Plant Operation Research Articles

Macroplastic and Microparticle Pollution in Beach Sediments from Urias Coastal Lagoon (Northwest Mexico).

This study investigates the occurrence and characteristics of macroplastic and polymer microparticles in the Urias coastal lagoon's beach sediments, in northwest Mexico. Coastal lagoons, productive and vulnerable ecosystems, are impacted significantly by anthropogenic activities, leadings to their pollution by various contaminants, including plastics. Our research involved sampling sediments from four sites within the lagoon that were influenced by different human activities such as fishing, aquaculture, thermoelectric power plant operations, industrial operations, and domestic wastewater discharge. Our methodology included collecting macroplastics and beach sediment samples, followed by laboratory analyses to identify the plastic debris' size, shape, color, and chemical composition. The results indicated a notable presence of macroplastic items (144), predominantly bags, styrofoam, and caps made of polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). The polymer microparticles were mainly fibers, with cotton and polyester as the most common polymers, suggesting a significant contribution from clothing-related waste. The dominant colors of the microparticles were blue and transparent. High densities were observed in areas with slower water exchange. Our findings highlight the urgent need for better waste management practices to mitigate plastic pollution in coastal lagoons, preserving their ecological and economic functions.

Tokamak divertor plasma emulation with machine learning

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 feedforward 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.

The concept of autonomous hydrogen energy complex: Adaptation of large nuclear power units to uneven energy consumption schedules

Evaluation of flexible operation of nuclear power plants (NPP) during load reduction hours shows inefficiency of the process with account for high capital investments in NPPs at relatively low fuel costs. The concept of producing hydrogen fuel at NPPs during the hours of low power demand in the energy grid will be an effective solution to the problem. Generating electricity through utilization of electrolysis hydrogen and oxygen production provides a possibility to significantly extend the flexibility range for power released by the NPP in the energy grid under a constant reactor capacity factor. Utilization of a low-power steam turbine can be a good solution to address the challenges of electricity production from hydrogen and oxygen. This will ensure high reliability of the main NPP steam turbine. In addition, in their previous works based on the experimental data relating the operation of NPPs, the authors showed that a low-power steam turbine and residual energy release from the reactor can be used for NPP auxiliary needs backup in case of a complete blackout. Thus, increased safety of NPPs will be ensured by installing an autonomous hydrogen energy complex. Moreover, this can reduce the risk of an accident when using both the newly developed and existing safety systems. This work presents an analysis of the current state of hydrogen production at NPPs, including the research results and rationale for efficiency of the developed energy complex. The calculations showed that development of NPPs in combination with autonomous hydrogen energy complexes will increase economic attractiveness of newly designed NPPs and will increase the competitiveness of existing nuclear plants on electricity market, will also provide their survival in the energy sector in the long term.

Design and Material Optimization of Oil Plant Piping Structure for Mitigating Erosion Wear

Erosion in piping structures poses a significant challenge for oil industries as the conveyance of solid particles leads to operational malfunctions and structural failures affecting the overall oil plant operation. Conventional oil recovery methods have historically dominated, while in response to the challenges imposed by declining conventional oil production, the global shift towards non-conventional methods necessitates a reevaluation of erosion mitigation strategies due to increased piping infrastructure. Therefore, in this study research has been conducted to reduce erosion and optimize the piping structure. Variables impacting the erosion in piping were investigated from the literature, and simulation cases were made based on the impacted variables. Computational Fluid Dynamics (CFDs) analysis was performed using the Discrete Phase Model (DPM) to determine the erosion wear rate in each simulation case; based on the CFD results, variables with low Turbulent Dissipation Rates (TDRs) and Erosion Rates (ERs) were determined, and the optimized piping structure was designed. As a result, the optimized piping structure showed an 80% reduction in the turbulent dissipation rate and a 99.2% decrease in the erosion wear rate. These findings highlight a substantial improvement in erosion control, ensuring the safety and longevity of piping structures in oil plant operations.

Variable universe fuzzy control of once-through steam generator feedwater

The feedwater control of once-through steam generators (OTSGs) is critical to the safety and stability of nuclear power plants. As traditional PID control methods are difficult to ensure good control of OTSGs under flexible operating conditions, a variable universe fuzzy control (VUFC) method is proposed for OTSG feedwater. The widely-used cascade control structure was first adopted for the OTSG feedwater control system. Then the outer-loop pressure and inner-loop feedwater controllers were designed as fuzzy controllers with their fuzzy universes adjusted adaptively during plant operations. To assess the performance of the VUFC method, dynamic responses of an OTSG employing the fuzzy controllers were compared with those adopting the well-designed PID controllers under typical load change transients. Comparison results illustrate that the VUFC method can provide much better OTSG pressure control with smaller overshoots and shorter settling times, demonstrating the applicability of the VUFC method for OTSGs to provide excellent feedwater control.

Theoretical studies of the movement of mineral fertilizer granules in the mixing chamber of a centrifugal mixing plant

Relevance. The use of mineral fertilizers helps to improve soil fertility, stimulates the growth and development of plants, which is achieved due to the availability of necessary nutrients. In turn, it is important to observe the correct application of fertilizers, which allows you to control the level of nutrients in the soil, preventing both their excess and deficiency. Currently, the main application of fertilizers falls on complex mineral fertilizers, while one of their disadvantages is that they may contain nutrients that will not be in demand by the plant, which can lead to a violation of the ecological balance. One of the solutions to this problem is the use of flour mixtures. When using mixtures, the determining factor is the uniformity of mixing, which is largely determined by the equipment used. In this regard, the urgent task is to develop a mixing equipment that meets the necessary indicators.Methods. During the research, the processes occurring in the mixing chamber of the developed mixing plant were studied using methods of mathematical analysis, graphical and mathematical modeling.Results. As a result of the research, it was found that during the operation of the mixing plant, the mixing quality is determined by the rotation frequency of the conical rotor. At the same time, the granules of mineral fertilizers after leaving the conical rotor, pouring into the collecting funnel, continue their movement along a spiral trajectory. During the movement of the granules along the surface of the rotating cone, as well as when moving through the funnel, their trajectories will intersect with each other, which will lead to their active mixing.

Synergistic effects of a swirl generator and MXene/water nanofluids used in a heat exchanger pipe of a negative CO2 emission gas power plant

The focus on optimizing heat exchangers contributes to improved temperature control mechanisms, ensuring the sustainable operation of innovative power plants working toward negative CO2 emissions. In the realm of oxy-combustion within Negative CO2 Emission Power Plants (nCO2PP), the temperature of combustion products surpasses 3000 ( K ) . Addressing this challenge, the imperative arises to reduce these elevated temperatures to a manageable 1100 ( ° C ) . This critical cooling process is achieved through the injection of water, facilitated by the implementation of heat exchangers. The study delves into the optimization of heat transfer within the heat exchanger pipe, specifically tailored for the context of a nCO2PP. Employing a numerical simulation, the investigation explores the impact of vortex generator geometry, vane angles, single and dual propeller-type swirl generators, and the integration of a novel class of fluid, MXene/water nanofluid. Initially, the study scrutinizes propeller-type geometry at vane angles spanning from 15° to 60°. The enhanced swirl flow associated with lower vane angles leads to improved fluid mixing, fostering more effective heat transfer. Results showed that the 15-degree vane angle, with a wider circumferential coverage, may result in increased wall contact, influencing heat transfer efficiency. Subsequently, at Re = 6000, incremental rates of the Nusselt number ( Nu n − Nu s Nu s %), for θ = 15°, 30°, 45°, and 60° are 175.1, 108.8, 90.7, and 40.3%, respectively. Also, the increment rates of Friction Factor ( f n f s ) for aforementioned vane angle are 38.48, 9.26, 4.08, and 2.42%, respectively. In addition, for ∅ MXene = 0.5 % , the Nusselt number experiences considerable increments of 22.94, 24.17, 24.70, and 24.707% at Reynolds numbers of 6000, 12,000, 18,000, and 24,000, respectively, compared to pure water, emphasizing the potential of MXene to enhance heat transfer efficiency.

WMO Global Energy Resilience Atlas—Climate Risk Indices for Hydropower

The importance of energy transition was underlined at COP28 in Dubai, where governments committed to tripling renewables capacities and doubling the rate of energy efficiency by 2030. However, the power generated by climate-dependent energy sources exhibits greater vulnerability to potential climate changes in the long term. Therefore, climate models play a pivotal role in estimating the effects of climate change on renewables in the context of strategic planning for the development and operation of new renewable power plants. In this context, the World Meteorological Organization (WMO) developed a Global Energy Resilience Atlas aimed at providing insights into the climate change risks for the hydropower sector, the largest renewable electricity source for most countries, generating over 4300 TWh globally. This study focuses on defining four Hydro Climate Risk indices (HCRIs) using historical and climate projection precipitation data for three climate scenarios. The final product is a freely available and interactive tool. The developed methodology and tool address how climate changes have historically affected hydropower generation and how they will impact the future at national scales. The final product also addresses the needs of policymakers at national, regional, and global levels in crafting long-term planning for a more secure energy sector, accelerating the energy transition to more sustainable and reliable energies.

Development of a Mathematical Model of Operation Reliability of Mine Hoisting Plants

The work analyzes the performance assurance of mine hoisting machines, including the problem of the quality of performance of the functions. The quality of functioning allows evaluation of a set of properties of the process of lifting loads, designed to meet the given requirements in accordance with the purpose and evaluated performance indicators. In this case, the quality of the function depends not only on the elements that worked properly or failed during system functioning but also on the moments involving certain changes in the states of the system. The considered system of power supply of mine hoisting installations is rather complex with respect to reliability. The proposed approach allows this rather complex system to lead in terms of the form of a serial connection of elements, allowing for determining the influence of the functioning of its subsystems and electrical equipment on the technological process of cargo lifting in a coal mine. The presented mathematical concept of increasing the reliability and failure-free operation of mine hoisting plants with the help of the developed mathematical model of the mine hoisting plant allowed studying the reliability indicators of the hoisting plant operation and reserving the equipment most effectively to increase reliability. The determination of coupling coefficients in this study made it possible to analyze the impact of the reliability of electrical equipment and power supply systems on the operation of technological machines to improve the reliability of mining equipment and the efficiency of technical systems of mining equipment.

Safety Management and Accident-Control Strategy for a Commercial-Scale Plant for Supercritical Water Oxidation of Sludge

Supercritical water oxidation is a promising technology for decomposition of industrial wastewater and sludge. However, the system is operated under high temperature and pressure (usually higher than 500 °C and 25 MPa). Corrosion of component materials and salt deposition may lead to leakage or even the burst of the pressure vessel in the SCWO system, resulting in a high level of worry about the safe operation of the system. In this paper, the safety management and accident- control strategies are introduced according to a commercial-scale SCWO plant in China. The safety management strategy refers to the special design and operation strategies of some facilities. Different types of potential accidents are analyzed and the coping strategies for accidents of different levels of severity are described in detail. The strategies are valuable for the safe operation of commercial SCWO plants and other plants operated in high temperature and high pressure.

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.

Research of VDT scheme for porous media thermal-hydraulics analysis of plate-type fuel assembly

Thermal-hydraulics analysis of nuclear reactor core is crucial for the safe operation of nuclear power plants. Porous media models in computational fluid dynamics (CFD) code are commonly used to simulate flow and heat transfer in nuclear reactor cores because they simplify geometry and reduce computational cost. However, their low spatial resolution limits detailed analysis of internal flow and temperature fields. This research develops a Virtual Domain Technology (VDT) scheme to improve the resolution of porous media models without increasing mesh density. VDT sets localized resistance coefficients to create virtual solid and fluid domains within the porous core representation. The flow and thermal effects of these virtual domains mimic real structural effects. VDT was verified on a cylinder flow case and showed excellent agreement with CFD for velocity and pressure fields. Application to a plate-type research reactor core demonstrated VDT’s ability to capture detailed temperature profiles and flow traces consistent with CFD, using only 6% as many mesh cells. By improving spatial resolution, VDT enhances porous media-based modeling of reactor cores to provide more accurate thermal-hydraulics analysis, while maintaining computational efficiency. This will aid optimization and safety analysis for advanced reactor designs.

SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors

In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. We test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.

Optimal modes of operation and product cost allocation in sugarcane steam cogeneration plants

Sugar and ethanol production from sugar cane is one of the most competitive sectors of Brazil’s economy. The bagasse generated during the production process is used as fuel in cogeneration plants that provide thermal and electrical energy to the process. In the last decades, many sugar cane factories have produced a surplus of electricity that may be sold to the grid as a new product. This paper applies energy billing optimization and thermoeconomic analysis to a sugarcane steam cogeneration plant to determine the optimal operating mode of the plant, unveils the cost formation process of its internal products (refinery heat, process heat, and consumed electricity), and examines how the results are affected by: (i) the demand for the plant’s energy services, (ii) the availability of bagasse, and (iii) the selling price of surplus electricity. The thermoeconomic analysis involves a detailed study of the cost formation process, which is achieved through the decomposition of the steam cycle of the cogeneration plant into subcycles. Three main subcycles, in addition to the deaeration cycle and other auxiliary subcycles, have been identified: the cogeneration cycle generating work in the high-pressure turbine and refinery heat (subcycle one), the cogeneration cycle generating work in the high- and medium-pressure turbines and process heat (subcycle two), and the condensing cycle that generates only work in the high-, medium-, and low-pressure turbines (subcycle three). These subcycles make up the productive structure of the steam cogeneration plant and explain how water/steam goes through energy conversion processes from the bagasse energy to the heat and electricity produced. Both the optimization model and the thermoeconomic analysis serve as valuable tools for planning in response to potential changes in bagasse and electricity market prices, as well as fluctuating product demand conditions. In the base case, combining optimization with thermo-economic analysis, the unit monetary cost of the final products has been determined: heat for refinery (8.85 R$/MWh), electricity sold (183.60 R$/MWh), internally consumed electricity (41.51 R$/MWh), and process heat (8.85 R$/MWh).

PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM

Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.

FMEA-Based Logistic Regression Model for the Evaluation of Photovoltaic Power Plant Risk

The purpose of this research is to identify the primary operational risks associated with photovoltaic power plants and develop effective risk management strategies to optimize the operation of existing plants and mitigate risks for future plants that will be constructed as part of the new renewable energy (EBT) transition agenda until 2030. The integration of Failure Mode and Effect Method Analysis (FMEA) with logistic regression provides the formation of a risk treatment ranking that management should prioritize. Risk assessment relies on the expertise and experience of professionals in performing their responsibilities associated with photovoltaic power plants. The research findings have identified 10 potential risks associated with improving photovoltaic power plants operations to prevent failure or damage to the system. These risks are categorized into five stages of the operation process: planning and procurement, installation, operation, and maintenance. Risk rankings and mitigation are generated to prioritize actions aimed at limiting the occurrence of failure/damage and low-capacity factors in photovoltaic power plants as recommendations for the management.

Low-disturbance land remediation using vertical groundwater circulation well technology: The first commercial deployment in an operational chemical plant

Soil and groundwater contamination by organic pollutants from chemical plants presents significant risks to both environmental and human health. We report a significant field trial where a chemical plant in operation showed soil and groundwater pollution, as verified by sampling and laboratory tests. While many remediation methods are effective, they often require the temporary shutdown of plant operations to install necessary equipment. This paper introduces a novel combination of low-disturbance contaminant remediation technologies, including groundwater circulation well (GCW), pump and treat (P&T), and in-situ chemical oxidation (ISCO) technologies, that can be applied on the premises of an active plant without halting production. The groundwater with dissolved contaminants is removed through P&T and GCW, while GCW enhances ISCO that focus on eliminating the remaining hard-to-pump contaminants. Results show: (1) after two years of remediation effort, the contaminant levels in soil and groundwater were significantly reduced; (2) the average concentration reduction rate of four contaminants, including 1,2-dichloroethane, methylbenzene, ethylbenzene, and M&P-xylene, exceeds 98 %; (3) the presented remediation strategy results in the improvement of remediation efficiency. Specifically, the concentration of 1,2-dichloroethane in observation wells dropped from 40,550.7 μg/L to 44.6 μg/L. This study offers a first-of-its-kind commercial deployment of a GCW-based remediation strategy in an active plant setting. Moreover, the combined remediation approach presented here can serve as a model for designing contaminant remediation projects that require minimal operational disruption.

Sensitivity to heat and mass transfer correlations of the model for an evaporative condenser

Evaporative condensers have established themselves in refrigeration plants for food process and storage owing to the combination of air and water cooling, with an overall reduction in energy (pumping) and fluid (water) consumption. Given the steep rise of electricity prices, the need to develop conduction strategies at the plant level which are less energy-intensive has driven the need for models to carry out simulations of the new strategies without actually impacting normal plant operations. This paper describes the development of a distributed-parameter model of an evaporative condenser with fill-pack, based on Merkel’s pioneering works on evaporative cooling. Several correlations are available for the heat and mass transfer coefficients, but none that satisfies the operating conditions of the case at hand, yet they are the most critical set of parameters for the model, therefore a sensitivity study has been carried out, to ascertain which combinations of transport coefficients out of a total of 288 would yield a total cooling power closest to the value declared by the manufacturer for a real-life evaporative condenser under rating conditions. This work demonstrates the applicability of the correlations examined, and also twenty of the examined combinations allow the model to reach a good agreement with the reference data (within ±5%). Further, the distributed-parameter structure of the model allows highlighting the reasons for good or bad performance of the correlations by determining the spatial distribution in the cooling coil and fill-pack of quantities such as moist air enthalpy and water temperature. The model and methodologies are a little cost-intensive in terms of computational time and can be applied to the study and design of any similar piece of equipment.

MODELING THE DISTRIBUTION OF EMERGENCY RELEASE PRODUCTS AT A NUCLEAR POWER PLANT UNIT

Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.

Synchronizing production of precast elements with on-site erection

Offsite construction technologies are developed to reduce project cost and duration. To make the most of the potential offered by prefabrication the planner should consider the whole supply chain. A failure to coordinate the off-site production with on-site erection is a source of waste (waiting time of the construction crews or redundant handling activities on-site). Most of the research to date focused on optimizing operations of a prefabrication plant assuming a deterministic schedule of demand for its products. The purpose of this paper is to develop a mathematical model for integrated scheduling of offsite and on-site operations. Its solution is a schedule that minimizes the downtime of both the prefabrication plant and the on-site erection crews. In accordance with the Just-in-Time concept, the prefabrication schedule is set in a way to reduce the stocks of finished products, thus reducing the storage area and cost of funds tied up in inventory. The schedule’s robustness against the disturbance in the production and erection workflows is assumed to be assured allocating time buffers. The advantage of the proposed method is the ease of collecting the input: instead of detailed cost records, estimates of unit cost of lost time can be used.