Plant Operation Research Articles

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Creating a renewable energy-powered energy system: Extreme scenarios and novel solutions for large-scale renewable power integration

The large-scale integration of renewable power sources, such as wind and solar, is pivotal in achieving climate change mitigation goals and reducing dependency on fossil fuels within energy systems. This manuscript explores the integration of substantial wind and solar power capacities into Luxembourg's energy framework, addressing challenges like variability, intermittency, and curtailment. It delves into scenarios that include power-to-heat (P2H) and vertical farming (VF) technologies to improve system flexibility and promote the incorporation of renewable energy. Utilising a mixed-integer linear programming (MILP) algorithm, an energy balance model with hourly resolution optimises the system. This algorithm guides the operation of individual plants, ensuring optimal performance based on a variety of criteria. Notable findings underscore the significant influence of an appropriate mix of renewable power resources, indicating a potential increase in renewable power consumption by up to 50% within the system. Furthermore, curtailment management solutions are shown to enhance renewable power integration by as much as 30%, highlighting the importance of strategic, system-wide approaches. Additionally, vertical farming emerges as a promising avenue for significantly augmenting the utilisation of surplus solar power, potentially by up to 100%. While the integration of large-scale renewable power is demonstrably viable, it necessitates a meticulously crafted strategy that considers technological innovations and strategic planning.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Assessment of raw and produced water quality at Al-Doura water treatment plant

Abstract The population growth rate has increased rapidly in recent years so there is an obligation to evaluate the productivity of produced water (PW) by investigating the operation of the Water treatment plant (WTP). WTP’s primary goal is to remove and destroy harmful organisms from contaminated water to make it safe for human consumption. The quality of PW depends on the performance of WTP. This study aims to assess the removal efficiency of Al-Doura WTP in Baghdad City/Iraq and ensure that the drinking water quality meets the drinking water standards. Twenty-two water quality parameters are analysed over four years (from January 2020 to December 2023). The samples of raw water are collected from the Tigris River at the intake of the WTP, while the PW samples are collected at the final stage where the water undergoes treatment before being distributed to the consumers through the network. The measured parameters are Temperature, Turbidity, Alkalinity as CaCO3, Total Hardness as CaCO3, Calcium as Ca, Chloride as Cl, Magnesium as Mg, pH, Colour, Electrical Conductivity EC, Free chlorine (Residual chlorine), Sulphate as SO4, Total dissolved solids, Suspended solids, Iron as Fe, Fluoride as F, Aluminium as AL, Nitrite as NO2, Nitrate as NO3, Ammonia as NH3, Silica as Sio2, and Orthophosphate as PO4. In this study, special focus is concerned with some of these water quality parameters, temperature, pH, electrical conductivity EC, and Turbidity. The analysis is sub-categorized into winter and summer seasons. The SPSS software is used to analyse the data. The results indicate that there is no significant difference between the temperature of raw water and that of the produced water, and the pH values indicate slight alkalinity for raw water in both seasons. Electrical conductivity Ec values for both raw and produced water are found always greater in winter than in summer. The turbidity is found to satisfy the permissible limits for both the Iraqi and World Health Organisation standards (Turbidity < 5 NTU). The drinking water produced from the Al-Doura water treatment plant is found to be within Iraqi and WHO standards, The high difference in turbidity concentration before and after treatment and the removal efficiency excellent value of close to 98% indicates that the plant is highly effective and reflects the effective process of Al-Doura WTP in removing particulate matter and improving water clarity. The Pearson, correlation analysis results indicate a strong (positive, and negative) correlation in the relationship among some parameters reported in this article.

Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network

Activated sludge models are increasingly being adopted to guide the operation of wastewater treatment plants. Chemical oxygen demand (COD) is an indispensable input for such models. To ensure that the activated sludge mathematical model can adapt to various water quality conditions and minimize prediction errors, it is essential to predict the parameters of the COD components in real-time based on the actual influent COD concentrations. However, conventional methods of determining the components' contributions are too intricate and time-consuming to be really useful. In this study, the chemical oxygen demand in the actual waste water treatment plant was disassembled and analyzed. The research involved determining the proportions of each COD component, assessing the reliability of the measurement parameters, and examining potential factors affecting measurement accuracy, including weather conditions, pipeline conditions, and residents' habits. Then, a backpropagation neural network was developed which can deliver real-time predictions for five important contributors to COD in real time. In addition, using the receiver operating characteristics curve and prediction accuracy to evaluate the performance of the prediction model. For all five components, which SS, XS, SI, XA, and XH, the prediction accuracy of model was more than 80 %. The maximum deviation values of these parameters fall within the range of the actual detected values, suggesting that the model's predictions align well with real-world observations, and demonstrated prediction performance adequate for practical application in wastewater treatment. This article can provide research basis for the engineering application of activated sludge model and help for the intelligent upgrading of waste water treatment plants.

Effect of long-term storage of cattle manure on its energy potential and biodegradability

Abstract In recent years in Russia, due to the complicated geopolitical situation in some border areas, biogas plants have been considered not only as waste utilization facilities, but also as reserve energy sources, which are safer than traditional - nuclear power plants. In this context, the development of an algorithm for the flexible operation of a biogas plant is particularly relevant, which gives rise to the need to study the stability of the system under the influence of different unfavourable factors. In this work, the influence of long-term storage of cattle manure on its energy potential and biodegradability is studied. The specific methane yield in the test variant with manure stored for 10 months before anaerobic fermentation was 1.41±0.55 ml/g oDM, which is 6.23 times lower than in the test variant with fresh manure; the methane content of manure after long-term storage is 9.66 times lower and the degree of decomposition of its organic matter is 2.72 times lower compared to similar indicators of manure processed without preliminary storage. However, the specific biogas yield from long-term stored manure is 1.66 times higher than the control, which indicates intensive formation of other gases. Thus, long-term storage has a negative impact on the energy potential of cattle manure; if it is necessary to process it in a biogas plant, it is advisable to combine it with more energy-intensive raw materials.

Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge.

The main problem in the operation of nuclear power plants (NPPs) is the scale formation of mineral impurities in an open recirculating system (ORS). The discharge of water from an ORS into natural water bodies can alter the chemical equilibrium of wastewater components, necessitating continuous monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts (DIS) in water within an ORS during water treatment, using the Rivne Nuclear Power Plant (RNPP) as a case study. Moreover, the analysis impact of their discharge with return water in the Styr River. The DIS concentration has a significant impact on the efficiency of the system and the environmental of an ORS power plant. Altogether, each of the DIS components was analysed separately using the standard measurement methods, statistical methods of data processing and correlation analysis. In addition, the annual discharge of the DIS components was calculated, and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the impact of the formation of DIS and the variations in their concentration levels upon the discharge of wastewater into a natural water body were examined.

Optimal scheduling of virtual power plants considering integrated demand response

Abstract In contemporary power system management, leveraging multilevel demand response strategies plays a pivotal role in balancing energy supply with demand, thereby enhancing system efficiency and flexibility. This paper introduces an innovative optimization model for virtual power plant operations that anticipates the scheduling needs several days in advance. It incorporates a comprehensive approach to demand response on the consumer side, highlighting two main types: price-based and substitution-based responses. Furthermore, the model integrates the functionalities of energy storage and conversion devices. This integration aims to facilitate effective load management, ensuring an optimized load distribution while minimizing the overall system costs.

Biofilm Reactors: A Potential Alternative to Current Treatment Technology for Wastewater in Kathmandu Valley

Kathmandu Valley faces challenges managing its growing wastewater volume, compounded by the complex composition of unregulated industrial discharges. Releasing untreated wastewater poses a severe risk to public health and the environment. Existing wastewater treatment infrastructure, primarily reliant on conventional activated sludge processes (ASP) struggles to meet growing demands. These systems require substantial land area, are sensitive to influent variations, produce a high volume of sludge, and incur high operational and maintenance costs. Biofilms, naturally occurring assemblages of microorganisms adherent to surfaces and embedded within an extracellular polymeric matrix (EPS), present a compelling alternative for wastewater treatment due to their diverse pollutant removal capabilities. When implemented as biofilm reactors, they offer distinct advantages, including tolerance to fluctuations in wastewater composition, minimal land requirements, and reduced energy consumption. Notably, microbes residing within a biofilm are capable of biodegradation of persistent materials such as pharmaceuticals, metals, and plastics. Globally, biofilmmediated wastewater treatment has been implemented successfully, while a knowledge gap remains for the treatment of Kathmandu's wastewater. This review critically assesses biological wastewater treatment methods, providing insight into: a) suspended growth process with their configuration, application, and limitations, b) wastewater treatment infrastructures of Kathmandu Valley, and c) biofilm process with their configuration, factors influencing biofilm development and performance, application of specific microbial strains for enhanced treatment efficiency, and factors to be considered during implementation. Furthermore, the paper recommends: a) an extensive study of laboratory-scale biofilm reactors evaluating and optimizing their performance for local integration and b) investigating the role of diverse microbial communities to further enhance the treatment plant's operation. By prioritizing research and development towards biofilm technology, Kathmandu Valley can achieve efficient and environmentally friendly wastewater management.

A comparison on the critical success factors of MSW and RDF power plant development in Thailand: Using an interpretive structural modelling and cross-impact matrix multiplication applied to classification analysis

Waste-to-energy (WtE) power plants, supplied mainly with municipal solid waste (MSW) and refuse-derived fuel (RDF), which convert waste into electricity, have emerged as a solution to Thailand's waste management problems. This study focused on identifying and studying the critical success factors (CSFs) that influence the success of MSW and RDF power plants in Thailand. This study employed interpretive structural Modelling and cross-impact matrix multiplication applied to a classification analysis to evaluate the impact of these CSFs on the development of WtE projects. The results showed that, for MSW, most CSFs were related to energy and waste management policies, followed by waste quality for electricity generation. In addition, strong financial resources and appropriate power plant locations are important for MSW management success. Conversely, for RDF, most CSFs were sufficient waste quality for electricity generation and performed well according to licensing conditions. In this study, high-level CSFs indicated that these factors were crucial for MSW and RDF development. CSFs differ based on specific technologies and regulations. However, sufficient waste quality (heating value and moisture content) is a common CSF in the MSW and RDF technologies. This study provides valuable insights into the CSFs that affect the development of WtE. Understanding and addressing these CSFs is essential for the development and operation of WtE power plants in Thailand and other countries with similar conditions. Thus, policy-makers and other stakeholders can make informed decisions to ensure the success of WtE projects.

Increasing variable renewables in coal-based energy systems under high electrification in the transport and heating sectors: The case of Kosovo

The increasing penetration of variable renewables poses major challenges in energy systems with limited flexibility. However, there are many solutions to support variable renewable integration when utilizing potential synergies among the different energy sectors. This research examines the technical and environmental effects of electrifying the transport and heating sectors in a coal-based energy system with limited flexibility, aiming to support the integration of variable renewable energy. Scenario analysis is employed to evaluate the use of heat pumps with thermal storage (power-to-heat) for individual heating solutions, electric vehicles, and Vehicle-to-Grid in the transport sector. These measures are evaluated as the primary means for enhancing the energy system flexibility to accommodate a larger share of variable renewables. Additionally, a dynamic analysis was conducted on thermal power plant operational capacities and efficiencies under varying electrification rates and renewable energy shares. The analysis is developed in the EnergyPLAN model using Kosovo energy system as a case study. The results confirm that the electrification of the heating and transport sectors positively impacts the integration of variable renewable energy by reducing critical excess electricity production and CO2 emissions, while power-to-heat and Vehicle-to-Grid facilitate synergies among different energy sectors. However, scenarios with high shares of variable renewable energy present challenges for the nominal operation of thermal power plants, potentially leading to lower operational efficiencies and slightly higher CO2 emissions compared to scenarios where thermal power plant part-load efficiency remains unchanged.

Forecasting performance and analysis of operations of the ore processing plant of the Kola Mining and Metallurgical Company using simple dependencies

Forecasting performance and analysis of operations of the ore processing plant of the Kola Mining and Metallurgical Company using simple dependencies

Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.

The application of time series deep learning model to the fast prediction of parameters in the MSLB accident

The Main Steam Line Break Accident (MSLB) threatens the safe operation of nuclear power plants. The transient safety parameters of the Passive Containment Cooling System (PCCS) in the MSLB accident are predicted by the time series deep learning model based on LSTM or RNN. In the dataset preprocessing, the linear normalization and two of feature label segmentation methods are used. The neural network models and the other models, namely the GBRT, RF and SVR, are used to construct the multi-parameter time series deep learning models to predict the transient safety parameters. The performance of the different models and the impact of two segmentation methods on models are compared, and it is obtained that the prediction accuracy of LSTM is higher than that of other models.

Developing deep learning surrogate models for digital twins in mineral processing – A case study on data-driven multivariate multistep forecasting

The escalating demand for environmental and social sustainability underscores the critical need for large industries such as mining and metallurgy to function optimally. Achieving optimal operation of a mineral processing plant requires timely access to information and rapid implementation of corrective actions. Essential information, such as current and future production, plays a pivotal role in enhancing process efficiency by facilitating adjustments in key aspects, including feed, control and operating variables. This study delves into the application of deep learning algorithms to forecast the gold concentrate grade of a flotation process half an hour in advance, a valuable tool for short-term decision-making, e.g., with regard to optimising the operation and process control. Multivariate time series data from a real gold processing plant was acquired through the process instrumentation and control system and augmented with simulated data at 5-minute intervals over 236 days. Surrogate models based on LSTM, GRU and CNN architectures were developed and analysed for predicting the gold grade one to six steps ahead. Evaluation of the testing dataset indicates that the CNN exhibits superior performance, achieving an average error reduction of 36 %, 45 % and 35 % for the performance metrics RMSE, MAE and MAPE respectively compared to the baseline model. These findings reveal the high potential of deep learning surrogate methods for accurately forecasting process outputs, enabling effective data-driven process control and optimisation strategies.

Renewable-battery hybrid power plants in congested electricity markets: Implications for plant configuration

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. This paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4 h is 29.4 % for solar and 26.8 % for wind, assuming low battery degradation costs and storage sized to 100 % of the plant's nameplate generation capacity. Increasing storage duration beyond 4 h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90 % capacity credit with 4 h of storage, while wind hybrids require 8 h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

Carbon capture and co-pollutants in a networked power system

Abstract We evaluate how the availability of Carbon Capture (CC) in a networked electricity system affects the emissions of both carbon and of co-pollutants, under a range of plausible technical, economic, and policy scenarios about CC technology, the pace of renewable deployment, the structure of the power grid, and climate policy. We employ a Power Flow model of a three-node, mixed-source network in which fossil fuel power plants may invest in CC via retrofit. Our stylized model retains some of the complexities of a real power system while allowing for a detailed analysis of the impact of power plant operations and transmission constraints. We find that, in a networked system, the availability of CC may lead some generation to move from Natural Gas to Coal, thus leading to a significant increase in co-pollutants. This is of particular concern during the mid-transition, a period when both carbon and non-carbon electrical generation is active. The introduction of CC can lead to an increase in co-pollution even as the energy system transitions toward renewable energy and, surprisingly, co-pollution outcomes can be worse under a stronger decarbonization policy. This insight is important and timely in light of recent rules incentivizing the use of CC. Systems in the early stages of the energy transition may experience an increase in co-pollution if the co-pollutant dynamics are not considered in the first steps of CC policy design.

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.