multilevel Flow Models

Semiotic analysis is often used for describing the inter-relationship of structure, function and behavior of any artifacts as the means for designing various computerized tools for machine diagnosis and operation procedure. In this study, a graphical method called Multilevel Flow Models (MFM) is applied for supporting machine maintenance work of commercially available Micro Gas Turbine System (MGTS), to describe and handle the relationships between goals and functions that exist in various parameters of MGTS including signal, alarm and fault. A new three-step method including alarm validation, fault condition checkup and fault identification is proposed for fault diagnosis based on MFM. A trial software has been developed by using Visual C++ and Excel for monitoring and diagnosing the MGTS based on the proposed fault diagnosis method. And it was tested by several typical actual fault cases, to show that the proposed method is efficient to monitor the running state of MGTS and to diagnose the real reason of fault message from the operation software provided by the vendor of Micro Gas Turbine.

Modeling of PWR Plant by Multilevel Flow Model and its Application in Fault Diagnosis

The paper describes the application of Multilevel Flow Modeling (MFM)—a modeling method in means-end and part-whole way, in interface design of supervisory control of Pressurized Water Reactor (PWR) plant, and automatic real-time fault diagnosis of PWR accident. The MFM decomposes the complex plant process from the main goal to each component at multiple levels to represent the contribution of each component to the whole system to make clear how the main goal of the system is achieved. The plant process is described abstractly in function level by mass, energy and information flows, which represent the interaction between different components and enable the causal reasoning between functions according to the flow properties. Thus, in the abnormal status, a goal-function-component oriented fault diagnosis can be performed with the model at a very quick speed and abnormal alarms can be fully explained by the reasoning relationship of the model. In this paper, an interface design of the PWR plant is built by the conception of means-end and part-whole by using MFM, and several simulation cases are used for evaluating the fault diagnosis performance. The results show that the system has a good ability to detect and diagnose accidents timely before reactor trip.

Modeling of PWR Plant by Multilevel Flow Model and its Application in Fault Diagnosis

Modeling of PWR Plant by Multilevel Flow Model and its Application in Fault Diagnosis

Improving automatic control of complex water systems, using AI techniques: design of an expert component for the alarms analysis

The aim of this paper is to present an intelligent software tool, using Artificial Intelligence (AI) techniques, which allows the execution of an alarm analysis, during the remote sensing activity of complex plants. The AI component allows to identify all the primary faults of the system, discriminating them from the side effect alarms. In other words this tool shows which alarms are directly connected to primary faults and which alarms are consequential effects of the primary ones. The core of the software is an algorithm which uses a knowledge ontology and a set of alarm propagation rules, which are both based on a Multilevel Flow Modelling (MFM) paradigm. The algorithm has been tested implementing a rule based expert system (RBES) referred to an existing water plant. The main features of the water plant have been identified and all the main components and their possible alarm states have been analyzed to carry out the knowledge base.