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![Fig. 1. Example of water and oil flow in a hydrocylone [17].](publication/323955151/figure/fig1/AS:865112722268182@1583270354527/Example-of-water-and-oil-flow-in-a-hydrocylone-17_Q320.jpg)
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-cri...
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... PDR is controlled using two control valves, one at each outlet. A P&ID of the hydrocyclone used for experimental work is shown in Fig. 2. The setup has a pressure and a flow rate sensor on all inlets and outlets and one control valve on each outlet. The input water is delivered from a water tank by a pump. Both the underflow and overflow outputs are transported to the same water ...
Citations
... Multilevel Flow Modeling (MFM) is a functional modeling approach that has been shown to be capable of capturing causal-dependencies of complex-processes like NPP, and has been enabled automated reasoning [39,40], fault diagnosis [41,42] and planning for severe accident M. Song et al. management [43,44]. MFM describes the function-objective structure of a system in terms of a set of casually dependent mass and energy flows on different levels of abstraction. ...
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.
... An overview of the main process parts is shown in Figure 4. The system has, in the past, been used for several investigations to test/develop new control algorithms for process systems, modeling, slugging, fault detection/diagnosis, instrumentation performance, and HMI design [13,[43][44][45][46][47]. ...
Offshore oil and gas facilities are currently measuring the oil-in-water (OiW) concentration in the produced water manually before discharging it into the ocean, which in most cases fulfills the government regulations. However, as stricter regulations and environmental concerns are increasing over time, the importance of measuring OiW in real-time intensifies. The significant amount of uncertainties associated with manual samplings, that is currently not taken into consideration, could potentially affect the acceptance of OiW monitors and lower the reputation of all online OiW measurement techniques. This work presents the performance of four fluorescence-based monitors on an in-house testing facility. Previous studies of a fluorescence-based monitor have raised concerns about the measurement of OiW concentration being flow-dependent. The proposed results show that the measurements from the fluorescence-based monitors are not or insignificantly flow-dependent. However, other parameters, such as gas bubbles and droplet sizes, do affect the measurement. Testing the monitors’ calibration method revealed that the weighted least square is preferred to achieve high reproducibility. Due to the high sensitivity to different compositions of atomic structures, other than aromatic hydrocarbons, the fluorescence-based monitor might not be feasible for measuring OiW concentrations in dynamic separation facilities with consistent changes. Nevertheless, they are still of interest for measuring the separation efficiency of a deoiling hydrocyclone to enhance its deoiling performance, as the separation efficiency is not dependent on OiW trueness but rather the OiW concentration before and after the hydrocyclone.
... To get meaningful results from the proposed knowledge based system the initial knowledge needs to be accurate. Nielsen et al. (2018) are proposing a framework for model validation by comparing the inference generated from an MFM model with the propagation documented by experts in e.g. a Hazard and Operability Study (HAZOP) or aqcuired from numerical simulation or process data. As outlined by Lind (2017), the creation of a model library will facilitate the modeling process. ...
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.
Selama waktu operasi reaktor, struktur, sistem dan komponen (SSK) reaktor, misalnya sistem pendingin primer, akan mengalami penuaan atau keusangan yang akan mempengaruhi kinerja dan operasi selamat dari reaktor tersebut. Hal ini juga berlaku pada Reaktor Serba Guna G.A. Siwabessy (RSG-GAS) yang usianya lebih dari 30 tahun. Oleh karena itu program manajemen penuaan harus dilakukan untuk mengatasi hal tersebut. Salah satu aktivitas yang dilakukan adalah dengan melakukan penapisan komponen kritis sistem pendingin primer. Penelitian ini bertujuan untuk melakukan penapisan tersebut menggunakan model multilevel flow modeling (MFM) pada sistem pendingin primer RSG-GAS. MFM adalah salah satu metode functional modeling yang mengubah sistem kompleks menjadi struktur fungsi-fungsi yang saling terhubung dengan hubungan sebab akibat. Metode yang dilakukan adalan dengan menerapkan beberapa skenario kejadian kecelakaan loss of flow accident (LOFA) yang terdapat pada Laporan Analisis Keselamatan (LAK) RSG-GAS pada model MFM tersebut. Dampak dari kejadian tersebut dapat dianalisis menggunakan aturan jalur perambatan (influence propagation). Hasil investigasi berupa komponen-komponen yang terdampak, yaitu katup isolasi, pompa primer dan alat penukar panas, dikelompokkan sebagai komponen kritis dan harus mendapat perhatian untuk penanganan lebih lanjut. Jika komponen-komponen tersebut mengalami keusangan atau kerusakan maka harus dilakukan perawatan atau penggantian sehingga kinerja reaktor dapat dipertahankan dan reaktor dapat tetap beroperasi dengan aman dan selamat.
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.
