Control Room Research Articles

IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation

According to the Indian scenario, the majority of reservoirs for holding water are operated independently, which is problematic when there are crises (abnormal inflow, cloudy conditions), which causes the surrounding communities and agricultural areas to be submerged those aquifers. Due to the vast geographic region and depth, it is challenging to manually measure the essential reservoir life metrics. Therefore, this research work suggests a cutting-edge system of reservoir management that includes sensors that are appropriate for measuring variables such as pressure, water level, outflow velocity, inflow velocity, tilt, vibration, etc. The Arduino Uno integrates all of the sensors, and Microsoft Power BI receives the data in real time, where each parameter is shown in an appropriate format for visualization. In case of an emergency water level rise, the alarm is set off. The procedure begins with the collection of data from sensors and concludes with the presentation of that data on a dashboard in a control room situated in a distant place that links to a website where the relevant information can be seen by visitors.

Blast-Resistant Performance of Steel Petrochemical Control Room with 3D-Kagome Sandwich Wall

As the control brain of the petrochemical plant, blast-resistant performance requirements are important for the sustainability of the petrochemical control room and should be guaranteed when the vapor cloud explosion occurs in the petrochemical production process. The 3D-Kagome truss core sandwich structure is a kind of blast-resistant material with high energy absorption and recycling. Considering the influential factors of the radius of the truss core rod and thickness of the upper and lower panels, in this paper, the blast-resistant performance of a real steel petrochemical control room with a 3D-Kagome truss core sandwich wall was analyzed. With the optimization goal of plastic deformation energy and panel displacement, the optimal wall thickness and radius of the truss core rod were obtained. The optimized blast-resistant walls were assembled, and the dynamic response of the steel petrochemical control room with the 3D-Kagome truss core sandwich blast-resistant wall was analyzed. The simulation results indicate that the truss core layer is ineffective in dissipating blast energy when the radius ratio of the truss core rod exceeds 2.7% of the total wall thickness. Moreover, as the thickness of the upper and lower panels increases from 0.5 cm to 3 cm, the proportion of plastic deformation energy in the truss core layer gradually rises from 55% to 95%, stabilizing at around 90%. The optimal configuration for blast resistance is achieved when the panel thickness ratio is 6.7% of the total wall thickness; the truss core rod radius ratio is 2.7% of the total thickness. This study establishes the effectiveness of the optimized 3D-Kagome sandwich wall as a blast-resistant solution for steel petrochemical control rooms.

A longitudinal study combining the Double Diamond framework and Behavior Change Wheel to co-create a sedentary behavior intervention in police control rooms.

Police work can be sedentary and stressful, negatively impacting health and wellbeing. In a novel co-creation approach, we used the Behavior Change Wheel (BCW) and Double Diamond (DD) design framework to guide the collaborative design and development of a sedentary behavior intervention in the control rooms of two British police forces. Multiple stakeholders participated in four phases of research. In Phase 1, a literature review, focus groups (n=20) and interviews (n=10) were conducted to 'discover' the relationship between physical activity and wellbeing in the police. In Phase 2, a steering group consolidated Phase 1 findings to 'define' a specific behavior for intervention. Phases 3 and 4 'developed' the intervention across six workshops with control room workers and six steering group workshops. The co-creation process identified contextual sedentary behavior as the target behavior, driven by behavioral regulation, social influence and social norms. The sedentary behavior intervention targeted these drivers and aimed to engage control room workers in short bursts of physical activity throughout their shifts. Key intervention features targeted involvement of staff in decision-making and embedding physical activity into work practices. The BCW and DD can be combined to co-create evidence-based and participant-informed interventions and translate science into action.

Design, development and commissioning of the control system for the cryogenic distribution system at ESS

The cryogenic distribution system (CDS) at the European Spallation Source (ESS) connects the refrigeration plant with the linear accelerator, consisting in 13 spoke and 30 elliptical cryomodules that are cooled at 2K via multi-transfer lines, individual valveboxes for every cryomodule and an endbox. The designed control system monitors and controls the cooling helium flow through the CDS. The controls architecture for the CDS is based on one Programmable Logic Controller (PLC) per valvebox and another one for the endbox, which are integrated into EPICS through the Controls Network and takes care of the process functions, making it a total of 44 PLCs. This type of integration allows for the remote operation of the CDS from the control room and the necessary interaction of the control system with other related systems and EPICS services like archiving, alarms and save-and-restore. Pre-commissioning activities like loop checks, temperature curve validation, pressure sensor calibration and valve initialization started in summer 2022. Eventually, the system was ready for commissioning and a first cooldown was performed in December 2022. The paper describes the design, development and commissioning of the control system and diverse challenges during the deployment and commissioning activities. Future activities are discussed including the implementation of a sequencer for automatic cooldown and warmup, integration with the cryomodule controls and a master PLC to handle the helium management.

Identification of propagation path and root cause of faults based on generative adversarial networks in industrial systems

Fault diagnosis and detection (FDD) improves product quality, process safety, and profitability in modern industrial plants. Faults can propagate between different parts of plants due to connections between devices and control loops. As a result, control room operators may receive multiple alarms that distract their attention from addressing the main causes of abnormal situations. Therefore, investigating causal relationships between process variables is essential in determining the root cause of faults. This study proposes a new systematic approach for causality analysis to identify the propagation path and root cause of faults in industrial plants. The research's innovations are categorized into two main parts. Initially, the study utilizes machine learning algorithms to identify causal relationships using probabilistic concepts. Additionally, it introduces a novel architecture by merging the generative adversarial network (GAN) with the variational auto-encoder (VAE). The process variables are mapped to a latent space, where an adversarial generative network will investigate the causal relationship between variables. Finally, the Tennessee Eastman Process simulation and gas turbine industrial data are used to evaluate the proposed algorithm.

An ontology of 3D environment where a simulated manipulation task takes place (ENVON)

Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.

Design Change and Operational Consideration of the HVAC System during Nuclear Power Plant Decommissioning

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.

Perancangan Monitoring Coal Handling System PLTU Kaltim Teluk Berbasis Internet of Things

Abstract— Balikpapan Bay PLTU is a large capacity power plant located in the city of Balikpapan, which is located in East Kalimantan. This PLTU with a capacity of 2X110 MW meets the electricity needs of the Barito Mahakam network, which consists of the East Kalimantan, North Kalimantan, Central Kalimantan and South Kalimantan regions. The main fuel for electricity generation is low grade coal (LRC) or low calorie coal. High Speed Diesel (HSD) oil is used as initial fuel. DCS (Distributed Control System) is used for wood management at PLTU Kaltim Teluk. DCS helps operations by monitoring and controlling the main and supporting components, however, management, supervisors and other staff have difficulty knowing the operating conditions of the generating unit remotely because the monitoring system can only be accessed by the control room operator at the CCR. The aim of this research is to design an Internet of Things-based coal handling system control system for PLTU Kaltim Teluk. This real time based digital monitoring system can collect and display data from every sensor in the field. The research method is a literature study method and a field method to apply the design results and collect monitoring data at the two Balikpapan Bay PLTUs. The results show that testing, which includes integration testing with MySQL, functional testing, and URL and Endpoint testing, was carried out successfully. As expected, all the main features and functions of the PLTU monitoring system are running well. This system has averagepercentage error 0.1%. So this study has an accuracy rate of 99.9%. This system has the ability to make it easier for managers to monitor the system.

Thermal conductivity of an external wall with simulated smart Aerogel insulation system

Modern technologies are more than ever adopting intelligent designs that respond to environmental variants to produce more desirable outcomes. In the building and housing industries, intelligent designs aim to conserve energy through temperature controls and energy storage and distribution systems. In this context, an intelligent Aerogel insulation system is modelled and studied for the exterior building walls. Aerogel with a thermal conductivity of about 0.012 W/m-K has a superior insulating property, and an intelligent design built around it would introduce additional energy storage and delivery enhancements to the system. In this study, a test room and a control room in an open urban environment were built, and the intended intelligence, first considered by choosing Aerogel that has the potential to be engineered with smart properties according to researchers in composite material science, and second was considered by automation which was applied manually to the test room. The calculated thermal conductivity of an external wall with intelligent Aerogel insulation is -(1.83 ± 0.06) × 10−1 W/m. The negative sign is an attribute of a system function rather than of a material. The external wall with intelligent Aerogel insulation, in comparison to an identical non-insulated wall, exhibited 2.7 times better energy conservation.

Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and application of a machine learning algorithm to replace CFAST model

Accurately predicting evacuation time in a ventilated main control room (MCR) during fire emergencies is crucial for ensuring the safety of personnel at nuclear power plants. This study proposes to use neural networks alongside consolidated fire and smoke transport (CFAST) simulations to serve as a surrogate model for physics-based simulation tools. Our neural networks can promptly predict the evacuation time in MCRs, proving to be a valuable asset in fire emergencies and eliminating the need for time-consuming rollouts of the CFAST simulations. The CFAST model simulates fire and evacuation scenarios in a ventilated MCR with variations in input parameters such as door conditions, ventilation flow rate, leakage area, and fire propagation time. Target output parameters, such as hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD), are used alongside standardized evacuation variables to train a machine learning model for predicting evacuation time. The findings suggest that high ventilation flow rates help to dilute smoke and discharge hot gas, leading to lower target output parameters and quicker evacuation. Standardized evacuation variables exceed the required abandonment criteria for all door conditions, indicating the importance of proper evacuation procedures. The results show that neural networks can generate evacuation times close to those obtained from CFAST simulations.

Extrapolation of Monte Carlo–Derived Noble Gas Air-Submersion Effective Dose Rate Coefficients for Selected Occupational Settings to Other Geometries and Radionuclides

External exposures to airborne radioactivity at nuclear power sites are typically based on semi-infinite clouds with uniform concentrations and corresponding effective dose rate coefficients (EDRCs), along with the simplifying assumption that the radioactive clouds extend to infinity around the receptor of interest. The two regulatory models that are typically employed for finite-cloud adjustments to submersion doses, namely, Regulatory Guide 1.183 for hemispherical clouds and the International Commission on Radiological Protection (ICRP) document ICRP-30 for spherical clouds, were purposely oversimplified to facilitate their implementation. As a result, dose projections can be significantly underestimated under certain circumstances, particularly with radionuclides emitting low-energy photons and/or particles. In addition, these adjustments do not account for scatter radiation off surrounding walls, ceilings, and floors of typical occupational settings. In recognition of these limitations and for the mitigation thereof, Veinot et al. published an article [Rad. and Environ. Biophy., Vol. 56, p. 453 (2017)] that provided monoenergetic photon, electron, and positron EDRCs based on elaborate Monte Carlo computations for submersion in three typical occupational settings at nuclear facilities (namely, an office, a laboratory, and a warehouse). Included in the article were also EDRCs for exposure to 45 noble gases airborne within the said occupational settings. However, extrapolation of the results to other settings was limited due to geometry variations. Even so, as described in the present paper, the Veinot et al. article provided the basis for the definition of a straightforward model for extrapolation of the Monte Carlo–derived EDRCs to other occupational settings and radionuclides. The objective of the EDRC extrapolation model in this paper is to provide a simple but comprehensive approach to licensing-basis submersion dose calculations consistent with the recommendations in ICRP Publication 103. The model is reasonably accurate and applicable to submersion volumes that are smaller than an office and larger than a typical warehouse, as would be needed for control room habitability evaluations. It is emphasized that the model is only suitable for the external dose computation of airborne noble gases and particulates; dose contributions via the inhalation pathway and from direct shine from contaminated surfaces need to be evaluated separately. The EDRC extrapolation model was programmed into an Excel workbook (OccuSetEDRCs-R0.xlsx) that is available to interested parties; see Supplementary Data section for further details. The model can handle all 1252 radionuclides in ICRP-107 and submersion volumes ranging between about 40 and 4000 m3, with an upper estimated error of about 10% on average for large submersion volumes.

Novel Approach for Network Data Exchange Among Safety-Related Control Systems of Nuclear Reactors

Industrially mature digital networking technologies form the backbone of the distributed control architectures of nuclear reactors. Typically, hundreds of control systems responsible for assuring safer and secure plant operations are located in the field and interconnected via wired isolated local area networks, which are also connected to centralized servers and operating consoles located in the main control room. Many of such control systems require data processed by other systems situated far away in the plant, which mandates multiple concurrent reliable TCP sessions to be active or live for such field-level control systems. Multi-socket communication beyond 10 is practically difficult for embedded systems with limited memory and processing capabilities; hence, data accumulation, extraction, and routing are typically done with the help of commercially available high-performance servers and switches. The servers and operating consoles of a plant are generally Linux-based commercial-off-the-shelf (COTS) industrial computers, whose development lifecycle is not accessible for review and in accordance with safety guides; hence, qualifying such components for safety operations is extremely difficult. This paper presents a novel way of independently offloading safety-related data transactions from such COTS components (reducing dependency) with the help of very simple, reliable, and secure hardware, which is referred to as a data exchange and processing unit (DEPU). An approach is proposed to realize distributed control architecture with this proposed hardware (to simplify qualification tasks) along with existing servers and operating consoles without compromising powerful and convenient human-machine interface as well as the data logging capabilities of such components. Hardware and software design perspectives, performance characterization, and qualification testing of the DEPU in accordance with nuclear power plant instrumentation and control system design guidelines are also briefed in this paper.

Fire simulation in the main control room of Bushehr nuclear power plant using CFAST software

Fire simulation in the main control room of Bushehr nuclear power plant using CFAST software

Study on the Engineering Application of LNG Accident Consequence Model in the Design of Terminal Sump

In order to provide a theoretical basis for the design of the LNG terminal sump, this paper proposes a suitable mathematical model and solution method by analyzing the physical process of evaporation diffusion and thermal radiation of the sump. Combined with the engineering practice, a lot of calculations have been carried out on the vapor diffusion concentration and pool fire thermal radiation intensity of the terminal sump, and the correlations between the vapor concentration and thermal radiation intensity and their influencing factors have been quantitatively calculated. Based on the analysis of the calculation results, it is concluded that the vapor concentration and thermal radiation value at the fire control room of the wharf are strongly related to the equivalent diameter of the sump and the distance from the fire control room to the sump. Among them, the distance from the fire control room to the sump is more closely related to the vapor concentration, and the equivalent diameter of the sump is more closely related to the thermal radiation; Keeping 19.2~33.8meters between the fire control room and the sump can basically ensure that the vapor concentration at the fire control room does not exceed the standard; In order to ensure that the thermal radiation at the fire control room does not exceed the standard, the distance between the fire control room and the sump should not be less than 15meters, and the equivalent diameter of the sump should not be greater than 7meters. The research methods and conclusions proposed in this paper can provide useful reference for engineering designers, and also build a bridge for mutual reference and application for theoretical research and engineering practice.

Design of Water Pump Station Control in Polder System using IoT and Hybrid Fuzzy Neural Network

This study aims to design a prototype of water pump station control in a polder system using IoT and artificial intelligence at Sringin Polder system. The design includes the hydrology analysis to define how the pump station should be set to control the level of the embankment. The second part is the IoT system that contain the sensor and interface to the water pump in the pump station. The third part is the core of control system using hybrid fuzzy neural network as the heart of the control system. Pump station has important role in the polder system to control the level of water in embankment. Five pumps in the system should operate based on schedule determined by the hydrology modelling. Since it is located in separate area with the control room, MQTT protocol is used to deliver data communication in both ways, to send the water level detected by ultrasonic sensor and also to switch on and off the pump by control system. In other hand, hybrid fuzzy neural network is used to control the pump scheduling based on water level detected.

Measuring team situation awareness through team communication: A study on nuclear main control room crews.

Situation awareness (SA) in complex socio-technical systems is considered a key cognitive activity, as control crews are often engaged in simultaneous task processes and are required to monitor and evaluate system parameters, making decisions and projections for the future accordingly. However, measuring SA at the team level is still a challenging area of research within the topic. In our research, we applied verbal protocol analysis as an alternative method to assess nuclear power plant control room crews' team SA. We conducted a study of 10 control room crews, examining their intrateam communication and its relatedness to team performance. We have found that communication categories related to the second and third levels of team SA increased significantly after the onset of an emergency event. Furthermore, while none of the team communication categories was related to team performance before the emergency event, all of them showed a strong positive correlation with team performance after the emergency situation occurred. Our results underline the importance of adequate verbalization of key information within the team, so as to support the rapid and accurate development of team SA during emergency situations.

Extended cognitive reliability and error analysis method for advanced control rooms of nuclear power plants

This study proposes a modified extended cognitive reliability and error analysis method (CREAM) for achieving a more accurate human error probability (HEP) in advanced control rooms. The traditional approach lacks failure data and does not consider the common performance condition (CPC) weights in different cognitive functions. The modified extended CREAM decomposes tasks using a method that combines structured information analysis (SIA) and the extended CREAM. The modified extended CREAM performs the weight analysis of CPCs in different cognitive functions, and the weights include cognitive, correlative, and important weights. We used the extended CREAM to obtain the cognitive weight. We determined the correlative weights of the CPCs for different cognitive functions using the triangular fuzzy decision-making trial and evaluation laboratory (TF-DEMATEL), and evaluated the importance weight of CPCs based on the interval 2-tuple linguistic approach and ensured the value of the importance weight using the entropy method in the different cognitive functions. Finally, we obtained the comprehensive weights of the different cognitive functions and calculated the HEPs. The accuracy and sensitivity of the modified extended CREAM were compared with those of the basic CREAM. The results demonstrate that the modified extended CREAM calculates the HEP more effectively in advanced control rooms.

Automatic Transmission Line Fault Detection System Using GSM Technology

Abstract: The electrical system is divided into several different parts. One of them is the transmission system, where electricity is transmitted from substations and substations along power lines to consumers. Both methods can experience various types of errors, often referred to as "problems". A fault is simply defined as a sequence of unwanted but unavoidable events that can temporarily disturb the steady state of a power system when the insulation of the system fails at some point. An intelligent fault finding and localization system was used to adequately and accurately mark and locate the occurrence of a fault. This ensures a shorter response time for the technical team to fix these faults, thus helping to save transformers from damage and disasters. The system uses current transformer, voltage transformer, Arduino uno controller, RS-232 connector and LCD display, GSM module, audio signal. The system automatically detects faults, analyzes and classifies them, and then calculates the distance of the fault from the control room by means of an algorithm based on impedance. Finally, the error information is transmitted to the control room.

Real-time decision support for human–machine interaction in digital railway control rooms

This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs). In this setting, TCs decide on the spot whether to manually or automatically open signals to regulate railway traffic, a critical aspect of ensuring punctuality and safety. This time-setting specific HMI differs across TCs and is not yet supported by a data-driven tool. The proposed DSS includes agreement levels for predictions among different modeling paradigms: linear models, tree-based models, and deep neural networks. SHAP (SHapley Additive exPlanations) values are deployed to assess the agreement level in explainability between these different modeling paradigms. The prescriptions are based on the HMI of well-performing peers. We implement the DSS as proof of concept at the Belgian railway infrastructure company and report end-user feedback on the perception, the operational impact, and the inclusion of agreement levels.

Dosimetry during iodine-131 therapy - a technical point of view from a single centre's own experience.

Nuclear medicine uses radionuclides in medicine for diagnosis, staging, therapy, and monitoring the response to therapy. The application of radiopharmaceutical therapy for the treatment of certain diseases is well-established, and the field is expanding. Internal dosimetry is multifaceted and includes different workflows, as well as various calculations based on patient- specific dosimetry. The objective of this study was to introduce the technical issues which might occur during iodine-131 (¹³¹I) dosimetry performed in nuclear medicine departments. Retrospective analysis was performed on a group of 44 patients with papillary thyroid cancer who between May 2021 and October 2021 underwent a 131I treatment: 80-100 mCi (2200-3700 MBq, based on the previous medical history and stage of the disease). Patients underwent a series of ¹³¹I therapy scans using gamma camera Discovery NM 670 CT. Whole body scan (WBS) was performed 2, 4, 24 and 48 hours after ¹³¹I administration. Additionally, after 24 hours of single photon emission computed tomography/ computed tomography, two fields of view (SPECT/CT 2-FOV) were performed from the mid-head to the bladder. During the dosimetry procedure, several issues arise. Firstly, after receiving therapeutic doses of ¹³¹I, patients should remain in their rooms until the appropriate activity is achieved before being transported to the diagnostic room. Secondly, the walls between examination rooms meet the requirements for accurate diagnosis but not for therapy, leading to the occurrence of artefacts in patients examined behind the wall, potentially influencing the examination results. Thirdly, personnel in the control room also experience additional exposure (10 times greater than in the case of standard diagnostic procedure). The dosimetry in patients in whom therapeutic procedures are performed with the use of isotopes is mandatory according to Polish and European law, technical issues which occur during the dosimetry procedures might influence the organization of the work in departments.