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Safeguards identification in computer aided HAZOP study by means of multilevel flow modelling

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2023年第5期237卷 922-946页

作者： Wu, Jing Song, Mengchu Zhang, Xinxin Lind, Morten Tech Univ Denmark Dept Elect & Photon Engn Room 114Bldg 326Elektrovej DK-2800 Lyngby Denmark

multilevel flow modelling (MFM) was proposed as a knowledge representation method for Hazard and Operability Studies (HAZOPs). MFM reasoning software can facilitate the cause-consequence analysis during the HAZOP analysis of whole life cycle of the plant. Recent studies have further confirmed that MFM offers the opportunity to redeploy the insights achieved by the HAZOP team to assist an operator dealing with an abnormal event. However, past studies into MFM-based HAZOP have been lacking in the specification of the principle. This principle makes MFM possible to determine safeguards for studied hypothetical events. This paper proposes such principle, which further increases the application of computer aids in HAZOP studies. The paper provides an analysis and classification of different types of safeguards on the functional level and introduces the safeguards into MFM methodology. It further presents an MFM-specific barrier function and its reasoning rules. The safeguard representation and reasoning transparency explicitly the relationship between suitable safeguards and hypothetical events given knowledge about the complex interdependencies between process design, equipment design, safety barriers and instrumentation. Based on the principles developed, an existing MFM model of a typical oil and gas process module is extended with explicit safety functions and used as an example for utilizing the specified principle for identification of safeguards. Potential safeguards for the process module are produced as the results.

关键词： Artificial intelligence knowledge representation knowledge-based reasoning safety functions multilevel flow modelling

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Integrating Hazard-oriented Knowledge Representation for multilevel flow modelling in Process Hazard Identification 22

Integrating Hazard-oriented Knowledge Representation for Mul...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Li, Ruixue Wu, Jing Zhang, Xinxin Ravn, Ole Tech Univ Denmark Dept Elect & Photon Engn Lyngby Denmark

ISBN: (纸本)9781713872344

In addition to process control at the equipment level, the identification of systemic hazards and the realization of process safety are also part of the operational problems of complex systems. To improve the effectiveness of hazard identification based on the multilevel flow modelling(MFM) models, this paper proposes that a hazard-oriented knowledge representation can represent more hazards than previous models used for online operational decision support in terms of latent intention, chemical, and physical hazard properties of materials and effects of multiple conditions in combination. Thus, the scope of application of the MFM model is extended from online operational decision support to hazard identification, and the application phase of the model is extended from the operational phase to the design phase. Moreover, the results are demonstrated by a Minxo process modeling and compared with its Hazard and Operability study(HAZOP) report results, and the representation rate of hazard of the MFM model incorporating hazard-oriented knowledge is improved, showing great potential for further optimization of the MFM-assisted HAZOP study. Copyright (c) 2023 The Authors.

关键词： Safety Analysis multilevel flow modelling System Control Hazard HAZOP Functional Modeling Process Systems

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Integrating Hazard-oriented Knowledge Representation for multilevel flow modelling in Process Hazard Identification

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IFAC-PapersOnLine 2023年第2期56卷 7208-7215页

作者： Ruixue Li Jing Wu Xinxin Zhang Ole Ravn Department of Electrical and Photonics Engineering Technical University of Denmark Denmark

In addition to process control at the equipment level, the Identification of systemic hazards and the realization of process safety are also part of the operational problems of complex systems. To improve the effectiveness of hazard Identification based on the multilevel flow modelling(MFM) models, this paper proposes that a hazard-oriented knowledge representation can represent more hazards than previous models used for online operational decision support in terms of latent intention, chemical, and physical hazard properties of materials and effects of multiple conditions in combination. Thus, the scope of application of the MFM model is extended from online operational decision support to hazard identification, and the application phase of the model is extended from the operational phase to the design phase. Moreover, the results are demonstrated by a Minxo process modeling and compared with its Hazard and Operability study(HAZOP) report results, and the representation rate of hazard of the MFM model incorporating hazard-oriented knowledge is improved, showing great potential for further optimization of the MFM-assisted HAZOP study.

关键词： Safety Analysis multilevel flow modelling System Control Hazard HAZOP Functional Modeling Process Systems

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Quantifying performance of passive systems in an integrated small modular reactor under uncertainties using multilevel flow modelling and stochastic collocation method

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PROGRESS IN NUCLEAR ENERGY 2022年 149卷

作者： Huang, Zhi 'ao Miao, Huifang Lind, Morten Zhang, Xinxin Wu, Jing Xiamen Univ Coll Energy 4221-104 Xiangan South Rd Xiamen 361002 Peoples R China Tech Univ Denmark Dept Elect Engn DK-2800 Lyngby Denmark

Many newly designed advanced nuclear reactors deploy several passive systems to increase the inherent safety. However, uncertainties often exist in these new designs because of lacking sufficient operation experience and experimental data, which leads to the possible deviation on the performance of systems from their prospective values and increases the risk of the plant. Thus, methods should be developed to quantify the influence of uncertainties on the performance of the passive systems properly to improve our understanding of these new designs and provide support for decision-making. Traditionally, the system performance analysis under uncertainties is performed by Monte-Carlo based methods (MCS), which is extremely time-consuming. Thus, to circumvent the limitation of traditional Monte-Carlo based method, a novel hybrid method that combines multilevel flow modelling and stochastic collocation (MFM-SC) is proposed in this paper. The MFM-SC method utilizes the MFM to model the functions of collaborative working passive systems and inference the relevant uncertain parameters according to causal reasoning rules. Then, the stochastic collocation method which combines the strength of Monte Carlo methods and stochastic Galerkin methods is used to create an orthogonal polynomial based surrogate model for the thermal hydraulic model. With the efficiency of the surrogate model in computation, the quantification of the performance of passive systems under uncertainties which requires thousands of repetitive computations can be easily conducted. An example test case is also presented in this paper to analyze the performance of core makeup tanks and the steam drum in an integrated small modular reactor during a station blackout accident under multiple uncertainties using MFM-SC method. The statistical results of the deviation range of the systems' performance under uncertainties are given according to thousands of experiments using a surrogate model, and the response surface

关键词： Uncertainty quantification Passive systems multilevel flow modelling Stochastic collocation Integrated small modular reactor

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Causality validation of multilevel flow modelling

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COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷 106944-106944页

作者： Nielsen, Emil Krabbe Gofuku, Akio Zhang, Xinxin Ravn, Ole Lind, Morten Tech Univ Denmark Dept Elect Engn Elektrovej 326 DK-2800 Lyngby Denmark Tech Univ Denmark Dept Chem Engn Soltofts Plads 227 DK-2800 Lyngby Denmark Okayama Univ Grad Sch Interdisciplinary Sci & Engn Hlth Syst 3-1-1 Tsushima Naka Okayama 7008530 Japan

multilevel flow Modeling is a methodology for inferring causes or effects of process system anomalies. A procedure for validating model causality is proposed, as interest has increased from industry in applications to safety-critical systems. A series of controlled experiments are conducted as simulations in K-Spice, a dynamic process simulator, by manipulating actuators to analyse the response of process variables. The system causality is analysed stochastically under a defined range of randomly sampled process conditions. The causal influence of an actuator on a process variable is defined as a probability of a qualitative and discrete causal state. By testing an MFM model, and interpreting the propagation paths produced by MFM, the results from MFM are compared to the stochastic causality analysis to determine the model accuracy. The method has been applied to a produced water treatment system for separation of liquid and gas, to revise the causal relations of the model. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Causality multilevel flow modelling Validation Causal inference

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Monte Carlo Simulations for probabilistic validation of consequence reasoning from multilevel flow modelling 25

Monte Carlo Simulations for probabilistic validation of cons...

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25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Nielsen, Emil K. Kirchhtibel, Denis Jorgensen, Thomas M. Tech Univ Denmark Dept Elect Engn Lyngby Denmark Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9781728189567

multilevel flow modelling can be used to identify causes or consequences of anomalies in process systems. The models can be used to identify numerous possible propagations of causes or effects but cannot distinguish between likely and unlikely causes or effects. We present a method for identifying likely and unlikely effect propagations in a given process window from Monte Carlo Simulations. We show that the joint probability of effects can be used to determine the probability of individual propagation paths. The analysis allows to identify subsets of the process window where certain effect propagations are more likely. The method enables prompt identification of likely propagations of effects from process anomalies.

关键词： Functional modelling multilevel flow modelling Consequence prediction Monte Carlo Simulations

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Representing Operational Knowledge of PWR Plant by Using multilevel flow modelling

Representing Operational Knowledge of PWR Plant by Using Mul...

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International Symposium on Future I&C for Nuclear Power Plants（ISOFIC2014）

作者： Xinxin ZHANG Morten LIND Sten Bay J?RGENSEN Niels JENSEN Ole RAVN Department of Electrical Engineering Technical University of Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark Safepark Consultancy

The aim of this paper is to explore the capability of representing operational knowledge by using multilevel flow modelling(MFM) *** paper demonstrates how the operational knowledge can be inserted into the MFM models and be used to evaluate the plant state,identify the current situation and support operational *** paper will provide a general MFM model of the primary side in a standard Westinghouse Pressurized Water Reactor(PWR)system including sub-systems of Reactor Coolant System,Rod Control System,Chemical and Volume Control System,emergency heat removal *** the sub-systems' functions will be decomposed into sub-models according to different operational *** operational model will be developed based on the operating procedure by using MFM symbols and this model can be used to implement coordination rules for organize the utilization of different MFM models in different *** the operational model and different process models,MFM can be used to identify plant situation.

关键词： multilevel flow modelling Operation Knowledge Reorientation PWR primary system

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multilevel flow Model of an Espresso Machine 11

Multilevel Flow Model of an Espresso Machine

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Sirola, Miki Thunem, Harald P-J Aalto Univ PB 15400 FI-00076 Espoo Finland Inst Energy Technol PB 173 NO-1751 Halden Norway

ISBN: (纸本)9781665426053

A functional model of an Espresso machine has been constructed. The multilevel flow modelling (MFM) technique developed at the Technical University of Denmark (DTU) and the MFM Suite tool developed in the Halden Project were used in modelling. The Espresso machine process including additional instrumentation for experimental setup is described. The MFM model is illustrated in detail and one example scenario analysing one possible failure is presented. Constructing a functional model of a challenging batch process has succeeded rather well. The qualitative correctness of the model is evaluated with several tests and one of them is presented in this paper.

关键词： component multilevel flow modelling Espresso machine Functional modelling

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Probabilistic safety margin characterization of an integrated small modular reactor using MFM and adaptive polynomial chaos

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ANNALS OF NUCLEAR ENERGY 2022年 171卷 109016-109016页

作者： Huang, Zhi'ao Miao, Huifang Lind, Morten Zhang, Xinxin Wu, Jing Xiamen Univ Coll Energy 4221-104 Xiangan South Rd Xiamen 361002 Peoples R China Tech Univ Denmark Dept Elect Engn DK-2800 Lyngby Denmark

Characterization of the safety margins can provide essential information for both licensee and regulatory commission to support decision-making, especially for the next generation of nuclear power plants which implement several novel designs. However, the traditional monte-carlo based best estimate plus uncertainty method is not appropriate for large scale application because of its high computational cost. Thus, a novel hybrid method that combines multilevel flow modelling (MFM) and generalized polynomial chaos (gPC)is proposed in this paper. In the framework of the proposed method, the MFM is utilized to model the functions of collaborative working passive systems of an integrated small modular reactor (iSMR) and perform causal reasoning for selecting relevant uncertain parameters, then the gPC is implemented to construct an orthogonal polynomials-based surrogate model for the thermal hydraulic model to perform fast sensitivity analysis and safety margin characterization. An adaptive sampling algorithm is also implemented to improve the efficiency and accuracy of the safety margin characterization. The proposed method is applied to a test case to analyze the sensitivity of uncertain parameters during an SBO accident of iSMR. Subsequently, the adaptive method is implemented for probabilistic safety margin characterization after considering a delayed core makeup tank (CMT) injection and the conditional core damage frequency is predicted. The result shows great efficiency of the proposed MFM combining adaptive gPC method. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Safety margin characterization multilevel flow modelling Generalized polynomial chaos Adaptive sampling Integrated small modular reactor

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Consequence Reasoning in multilevel flow modelling

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IFAC Proceedings Volumes 2013年第15期46卷 187-194页

作者： Xinxin Zhang Morten Lind Ole Ravn Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby 2800 Denmark (Tel: +45 45253562)

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.

关键词： multilevel flow modelling knowledge representation functional modelling consequence reasoning rule-based system decision support system

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