Plant Simulation Research Articles

Application of physical and mathematical modeling in the educational process by the example of power plant simulators

This article discusses the application of digital twin technology in thermal power engineering. Special attention is paid to the influence of digital models of the existing equipment of the powerplant on the educational process in higher educational institutions and professional development of service personnel. The article summarizes the results of the introduction of power plant simulators into the curriculum, the methodology of laboratory work and the organization of training sessions. It is also shown that the introduction of physical and mathematical modeling in education and practice contributes to improving the safety and efficiency of work in the energy sector. The results of the introduction of plant simulators into the curriculum are summarized. The software of computer equipment provided for classes at a higher educational institution is considered as a tool for practicing operation skills and creating digital models. When working in a software package, the user is given the opportunity to control units of equipment individually, collectively as a whole unit, or create computational models consisting of simple blocks of equipment. The proposed mutual cooperation between software developers and universities can lead to the creation of digital models to enable retraining of specialists in the workplace, as well as increasing the level of knowledge of graduates. The result of the introduction of a virtual testing ground in a higher educational institution in order to verify the knowledge of students is considered. Thus, the introduction of digital twins into the educational process and practice of work in the thermal power industry is a promising direction for the development of the industry. It contributes to improving the level of training of specialists, ensuring the safety and efficiency of equipment operation, as well as reducing the risks of emergency situations

Anomaly Detection and Analysis in Nuclear Power Plants

Industries are increasingly adopting digital systems to improve control and accessibility by providing real-time monitoring and early alerts for potential issues. While digital transformation fuels exponential growth, it exposes these industries to cyberattacks. For critical sectors such as nuclear power plants, a cyberattack not only risks damaging the facility but also endangers human lives. In today’s digital world, enormous amounts of data are generated, and the analysis of these data can help ensure effectiveness, including security. In this study, we analyzed the data using a deep learning model for early detection of abnormal behavior. We first examined the Asherah Nuclear Power Plant simulator by initiating three different cyberattacks, each targeting a different system, thereby collecting and analyzing data from the simulator. Second, a Bi-LSTM model was used to detect anomalies in the simulator, which detected it before the plant’s protection system was activated in response to a threat. Finally, we applied explainable AI (XAI) to acquire insight into how distinctive features contribute to the detection of anomalies. XAI provides valuable explanations of model behavior by revealing how specific features influence anomaly detection during attacks. This research proposes an effective anomaly detection technique and interpretability to better understand counter-cyber threats in critical industries, such as nuclear plants.

Light Distribution Models for Tree Growth Simulation

AbstractThe simulation and modelling of tree growth is a complex subject with a long history and an important area of research in both computer graphics and botany. For more than 50 years, new approaches to this topic have been presented frequently, including several aspects to increase realism. To further improve these achievements, we present a compact and robust functional‐structural plant model (FSPM) that is consistent with botanical rules. While we show several extensions to typical approaches, we focus mainly on the distribution of light as a resource in three‐dimensional space. We therefore present four different light distribution models based on ray tracing, space colonization, voxel‐based approaches and bounding volumes. By simulating individual light sources, we were able to create a more specified scene setup for plant simulation than it has been presented in the past. By taking into account such a more accurate distribution of light in the environment, this technique is capable of modelling realistic and diverse tree models.

Albedo Reflection Modeling in Bifacial Photovoltaic Modules

This paper focuses on the analytical modeling of albedo reflection in bifacial photovoltaic modules, with particular emphasis on the backside. First, we critically examine the approaches proposed in the literature, presenting them with a tutorial style and a uniform nomenclature. These approaches are demonstrated to yield physically meaningless results, as they erroneously assume that the ground area shaded by the module acts as a source of reflected irradiance independent of the portion of sky dome visible to such an area. Then we introduce a correction based on the view factor between the shaded area and the sky. The result is a comprehensive and accurate analytical model that also describes the case of suspended panels and can be easily implemented into PV plant simulators.

Data-Driven Remaining Useful Life Estimation

This paper explores the development and application of data-driven prognostic models for estimating the Remaining Useful Life (RUL) of Nuclear Power Plant (NPP) condensers experiencing tube fouling. Due to the unavailability of run-to-failure industry sensor data, we utilized simulated data generated by the Asherah Nuclear Power Plant Simulator (ANS), initially designed by the International Atomic Energy Agency (IAEA) and programmed in Simulink for cyber security simulations. ANS's adaptability allows it to simulate Pressurized Water Reactor (PWR) behaviors given a time series of operating conditions and to introduce degradation modules to mimic fouling effects. Our study compares two primary approaches applied to data generated by ANS: inference-based and direct prediction methods. The selected inference-based approach estimates the health state of the condenser using a pipeline formed by an Auto Associative Kernel Regressor and a Hidden Markov Model (HMM), which subsequently combines the state estimates with its parameters to predict the RUL. The direct prediction method employs a Gradient Boosting Regressor Decision Tree (GBRDT) to map input variables directly to RUL. Our findings demonstrate the efficacy and limitations of each method through the case study, providing valuable insights for the adoption of data-driven RUL estimation techniques in industrial and energy applications.

Online and Offline Fault Detection and Diagnostics in a Nuclear Power Plant Condenser

Nuclear power plants (NPPs) face significant financial pressures due to operational and maintenance costs. This research investigates Fault Detection and Diagnostics (FDD) techniques to optimize maintenance schedules and reduce expenses. The NPP condenser plays a critical role in converting turbine exhaust steam back into water for reuse. Condenser tube fouling, a prevalent fault mode, impedes heat transfer efficiency and can lead to decreased plant efficiency and safety risks. This study proposes an FDD framework that leverages raw signal analyses from temperature and pressure monitoring to detect and diagnose condenser tube fouling in both online and offline settings. The online approach facilitates close-to-real-time predictions, enabling proactive maintenance strategies. Additionally, the framework explores incorporating a condenser’s maintenance history for enhanced diagnostics. We employ a dataset obtained from a simulated nuclear power plant condenser using the Asherah Nuclear Power Plant Simulator (ANS). ANS replicates the operational dynamics of a pressurized water reactor (PWR) type NPP. The proposed methodology utilizes an encoder-decoder (E-D) structured 1DCNN model to analyze the raw signals. The research demonstrates consistent and accurate fault detection and diagnostics for condenser tube fouling in both online and offline scenarios. A high potential for generalization to unseen conditions was observed. However, online detection using small data windows necessitates caution due to potential false alarms around the transition points. Our findings pave the way for further exploration of robust diagnostics by accommodating a wider spectrum of fouling rates within degradation classes using ANS. This combined online and offline FDD approach offers a promising solution for promoting operational safety, efficiency, and cost-effectiveness in NPP condensers.

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Rancang Bangun Miniatur Plant Pusat Simulator Pompa Air Kotor Menggunakan Panel Kontrol Relay Kontaktor

Water pumps play an important role in the management of wastewater, particularly in maintaining environmental cleanliness and health. This research aims to design and create a miniature plant simulator for wastewater pumps using a relay contactor control panel. This miniature plant is designed to simulate the operation of the wastewater pump system automatically, replacing the often inefficient manual methods. The research process begins by determining the main components needed, such as a three-phase induction motor, contactors, thermal overload relays, and related sensors. Subsequently, the design and construction of the simulator and its control panel are carried out. Implementation includes testing the functionality of each component and the integrity of the overall system, including individual component testing and testing under system failure conditions. The research results show that the miniature plant simulator built functions well as expected. The automatic control system using relay contactors has proven effective in operating the water pump efficiently and safely. This miniature plant can be used not only as a learning tool but also as a prototype for developing larger and more complex wastewater pump control systems

Retraction notice: A framework for plant growth analysis and simulation using data analysis techniques

Retraction notice: A framework for plant growth analysis and simulation using data analysis techniques

Analysis of CSP/PV Hybrid Power Plants in High Latitude Regions

This study aims to explore the potential of combined CSP and PV systems in high latitude areas. Several performance metrics are evaluated and compared with standalone CSP or PV plants to identify the benefits and challenges of deploying such hybrid plants. Six different sites have been selected and the ASDELSOL hybrid power plant simulation tool, developed by the Thermodynamics and Renewable Energies Group at the University of Seville, has been used. This simulation tool allows performing dynamic performance simulations of hybrid solar plants under various operation strategies, along with conducting economic evaluations. We have analyzed a hybrid solar plant composed of 50 MW PTC and 75 MW PV, with 10 hours of TES and a 15 MW electric heater to transfer excess PV energy to the TES tanks in a 50 MW base load operation strategy, where the main objective of the PV plant is to cover the self-consumption of the PTC plant. Results obtained show that PV/PTC hybridization reduces LCOE and increases CF compared to standalone PV or PTC plants. These improvements are more pronounced in regions where the contribution of PTC plants is lower. In high latitude regions, an N-S orientation of PTC fields achieves higher production than an E-W orientation. However, a combination of both orientations can optimize their production and performance. Finally, it is concluded that it is necessary to carry out feasibility studies in high latitude locations before dismissing them outright, as it has been observed that, in certain regions, hybrid PV/PTC solar plants can offer an effective alternative.

Design and simulation of a photovoltaic plant for maximum use of the solar resource in the Toluviejo municipality of Colombia

Currently, there are various technologies for constructing photovoltaic plants including monofacial and bifacial photovoltaic panels, centralized and string-type inverters, and fixed mounting structures with light followers, to build electrical energy generation systems. This work reviewed the advantages and disadvantages between these technologies, and the implementation of a photovoltaic plant in Toluviejo, Colombia was proposed. To justify and confirm the design, simulations were carried out with the PVsyst software, resulting in the bifacial panels, centralized inverters, and structures with light followers, the most effective solution, and with the highest production for the proposed photovoltaic power generation plant.

High-pressure core meltdown and hydrogen generation during In-vessel and Ex-vessel phases of VVER-1000 nuclear reactor and effect of heat structures

Following unprecedented Fukushima Daiichi severe accident, which significantly amplify the production of hydrogen and the associated risk of explosions, particularly in accidents like station blackout (SBO) that jeopardize containment integrity and reactor safety, a thorough investigation and comprehension of the phenomenon known as high-pressure melt ejection (HPME) are necessary. This study examines how the SBO accident develops without operator intervention or access to emergency diesel generators through full nuclear power plant simulation, including containment, core, primary, and secondary circuits. Accurate time step selection is crucial for the simulation to produce reliable results for high-pressure melt ejection processes. While the process is faster and easier under Low Pressure Melt Ejection (LPME) conditions, time step tuning can significantly lengthen the (HPME) simulation (approximately one month for each scenario). After thorough examinations and analysis of various simulation findings, three cases were evaluated for the ex-vessel phase, illustrating the most likely scenarios of high-pressure molten material splashing into the cavity. The findings demonstrate that during the in-vessel phase, 578 kg of hydrogen is created. Additionally, during the ex-vessel phase, there is a significant increase in the containment’s temperature and pressure due to the high-temperature hydrogen and significant water and steam leakage. The temperature and pressure inside the containment rise well above the safety criteria due to the leakage of 1529 kg of hydrogen from the cavity and lead to hydrogen explosion due to MELCOR message. The calculated amount of hydrogen gas is within the explosion limit range of 18.3% to 59% of the containment atmospheric pressure volume. Moreover, the height and diameter of the cavity alter due to the direct heating and corrosion of the cavity walls, ultimately compromising the integrity of the plant in the alpha mode. Based on the results obtained, it can be seen that if a large portion of the molten material is deposited on the concrete surface (case III) instead of dispersed in the cavity atmosphere (case I), the rate of hydrogen production and therefore the total amount of hydrogen (and its related energy) produced will be increased and could lead to deflagration to detonation situation.

A simplified method of calculating heat flow through a heat exchanger with a significantly temperature-dependent specific heat of heat transfer fluids

A simplified method of calculating the heat flow through a heat exchanger in which one or both heat-carrying media have a specific heat that varies significantly with temperature is proposed. The proposed method allows for the calculation of the maximum heat flow rate provided that the thermodynamic properties of both heat carriers are known. It is intended for the simplification of heat exchanger modeling at the pre-design power plant simulations, which are used for the assessment of efficiency and the selection of schematic solutions. This is particularly relevant when the parameters of the heat exchangers are not known. The proposed method may prove useful for the simulation of binary cycle geothermal power plants, as well as for the modeling of gas turbine power plants utilizing carbon dioxide as the working medium for their cycles.

Simulation Model of a Steelmaking-Continuous Casting Process Based on Dynamic-Operation Rules.

The steelmaking-continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation's results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the "furnace-machine coordinating" mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the "furnace-machine coordinating" mode on the schedule.

Optimising production efficiency: Managing flexibility in Industry 4.0 systems via simulation

The digital transformation, propelled by Industry 4.0, is essential for businesses to adapt to evolving market demands and sustainability pressures. This study addresses the need for improved plant optimisation by enhancing machinery and workstations through advanced digital modelling. Using the Tecnomatix Plant Simulation environment, we developed a simulation platform to assess the feasibility of a flexible production system. The model, based on a validated case study, demonstrated a 15% improvement in system performance by incorporating varying levels of machine flexibility. This improvement highlights the importance of designing production systems that can adapt to changing requirements efficiently. The study shows how increased flexibility in manufacturing environments can enhance operational efficiency and machine utilisation across different scenarios.

Generative neural network models for synthetic solar irradiance sequences

With the significant increase in the amount of solar energy sources, the variation in the production caused by the stochastic nature of these sources becomes a larger problem for the electrical grid. Having a large percentage of installed units, therefore, requires some type of both short- and long-term planning of energy production. Most planning and forecasting tools require a large amount of solar energy data for the location in question and oftentimes that data is difficult to obtain. In this paper, we propose a method for generating synthetic solar irradiance data and test its applicability on forecasting and long-term planning problems. Synthetic data are generated using various generative adversarial networks and masked autoencoder based models, which are all trained on a dataset of solar irradiance data measured on the territory of the city of Banja Luka in Bosnia and Herzegovina. The synthetic data are evaluated both on different types of short-term forecasting tools and a long-term solar plant simulation tool, and the obtained results are compared with the cases where real data is used. By comparing the results, we have concluded that by using generative models we are able to generate synthetic data which is not only realistic, but applicable for both of these uses.

Experimental investigation of material properties of GFRP pipe for numerical simulation of novel nuclear power plant cooling water intake system

The cooling systems of nuclear power plants are typically made of reinforced concrete water intake structures and special steel pipes, making them large, important structures that necessitate very expensive special productions. In the scope of this research, an innovative design for 4-meter diameter GFRP composite pipes used in nuclear power plant cooling systems was developed using the circular wrapping method. The composite GFRP pipe used in the study has a substantial embedment depth. For this reason, an innovative design was created using unique stiffening rings, and the impact of these rings on the overall behavior was investigated. The nonlinear numerical analysis model of the nuclear power station cooling system, which consists of three pipes with a diameter of 4 m, was then created with ANSYS finite element software using the determined material models, and the results were interpreted. As a result of the study, a composite GFRP pipe design that can be used as a cooling system in significant structures like nuclear power plants has been developed.

A dynamic hybrid model of supercritical once-through boiler-turbine unit including recirculation mode and once-through mode

Supercritical once-through boiler-turbine units play a paramount role in maintaining grid stability owing to their exceptional efficiency and flexibility. Nevertheless, achieving precise modeling of this device under various operational conditions remains a significant challenge. This study conducts modeling and simulation of an actual supercritical once-through coal-fired power plant. The findings signify that the hybrid model proposed herein adeptly encompasses all operational modes of supercritical once-through units, including start-up recirculation mode and once-through mode. To bolster model precision, an optimization algorithm grounded on weighted mean of vectors (INFO) has been employed for parameter identification, while the INFO-XGBoost was utilized to fashion a machine learning model for static parameters. It has been validated that the developed model accurately captures the dynamic characteristics of the unit. Furthermore, the utilization of the INFO-XGboost to model static parameters has been proved to enable the errors in principal outputs of coordinated control system to be within 0.35 % of MAPE across the entire operational process, thus facilitating the design of more refined and intelligent control systems.

Part-load analysis and preliminary annual simulation of a constant inventory supercritical CO2 power plant for waste heat recovery in cement industry

The present work investigates the part-load performance of a MW-scale sCO2 power plant designed as waste heat recovery unit for an existing cement plant located in Czech Republic, in the framework of the H2020 funded project CO2OLHEAT. The study first presents the selected power plant configuration and then focuses on the evaluation of its part-load operation due to variation of flue gas mass flow rate and temperature. The range of flue gas conditions at the outlet of the upstream process is retrieved from a preliminary statistical analysis of historical trends obtained through the cement plant monitoring. The numerical model developed for this study aims at providing realistic results thanks to the adoption of turbomachinery performance maps provided by the turbomachinery manufacturer of the project. Moreover, heat exchangers have been modelled through a discretized approach which has been validated against manufacturer data, while piping inventory and pressure losses have been assessed through a preliminary sizing that considers the actual distances to be covered in the cement plant. Performance decay is estimated for the whole range of flue gas conditions, reporting the most significant power cycle parameters, and identifying the main causes of efficiency loss. The part-load analysis is carried out considering a constant CO2 inventory, in order to reduce the system complexity and capital cost and simplify plant operation. Results show that the operation entails minor variation of the compressors operative points in the whole range of operating conditions of the cement plant, avoiding the risk of anti-surge bypass activation. Moreover, the plant is able to work close to the nominal thermodynamic cycle efficiency (20.5 %–23.0 %) for most of the year and benefits from part-load operation in terms of overall performance. In the last part of the work, a preliminary techno-economic analysis of the plant is also presented to highlight the potential advantages of sCO2 technology for waste heat recovery applications. The results of the part-load performance of the plant are combined with the flue gases data obtained from the preliminary statistical analysis and the cement plant historical monitoring. An annual electricity production equal to 13′909.7 MWh is obtained, corresponding to 6560 equivalent hours and a system capacity factor of 74.9 %. The investment cost of each CO2OLHEAT plant component is estimated by means of cost correlations obtained from literature and the non-discounted payback time is computed as a function of the electricity selling price. The results show that, even considering electricity prices before 2022, the payback time of the CO2OLHEAT plant is estimated to be lower than 8 years, justifying the industrial interest in the proposed technology.

Optimal operational planning of a bio-fuelled cogeneration plant: Integration of sparse nonlinear dynamics identification and deep reinforcement learning

This paper presents a novel data-driven approach for short-term operational planning of a cogeneration plant. The proposed methodology utilizes sparse identification of nonlinear dynamics (SINDy) to extract a dynamic model of heat generation from operational data. This model is then employed to simulate the plant dynamics during the training of a reinforcement learning (RL) agent, enabling online stochastic optimization of the production plan in real-time. The incorporation of SINDy enhances the accuracy of capturing the plant's nonlinear dynamics and significantly improves the computational speed of plant simulations, enabling efficient RL agent training within a reasonable timeframe. The performance of operational planning with the RL agent is compared to that of dynamic programming, a widely used method in the literature. The evaluation metric encompasses energy efficiency, unmet demands, and wasted heat. The comparison investigates the effectiveness of RL and dynamic programming under various scenarios with different qualities of energy demand forecasts. The RL agent exhibits robustness and notably improves the operational planning performance, particularly when faced with uncertain energy demands in the environment. Furthermore, the findings show that the RL agent, trained on a school building data, could successfully perform planning tasks for a hotel building, indicating the transferability of learned planning knowledge across different cogeneration use cases.