Multilevel Flow Modeling Research Articles

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Enhancing Offshore Drilling Efficiency through Multilevel Flow Modeling of MPD Operations

Exploiting all forms of energy must be coupled with the preservation of human safety and the environment. Despite the energy reserves beneath the ocean depths, challenging obstacles confront offshore drilling operations, notably the high pressure beneath the well. To comprehend this process better, we turned to modeling the managed pressure drilling (MPD) using multilevel flow modeling (MFM). These models provide a robust framework enabling us to understand and analyze the intricacies of this complex process. Our use of these models allows us to examine the causes and effects associated with various aspects of this process. All of this enables us to drill safely for the well, achieving our primary goal in this context.

An integrated system theoretic process analysis with multilevel flow modeling for the identification of cyber‐physical hazards in a process industry

AbstractThe deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the “5 flows” concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems.

A dynamic assessment method for risk evolution in chemical processes based on MFM-HAZOP-FDBN

At present, chemical accidents have occurred frequently with disastrous consequences. In order to effectively prevent chemical accidents and their secondary disasters, it is urgent to develop detailed risk assessment for chemical processes. However, traditional methods tend to ignore the dynamic correlation coupling of chemical processes and devices; this results in the assessment results having poor accuracy. Therefore, a dynamic assessment method for risk evolution in chemical processes based on MFM-HAZOP-FDBN is proposed in this study. First, multilevel flow model (MFM) is established according to the transfer paths of material flow and energy flow in a chemical process, and further combined with hazard and operability analysis (HAZOP) to form the functional HAZOP technology. Second, risk identification results from the MFM-HAZOP are applied as the input of a fuzzy dynamic Bayesian network (FDBN); the device states and process errors are used as dynamic and static nodes of FDBN to present the risk propagation and evolution mechanism in the chemical process, respectively. Finally, the dynamic risk evolutionary trend throughout the chemical process is assessed quantitatively by determining the occurrence likelihood and consequence severity based on the risk matrix. The BP America (Texas City) refinery explosion is taken as a case study. Results show that the proposed method can provide a detailed characterization and dynamic assessment of risk evolution in the chemical process and the assessment results are more reasonable and effective.

Explicit Representation of Mechanical Functions for Maintenance Decision Support

Artificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training data. Functional information is seen as the most fundamental and important knowledge in maintenance decision making. This paper first proposes a mechanical functional modeling approach based on a functional modeling and reasoning methodology called multilevel flow modeling (MFM). The approach actually bridges the modeling gap between the mechanical level and the process level, which potentially extends the existing capability of MFM in rule-based diagnostics and prognostics from operation support to maintenance support. Based on this extension, a framework of optimized CBM is proposed, which can be used to diagnose potential mechanical failures from condition monitoring data and predict their future impacts in a qualitative way. More importantly, the framework uses MFM-based reliability-centered maintenance (RCM) to determine the importance of a detected potential failure, which can ensure the cost-effectiveness of CBM by adapting the maintenance requirements to specific operational contexts. This ability cannot be offered by existing CBM methods. An application to a mechanical test apparatus and hypothetical coupling with a process plant are used to demonstrate the proposed framework.

MFM-based alarm root-cause analysis and ranking for nuclear power plants

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

Automatic identification of maintenance significant items in reliability centered maintenance analysis by using functional modeling and reasoning

Complex industrial systems adopt reliability centered maintenance (RCM) for maintenance optimization to improve safety and reduce maintenance cost. Preserving function is the core maintenance principle, yet the function concept has not been systematically studied in the context of RCM. This article presents a framework of model-based RCM analysis, which is driven by functional modeling and reasoning. The study focuses on identifying the so-called maintenance significant items through assessment of failure consequences. Multilevel flow modeling (MFM) is proved competitive to identify sufficient system functions that expect maintenance to preserve. It is also able to define failure modes and represent their interactions with system functions, which are essential to RCM. A failure analysis tool for RCM is developed by taking advantage of the causal reasoning capability of MFM, which can be used to automatically analyze consequences of all failures predefined for a target system and generate equipment classifications useful to maintenance optimization. The study provides the possibility of RCM automation, which has been highly demanded by various complex industries. Moreover, as a fundamental functional knowledge framework, MFM can easily accommodate changes in design and operation, which affords the opportunity of implementing a living RCM program.

Integrating condition-based monitoring with process sensors information for operation and maintenance in a functional modeling framework

Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the system.

Integrating Hazard-oriented Knowledge Representation for Multilevel Flow Modelling in Process Hazard Identification

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.

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

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 surfaces are also calculated based on the MFM-SC method to reveal the relation between the uncertain inputs and the systems output separately. The result shows the significant improvement in the computational efficiency of the suggested MFM-SC method, and it can provide essential information for decision-making.

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

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.

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

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.

Concept of an intelligent operator support system for initial emergency responses in nuclear power plants

Nuclear power plant operators in the main control room are exposed to stressful conditions in emergency situations as immediate and appropriate mitigations are required. While emergency operating procedures (EOPs) provide operators with the appropriate tasks and diagnostic guidelines, EOPs have static properties that make it difficult to reflect the dynamic changes of the plant. Due to this static nature, operator workloads increase because unrelated information must be screened out and numerous displays must be checked to obtain the plant status. Generally, excessive workloads should be reduced because they can lead to human errors that may adversely affect nuclear power plant safety. This paper presents a framework for an operator support system that can substitute the initial responses of the EOPs, or in other words the immediate actions and diagnostic procedures, in the early stages of an emergency. The system assists operators in emergency operations as follows: performing the monitoring tasks in parallel, identifying current risk and latent risk causality, diagnosing the accident, and displaying all information intuitively with a master logic diagram. The risk causalities are analyzed with a functional modeling methodology called multilevel flow modeling. This system is expected to reduce workloads and the time for performing initial emergency response procedures.

Timing Aspects in Causality Analysis with Multilevel Flow Modelling

Temporal aspects of multilevel flow modelling (MFM) are important for reasoning about causes and consequences. In particular real time reasoning about sensor data are dependent on proper temporal ordering of events in order to cope with plant dynamics. The purpose of the present paper is to contribute to the further development of the temporal aspects of MFM by explaining how time stamps associated to the measured signals can be used to enhance the causality analysis and infer the possible causes of a sequence of alarms.

Knowledge acquisition and representation for intelligent operation support in offshore fields

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems’ instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support.

Integrative decision support for accident emergency response by combining MFM and Go-Flow

Emergency response planning associated with written procedures play a key role in the process safety to ensure that accident can be properly managed. Although the extensive use of digital techniques have significantly reduced the accident risk by early warnings and operation guidance, the Fukushima nuclear disaster reminds us that the unexpected events may still exceed existing system capability to indicate appropriate responding measures. A novel digitalized approach is hence required for planning decision support. This article presents a joint utilizing of two modeling and analysis methodologies, which are complementary to each other to provide comprehensive insights for decision-making in the accident emergency response. Multilevel Flow Modeling (MFM) is applied to develop an intelligent reasoning system, which can harness plant’s qualitative functional knowledge to search available equipments and corresponding action sequences potentially being able to manage the accident. Go-Flow is a success-oriented reliability analysis method specially suitable for quantitative risk assessment in the situations involving configuration changes. The underlying configuration of each MFM-driven emergency response measure will update the understanding of goal achievement and modify the Go-Flow chart, by which plan’s effectiveness of risk reduction can be evaluated. Moreover, Go-Flow is also used to conduct the risk matrix assessment for identifying critical components, which can provide a reference for effective assignment of resources in the long-term accident management. This work is expected to strengthen operator’s ability of emergency response planning from perspective of both measure development and optimization. An accident scenario similar to what occurred in the Fukushima Daiichi nuclear power plant has been used to demonstrate the application of the propose approach.

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. The MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

Identifying Safety Objectives and Functions for Emergency Shutdown in the Design Phase by Using Functional Modelling

This paper proposes a functional modelling method, called Multilevel Flow Modelling (MFM) for identification of safety objectives and functions of emergency shutdown (ESD) system in the design phase for emergency shutdown safely. Firstly, the required information for designing safety objectives and functions for an emergency shutdown is analysed. The information includes process topology, the initial state of the process, the shutdown objectives, and other constraints, such as design and operational limits of unit operations and hazardous and environmental constraints. Secondly, a procedure is proposed based on the required information by using MFM. The procedure incorporates steps: 1) The MFM model of the process in normal operations are built by following modelling procedures, 2) Initiate states are defined and changing conditions are identified of the process when it shuts down unplanned by using the objective-function tree in the MFM modelling, 3) The goals of the shutdown operation are defined, 4) With the consideration of the shutdown operating goals and changing conditions, the first safety function during shutdown process are identified by using MFM causal reasoning, 5) By using MFM consequence reasoning based on the first identified safety function represented in the MFM model, the rest of the safety functions are identified, and the emergency shutdown procedures are generated. This procedure is demonstrated by designing safety functions in an emergency shut down of a seawater deaeration process in a seawater injection system. The results show that the produced emergency shut down procedure based on the proposed method is feasible and that it can be validated against the real operating procedure.

Advanced and Rapid Tool in Control Room to Determine the Cause and Location of Events in Transmission Network

Operating personnel in control room act on SCADA alarm generated on data from station computer. Using new technologies and advanced technical solutions assistance tool can be designed. This tool provides quick help in busy situations for operator. For this new tool with three types of case studies insight will be given in this paper. Introduction part has short information about numbers of alarms and events in Control centre, and their distribution during one month period. Basic principles for alarm handling in SCADA system is given with all limitations. New tool, Intelligent Alarm Processing system is designed and implemented in control room. It has connection to SCADA system with standard data exchange format CIM/XML and run in real time, with only few seconds delay. This system based on Multilevel Flow Model has root cause analyses implemented for power system. Detail fault location algorithm description with block scheme for this Intelligent Alarm Processing system is part of third chapter. Special attention must be paid for modelling protection data in SCADA system which are sent to this new tool. Demonstration of Intelligent Alarm Processing system operation is reported in fourth chapter. Three characteristic disturbances in transmission network were elaborated. Most complex and challenging disturbances for operator in control room is cascading event. This case study is presented in detail in four sequences through graphical user interface. Second case study is also challenging for operators, heavy winter storm with numerous isolated events. In this case study very effective graphical presentation and alarm list with three types, primary event, consequences and detail list for this events were demonstrated. This list pointed out exactly and clearly what happened in the network. Last case study presents common disturbances which appears on daily basis, where this tool is of great assistance because it points on transmission elements very fast.

Causality validation of multilevel flow modelling

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.