Plant Operation Research Articles

A Study on One-Time Inspection for Aging Management of Operating Nuclear Power Plants

One-time inspection is used to confirm the effectiveness of nuclear power plant aging management programs such as water chemistry, fuel oil chemistry, and selective leaching program that is designed to prevent or minimize aging degradation during the plant operation. This paper represents one-time inspection experience of selected components and susceptible locations within the scope of water chemistry and fuel oil chemistry aging management programs. Prior to performing one-time inspection, sample sizes of components to be inspected were determined based on combination of materials, environment and aging effect. Examination techniques and relevant operating experience were analysed to obtain technical basis for further implementation of one-time inspection aging management programs. Existing correlation curves between sample size and total population number were reviewed to prepare further inspections for entire operating plants and ensure safety level of components subject to one-time inspection program.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedfoward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

How Cs-137 dispersions due to atmospheric deposition to East Vietnam Sea: a hypothetical level 7 incident from the Fangchenggang Nuclear Power Plant (China)

Only about 50 km from the Quang Ninh coastal area in Vietnam, the Fangchenggang Nuclear Power Plant (China) operation causes excellent concerns for the environment, especially the marine environment, if an incident occurs. Based on topographic and hydrometeorological data and assumptions about radiation release conditions when an incident occurs, the Delft3D-Part modeling system was set up to assess/forecast the effect of Cs-137 radioactive activity on the source of air deposition in the water environment of Vietnam. The results showed that the sources of the drop from the air significantly affected and caused a faster impact than direct radioactive sources after the incident. Under the influence of marine circulation, wind radioactive material after falling into the sea will quickly disperse and may affect the entire East Vietnam Sea after 3–6 months. The area with high radioactivity is concentrated mainly in the Tonkin Gulf and along the coast of Vietnam. Especially in the Gulf of Tonkin, the radioactivity can reach 300–1,200 Bq/m3 after the 5-day incident. The amount of radiation then gradually decreases to almost less than 100 Bq/m3 after one year and below 20 Bq/m3 after two years. According to depth, the amount of radioactive Cs-137 tends to shift between layers from the surface to the bottom: higher at first in the upper layers, then gradually decreasing and increasing in the lower layers. Radioactivity significantly reduced after two years of the incident but still exceeded QCVN 10: 2008/BTNMT.

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

River systems under peaked stress

Abstract The change in the global energy production mix towards variable renewable energy (VRE) sources requires efficient utilization of regulated rivers to optimise hydropower operations meet the needs of changing energy market. However, the flexible operation of hydropower plants causes non-natural, sub-daily fluctuating flows in the receiving water bodies, often referred to as “hydropeaking”. Drastic changes in sub-daily flow regimes undermine attempts to improve river system health. Environmental decision makers, including permitting authorities and river basin managers facing the intense and increasing pressure on river environments, should consider ecosystem services and biodiversity issues more thoroughly. The need for research innovations in hydropeaking operation design to fulfill both the water and energy security responsibilities of hydropower is highlighted. Our paper outlines optimized hydropeaking design as a future research direction to help researchers, managers, and decision-makers prioritize actions that could enable better integration of river science and energy system planning. The goal of this is to find a balanced hydropower operation strategy.

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite’s effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios.

A time-reliability correlation for estimating the diagnosis error probability of a nuclear power plant with up-to-date Human–Machine interfaces

Despite being developed more than four decades ago based on expert judgment, the THERP time-reliability correlation (TRC) remains widely employed for calculating diagnosis human error probabilities in human reliability analysis for nuclear power plant risk assessment. However, with numerous advancements in nuclear plant equipment and operations, as well as the emergence of plants featuring advanced interfaces, there's a growing need to validate the THERP TRC. The objective of this study is to establish a TRC for the diagnosis human error probability in a modern reference nuclear power plant equipped with up-to-date human–machine interfaces and compare it with the median of the THERP TRC. To achieve this goal, we devised a method to gather event diagnosis times from a simulator and developed procedures to derive diagnosis TRCs using this data. Our findings indicate that while the median of the THERP TRC offers a conservative diagnosis human error probability for up to 25 min, it becomes overly optimistic beyond this threshold.

Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems

Abstract The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise.

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

AbstractThe reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Legal regulation of nuclear and radiation safety during operation of nuclear power plants

Legal regulation of nuclear and radiation safety during operation of nuclear power plants

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.

Resource potential of dust entrainment of asphalt-concrete plants

In the modern construction industry, the problem of finding materials with increased stability that would provide minimal environmental impact with optimal economic efficiency is urgent. Considering global trends in the field of ecology and sustainable development, attention is focused on recycling and reuse of industrial waste. Of particular interest is the pulverized material formed during the operation of asphalt concrete plants. In this paper, the potential of integrating this type of waste as an alternative construction resource is studied. This article focuses on the urgent need to adapt the principles of sustainability in construction practice through the integration of materials with minimal environmental footprint. The key aspect of the study is the concept of recycling of pulverized waste from asphalt concrete production, which can help optimize the resource potential and reduce the environmental burden on the ecosystem. The study examines various dust compositions obtained from asphalt plants of different operating principles. The morphology of particles and the bitumen capacity parameter were investigated. It was found that by replacing up to 50% of mineral powder with carryover dust, there can be a significant environmental and economic impact for the road construction industry.

Порівняльна оцінка собівартостей відпуску електроенергії з різних джерел в базовому та регулювальному режимах

The paper analyses the role and the technical and economic indicators of different electricity generation sources in the transition to "carbon-free" energy. A simplified method is proposed for estimating the minimally justified price of selling electricity from thermal power plants (TPPs), renewable energy sources (RES) and new maneuverable natural gas capacities is proposed. It is shown that the price of electricity from RES and new maneuverable capacity has a decisive influence on the amount and fixed period of return on investment in construction, as well as on the capacity utilization factor (CUF), which depends on the mode of operation of the power plants. Comparative estimates of the CUF of different electricity generation sources are presented. It has been shown that the cost of electricity from RES, new peak and maneuverable capacity is significantly higher than the cost of electricity from thermal power plants during the payback period. It is proved that at present the reserve for the reduction of the power shortage and the load regulation is available only in the thermal generation, and the conditions for the possibility of its effective use are the maintenance of the proper technical condition of the pulverized coal boiler units and the sufficient fuel base. To ensure acceptable electricity prices, RES and new control capacity should be phased in, using existing TPPs and CHPs to balance both the weighted average price and the regulation of power system load modes. Keywords: thermal power plants, coal, natural gas, gas turbine, steam-gas, gas-piston power plants, renewable energy sources, cost of electricity supply.

Physics-informed deep learning and linear programming for efficient optimization of combined cycle power plants

Efficient operation of combined cycle power plants (CCPP) involves solving mixed-integer nonlinear optimization problems, constrained by thermodynamic differential equations, thermal constraints, and temporal dependencies between variables. The optimization problem aims to maximize profit for day-ahead operation by minimizing short-term and long-term costs, and accounting for volatility in ambient conditions and energy price. In operational terms, the challenge lies in developing an efficient and robust optimization algorithm capable of determining optimal values for thousands of temporal variables. It must also have the capacity to evaluate a multitude of scenarios encompassing various weather and market conditions. To address this challenge, this paper proposes a hybrid optimizer that utilizes deep learning models trained on historical operational data of a real CCPP, along with a surrogate physics-driven piecewise-linear model of CCPP, to determine the optimal values of thousands of control setpoints, with strong temporal co-dependencies, within a two-minute runtime required by industry constraints. A multi-resolution architecture is proposed to efficiently implement this optimizer. The algorithm sequentially solves the problem in coarse and fine resolutions to accelerate convergence. Final results are checked for feasibility using pre-trained deep learning models, ensuring accuracy and validity. The study shows that the proposed model provides comparable results to the exact solution while reducing computational costs, up to 95%. This research has practical implications for power plant system operators, as well as traders and dispatchers, enabling them to make optimal decisions for day-ahead contracting purposes and CCPP operation in a reasonable time, ultimately leading to more efficient and cost-effective power plant management. Furthermore, the methodology introduced in this work could potentially be extended to other industrial applications that require large-scale, complex optimizations, where the precise governing principles of a process can be approximated by piecewise-linear functions.

Investigating Salt Precipitation in Continuous Supercritical Water Gasification of Biomass

The formation of solid deposits in the process of supercritical water gasification (SCWG) is one of the main problems hindering the commercial application of the process. Seven experiments were conducted with the grass Reed Canary Grass with different preheating temperatures, but all ended early due to the formation of solid deposits (maximum operation of 3.8 h). The position of solid deposits in the lab plant changed with the variation in the temperature profile. Since the formation of solid deposits consisting of salts, coke, and corrosion products is a severe issue that needs to be resolved in order to enable long-time operation, inner temperature measurements were conducted to determine the temperature range that corresponds with the zone of solid formation. The temperature range was found to be 400 to 440 °C. Wherever this temperature was first reached solid deposits occurred in the system that led to blockage of the flow. Additional to the influence of the temperature, the influence of the flow direction (up-flow or down-flow) on the operation of the continuous SCWG plant was examined. If salts are not separated from the system sufficiently, up-flow reactors should be avoided because they amplify the accumulation of solid deposits leading to a shortened operation time. The heating concept coupled with the salt separation needs to be redesigned in order to separate the salts before entering the gasification reactors. Outside of the determined temperature zone no deposition was visible. Thus, even though the gasification efficiency was low it could be shown that the operation was limited to the deposits forming in the heating section and not by incomplete gasification in the reactor where T > 600 °C.

R&D in advanced technology fuels (ATFs) in Spain

Accident Tolerant Fuels, also known as Advanced Technology Fuels (ATFs), have the potential to improve the safety and economic performance of commercial nuclear reactors as well as to provide additional operational flexibility for responding to variations in system energy demand.The design, fabrication and in-pile behaviour of currently operating and innovative nuclear fuels to enable a reliable and safe operation of nuclear power plants is a priority for the nuclear industry. The Spanish nuclear professionals (public and private companies, academia, and research centres) have achieved significant advances in this area. Both irradiation programs in commercial reactors, along with experimental and modelling activities, were focused into comprehending and modelling different ATF concepts to understand and predict the behaviour under normal and off-normal conditions.The International Atomic Energy Agency (IAEA) sub-program “Nuclear Power Reactor Fuel Engineering” is strongly involved in the development and validation of computer codes for ATF fuel design and the valuable Spanish contribution to this sub-program is described in this article.The future Spanish R&D in this field is expected to be focused on further improvements on ATF product development and performance knowledge.

Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction

Timely and accurate three-dimensional (3-D) NOx concentration distribution prediction is essential for achieving low-emission and efficient operation in power plants. This study proposed a theory-guided data-driven prediction method for the 3-D NOx concentration distribution prediction. Firstly, the method created a foundational dataset by fusing numerical simulation data from the computational fluid dynamics (CFD) with operational data from the distributed control system (DCS). Then, the data was classified into three load condition categories, and the center operating conditions for each category were computed separately. Subsequently, the K-means algorithm was employed to extract representative data to address the computational challenges associated with big data. Finally, a Theory-Guided Deep Neural Network model (TG-DNN) was established leveraging the principle of carbon element mass conservation and deep neural network. Experimental results demonstrate that the method effectively monitors the 3-D NOx concentration distribution, potentially facilitating efficient production processes.

Modeling and nonlinear predictive control of solar thermal systems in district heating

This study presents data-driven modeling and nonlinear model predictive control of solar thermal plants in district heating, for the purpose of operation optimization. The study considers the efficient operation of a solar thermal plant in Hillerød, Denmark. A dynamic model is estimated as a system of stochastic differential equations using grey-box modeling and real-world data. The presented nonlinear model predictive controller design is based on repeated trajectory linearization and the dynamic model. Several objective designs are considered, e.g., maximizing energy or temperature. The study provides simulations for analyzing the model fitness and controller performances. The model is shown to fit the daytime operation of the plant. The designed controllers are shown to improve efficiency, increasing the transported energy up to 28%.

Thermal efficiency and performance analysis of 50 MW concentrated solar power plant

This study evaluates the operational efficiency and performance of the Shagaya 50 MW Concentrated Solar Power (CSP) plant in Kuwait that has been operational since February 2019. Utilizing Parabolic Trough technology, the plant incorporates a large Solar Field (SF) comprising 8 platforms with total of 206 solar-collector loops. Thermal energy captured by the SF is utilized in a Low Temperature Rankine Cycle to generate electricity via water-steam cycle and heat exchangers, with a nominal turbine capacity of 50 MW. Supported by a thermal energy storage (TES) system capable of storing 1200 MWht energy in molten salt. Through a comprehensive assessment spanning 41 months, real-time operational data is analysed to evaluate the CSP-TES system's efficacy within Kuwait's climatic conditions. Various performance parameters are examined, providing insights into CSP technology's practical implications and operational strategies. Driven by the annual difference in downtime and TES availability, comparative analysis of the plant's annual electricity production over the three years reveals notable variations: 74.99 GWh in 2019, 160.62 GWh in 2020, and 140.04 GWh in 2021, corresponding to capacity factors of 19 %, 40.75 %, and 35.53 %, respectively. The findings underscore the strong correlation between the SF and TES system in determining overall plant efficiency. The study also highlights the impact of high wind speeds on plant operation, triggering shutdowns on 9 days during the assessment period. Additionally, soiling losses in the SF are assessed through daily reflectivity measurements, guiding cleaning efforts using semi-automated armed trucks. Seasonal peaks in cleanliness factor coincide with local spring season, underlining the detrimental effect of reflectivity losses on plant output due to higher dust mass densities. A design flaw causing frequent breakdowns of receiver tubes under high winds is identified and addressed.