ArchiveTo further improve the cycling performance of the cold storage packed bed for liquid air energy storage systems, a multi-cycle study was conducted on the two-dimensional continuous solid-phase model of the cold storage packed bed using the finite element simulation method. Performance improvement methods for filling phase change materials at the top of a packed bed in two different ways were proposed and analyzed. The influence of the thermal physical properties and filling thickness of different phase change materials on the key parameters of the composite cold storage packed beds was discussed. The results show that both the composite cold storage packed bed and the solid-phase cold storage packed bed have an increase in round-trip efficiency with the cycle times, and tend to a quasi-steady state in the 10th cycle. As the intermediate phase transition temperature of the phase change material increases, the rate of phase transition occurring in the cycle gradually decreases. The composite cold storage packed bed filled with phase change materials with higher intermediate phase change temperature, higher volumetric heat capacity, and higher latent heat of phase change exhibits better performance. Increasing the filling thickness of phase change materials can help further improve the performance of composite cold storage packed beds. The composite cold storage packed bed with the best comprehensive performance shows an increase of 15.2% in cold storage density and 0.22 percentage points in round-trip efficiency compared to the solid phase cold storage packed bed, while the cold storage efficiency only decreases by 1.52 percentage points. The research can provide theoretical guidance for the design of cold storage packed bed systems for large capacity liquid air energy storage.
Molten salt thermal energy storage technology can improve the flexibility of thermal power units. The molten salt evaporator, a core component of the system, utilizes the thermal energy of molten salt to convert boiler feedwater into superheated steam. However, the unique thermophysical properties of molten salt and the complex structure of the heat exchanger render existing heat transfer correlations inadequate for accurately predicting its thermal performance. Experimental studies on molten salt heat exchangers are costly, while existing numerical simulations cannot achieve coupled heat transfer calculations between the single-phase molten salt side and the phase-change working fluid side. In this study, with given feedwater inlet parameters, a numerical model for the molten salt side heat transfer is established by assuming an initial enthalpy distribution along the flow path. The heat flux distribution obtained from the simulation is then used to calculate the enthalpy variation of the working fluid, which is iteratively compared and corrected against the initially assumed values. Through multiple iterations, an accurate computation of the heat transfer process in the molten salt steam generator is achieved, enabling a detailed investigation of its operational characteristics. The results indicate that at an operating pressure of 2 MPa, during the transition from subcooled water to complete vaporization, the vaporization rate gradually increases. The heat flux peaks at 86 295.12 W/m2 upon complete vaporization, then decreases rapidly and eventually stabilizes around 5 000 W/m2. Significant temperature non-uniformity is observed across the flow cross-section of the molten salt, with a maximum thermal deviation of 216%. This temperature non-uniformity is alleviated as the inlet molten salt temperature decreases.
In response to the growing environmental concerns and the depletion of fossil fuels, the global installed capacity of renewable energy sources such as wind and solar power continues to rise rapidly. However, the intermittent and variable nature of these energy sources poses significant challenges to power grid stability and the effective integration of clean energy. Solid oxide cells offer a promising pathway toward resolving these issues, owing to their ability to operate reversibly, high energy conversion efficiency, and compatibility with a wide range of fuels. By flexibly switching between solid oxide fuel cell mode and solid oxide electrolysis cell mode, they enable efficient electrical energy storage and chemical fuel production, demonstrating considerable potential for large-scale renewable energy storage and grid-balancing applications. This study conducts multivariable parameter optimization for a reversible solid oxide cell system, aiming to investigate the effect of multivariable parameters on the performance of reversible solid oxide cell systems.
A stack-level model is established and integrated with auxiliary components, such as fans, heat exchangers, and separators. The Aspen Plus software is used to construct a full system model. Using mixed-integer linear programming with an objective function that minimizes total energy demand, along with a pinch analysis technique, the thermal integration of the system is optimized for a given current density. This approach determines the optimal operating temperature under different current conditions, calculates the required air flow rate to satisfy stack temperature limits under optimized heat recovery, and evaluates the resulting auxiliary power consumption, ultimately leading to the computation of optimal system efficiency.
The results show that in the power generation mode, the system efficiency increases at first and then decreases with the increase of current, and the maximum value is 53.5%. In the endothermic state of hydrogen production mode, the stack efficiency decreases with the increase of current, and the maximum value is 119.5%. While the system efficiency increases with the current, the maximum value reaches 79.2%. In the exothermic state of hydrogen production mode, the stack efficiency increases with the current, with the maximum value of 95.4%, and the system efficiency is stable at about 79.3%.
This research clarifies how operating current and other key parameters influence the performance of reversible solid oxide cell systems. The findings offer theoretical insights and practical optimization strategies to enhance the efficiency and operational flexibility of such systems in real-world energy storage applications, supporting the broader integration of intermittent renewable energy sources into the power grid.
Compressed air energy storage (CAES) plays a critical role in stabilizing power systems with high penetration of renewable sources by mitigating intermittency and supporting the achievement of “dual-carbon” objectives. The thermo-mechanical response and damage evolution of an underground lined CAES cavern under repeated operational cycles are investigated based on a 30 000 m³ demonstration project in Zhangbei County.
A coupled thermo-mechanical numerical model is developed using COMSOL Multiphysics, incorporating non-ideal thermodynamics of high-pressure air, a fracture energy-based damage model for concrete, the reinforcing effect of steel bars, and the mechanical behavior of the excavation damaged zone (EDZ).
Simulations are conducted under a typical operational cycle comprising 8 h charging, 4 h pressure maintenance, 4 h discharging, and 8 h maintenance. The results reveal significant temperature fluctuations inside the cavern (−30.68~70.18 ℃). Concrete cracking is found to initiate at a low internal pressure (~1.9 MPa) and evolve into circumferentially spaced cracks. Steel reinforcement effectively carries tensile stress at crack locations, demonstrating effective collaboration with concrete. The surrounding rock is shown to bear approximately 97.7% of the internal pressure, with its stiffness significantly affecting lining stress and cavern convergence. Increased EDZ stiffness is observed to improve load transfer and stability. Plastic zones are found to develop predominantly near the cavern crown and bottom during charging, exhibiting irreversible deformation.
The synergistic behavior of the steel-concrete-rock composite system is elucidated, providing a theoretical basis for the design and safety assessment of CAES caverns.
The utilization of industrial solid waste for thermal energy storage represents an innovative approach to address environmental challenges while advancing energy storage technologies. This study comprehensively examines the potential of industrial solid wastes, including coal fly ash, red mud, sewage sludge, gypsum, metallurgical slag, and waste concrete, as composite thermal energy storage materials. The discussion encompasses the material properties, preparation methods, and applications of industrial solid wastes in composite heat storage systems. The study highlights their capacity for high-temperature stability, enhanced thermal conductivity, and phase change material integration, offering significant energy density improvements. Moreover, the review identifies challenges such as material heterogeneity and long-term thermal cycling performance. Strategies for industrial solid waste modification, encapsulation of phase change materials, and innovative composite designs are analyzed to enhance their applicability in sustainable thermal energy storage systems.
Co-combustion of coal and biomass coupled with CCS technology has negative carbon emission potential, which is one of the important paths to realize the low-carbon transformation of coal power. This study aims to further explore the carbon reduction potential of this technology in enterprise-scale application.
A carbon accounting system is established at the enterprise level. Taking a 350 MW coal and biomass co-combustion plant coupled with CCS technology as the research object, the optimization of the carbon accounting model for the combustion process, desulfurization process, and indirect emission is carried out based on the whole process of “combustion end - CCS end”. The carbon flow analysis of multi-source emissions is carried out to quantitatively evaluate the impacts of biomass type, blending ratio and carbon capture efficiency on carbon emissions.
The results show that straw blending has a slightly better emission reduction effect than wood blending. Increasing the blending ratio and carbon capture efficiency will increase the indirect carbon emissions of the CCS system. Under the conditions of less than 20% blending ratio and 80%~100% carbon capture efficiency, increasing the blending ratio of biomass can get more net emission reduction benefits than increasing the carbon capture efficiency. There is a significant parameter coupling effect among biomass type, blending ratio and carbon capture efficiency.
The results of the study provide data support and decision-making basis for power generation enterprises to formulate low-carbon transition strategies.
Biomass resources in China are widely distributed and highly abundant, holding great potential for substituting traditional fossil fuels and promoting the achievement of carbon peak and carbon neutrality goals. During storage and transportation, biomass is prone to self-heating. When the accumulated heat raises the internal temperature of biomass to a certain level, chemical reactions will gradually accelerate, leading to biomass self-ignition. In traditional biomass self-ignition studies, thermogravimetric/calorimetric experiments typically employ powdered samples. However, this approach significantly deviates from the actual storage conditions of biomass. Moreover, existing biomass reaction kinetics models exhibit poor adaptability below 250 ℃. To address these issues, a testing platform for the low-temperature pyrolysis and oxidation characteristics of biomass was established. The thermal degradation behavior of rice straw and soybean shell samples with different particle sizes (original large particles, 2.0 mm particles, and 0.2 mm particles) was investigated under various oxygen concentrations. Two kinetic models, namely the pyrolysis-independent component oxidation model and the pyrolysis-lumped oxidation model, were developed and optimized. These models accurately predicted the pyrolysis and oxidation behavior of biomass in the low-temperature range. The results indicated that the reaction rate increased significantly with temperature. However, as biomass consumption progressed, the promoting effect of temperature on the reaction rate gradually diminished. Increasing the oxygen concentration also accelerated the reaction rate, but its impact was weaker than that of temperature elevation. Under the same temperature and oxygen concentration conditions, the 2.0 mm particle samples exhibited the highest reaction rate, while the original samples had the lowest rate, with the 0.2 mm particle samples falling in between. Experiments on biomass samples with original particle sizes and the development of targeted kinetic models are more representative of real-world conditions. The pyrolysis-lumped oxidation model effectively predicted the mass loss behavior of rice straw and soybean shell samples with different particle sizes under various oxygen concentrations as the temperature increased, demonstrating its applicability for predicting low-temperature pyrolysis and oxidation reactions of biomass.
Modern industrial production emits vast quantities of CO2, and to mitigate the greenhouse effect caused by CO2, geological sequestration of CO2 is imperative. Consequently, the utilization of salt caverns for CO2 capture and storage is being considered.
This study uses the salt rock formation in Daning County, Shanxi Province as a potential reservoir. Based on geological survey data, a geological model is established for the Daning County salt rock CO2 storage pilot area. Under fluid-solid coupling conditions, CO2 leakage extent is represented by CO2 pore pressure as a sealing indicator, while vertical displacement at the top of the salt cavern reservoir and vertical stress serve as stability indicators. Long-term sealing integrity and stability studies are conducted for the salt cavern reservoir under varying CO2 storage pressures and different pillar spacing conditions.
As the gas storage pressure increases, the leakage range of the gas expands, the vertical displacement of the reservoir rock increases, and the range of the plastic zone in the rock decreases. At the final state with storage pressures of 17, 23, 27 and 33 MPa, the leakage ranges of the gas are 47, 67, 73 and 84 m, the vertical displacement at the top of the cavity is –11.5, 12.9, 28.7 and 52.2 mm, and the range of the rock mass plastic zone is 22, 11.8, 8 and 4 m, respectively. As the spacing between mine pillars increases, the vertical displacement and vertical stress of the surrounding rock decrease. The spacing has little effect on the gas leakage range and plastic zone. When the spacing between mine pillars is 1.0, 1.5, 2.0 and 3.0 times the original spacing, the gas leakage range remains between 60 m and 63 m, and the vertical displacement at the cavity top is 31.7, 29.1, 28.2 and 27.3 mm, respectively. The plastic zone extent within the gas storage reservoir is broadly consistent, ranging between 9 m and 10 m, respectively.
Excessively high pressure in a storage reservoir compromises its sealing integrity, while excessively low pressure undermines its stability. A greater spacing between pillars within the reservoir enhances both sealing integrity and stability, but the impact is relatively minor. This study provides a theoretical foundation for CO2 storage in the Daning Salt Cavern in Shanxi Province.
Building a clean and low-carbon new power system is a key vehicle for achieving the strategic goals of carbon peaking and carbon neutrality. Developing clean, low-carbon, high-efficient, and flexible new thermal power generation technologies has become a major strategic requirement for building a new energy system. The semi-closed supercritical carbon dioxide (S-CO2) Brayton cycle directly heats the composite working fluid through the combustion of the fuel and the pure oxygen. Not only can it enhance the power generation efficiency of the system, but it also enables carbon capture at the same time. This study aims to investigate the unclear heat transfer and mass transfer characteristics of the CO2/H2O composite working fluid during the cooling and condensation processes in the heat exchanger of the semi-closed S-CO2 Brayton cycle.
A three-dimensional numerical simulation model for the cooling, condensation and flow heat transfer of the CO2/H2O composite working fluid was established. This study systematically investigated the influence pattern of the mass flow rate (2×10–4~4×10–4 kg/s), the heat flux (–9~–14 kW/m2), and the mole fraction of the inlet water vapor (3.3%~20.0%) on the distribution of the liquid film of the condensate, the surface heat transfer coefficient, and the mass transfer rate.
The results indicate firstly that the average surface heat transfer coefficient increases with increasing mass flow rate. However, at different mass flow rates, the variation pattern of the average surface heat transfer coefficient differs as the heat flux increases. Moreover, the axial mass transfer rate exhibits a trend of increasing first and then decreasing along the flow direction of the composite working fluid. Furthermore, under low mass flow rate and high heat flux conditions, the condensate accumulates at the bottom of the circular pipe, while under high mass flow rate and low heat flux conditions, the condensate forms a ring-shaped distribution along the inner wall surface of the circular pipe. Additionally, when the mole fraction of the inlet water vapor increases from 3.3% to 20%, the average surface heat transfer coefficient increases by 20.22%. Besides, the peak value of the mass transfer rate shifts toward the inlet direction.
The results can provide theoretical support for the design of the heat exchangers in the semi-closed S-CO2 Brayton cycle, and then contribute to improving the efficiency of the system and the performance of the carbon capture.
Against the backdrop of increasingly severe global energy and environmental problems, hydrogen fuel is garnering significant attention as a pivotal component of the future sustainable energy landscape by all countries in the world. The advantages of hydrogen are manifold, including its diverse production sources and crucially, the potential to enable carbon-free combustion with low nitrogen oxide emissions when utilized in advanced combustion systems such as micro-mix combustors. The stability and pollutant performance of a micro-mix combustor depend critically on the fuel-air mixing characteristics and the consequent combustion behavior. Given this critical dependence, it is necessary to study the mixing characteristics of micro-mix combustors.
This study is based on the self-designed single-stage micro-mixing combustor, and employs numerical simulation to investigate the mixing and flow characteristics of the single-stage micro-mix combustor, exploring its working principle and studying the effects of different offset distances, mixing distances, air hole shapes and momentum flux ratios on the flow field structure and mixing characteristics at low power.
The numerical simulation results show that increasing both the offset distance and mixing distance contributes to improved mixing uniformity. With the increase of the offset distance, the influence of the equivalence ratio on the mixing effect becomes more significant: increasing the equivalence ratio accelerates the merger of internal vortices, while the contraction of external vortices leads to an increase in vorticity magnitude. As the mixing distance increases to a certain distance, the uniformity index growth gradually decelerates, while hydrogen diffusion becomes severe, making it difficult to ensure complete mixing is confined within the mixing zone. When fuel jet depth is low, air holes with small aspect ratios and small upper-lower area ratios achieve better mixing performance. At a low equivalent ratio, the internal vortex of triangular air holes exerts a strong entrainment effect on the fuel. Furthermore, the mixing uniformity is affected by the combined action of the momentum flux ratio and the vorticity magnitude. The momentum flux ratio in the small-scale jet in cross flow has a more significant and pronounced effect on mixing than the vorticity magnitude. When the air hole is smaller under the same momentum flux ratio, the mixing effect is better. With the increase of momentum flux ratio, the fuel distribution shows a trend changing from semicircle to water droplet shape and then to horseshoe shape.
The research provides valuable references for the optimized design and application of micro-mix combustion technology in micro gas turbines.
In pulverized coal-fired furnaces, the heat transfer process is dominated by radiation. With the increase in furnace size, the radiative contribution of pulverized coal particles becomes increasingly prominent. Notably, the burnout ratio of pulverized coal particles exerts a critical influence on their radiative properties. However, most existing numerical simulation studies tend to overlook this effect and set the particle emissivity and scattering coefficient as constants. To address this issue, this study takes a 600 MW supercritical opposed-fired boiler as the research object and employs computational fluid dynamics (CFD) to analyze the effects of three types of radiative property models on the prediction results of radiative heat transfer. These models include the constant model (emissivity and scattering coefficient remain constant), the linear model (radiative properties vary linearly with particle burnout ratio), and the Planck mean coefficient model (based on Planck mean emissivity and scattering coefficient). The results indicate that compared with the scenario where both gas and particle radiation are considered, neglecting particle radiation leads to an overestimation of the furnace peak temperature by approximately 300 K. When particle radiation is taken into account, compared with the Planck mean coefficient model, the constant model underestimates the heat transfer rate of the spiral membrane wall in the burner region by about 9% and the peak value of the average wall heat flux by 12.5%, while the linear model results in an overestimation of the peak value. These findings can provide a reference for the reasonable selection of radiative property models in the numerical simulation of pulverized coal combustion.
Under the background of China’s “dual-carbon” strategy, the efficient and stable operation of coal-fired power station boilers is crucial for peak shaving of the power grid, and temperature field monitoring is one of the keys to ensuring the safe and efficient operation of boilers. Addressing challenges such as decreased combustion stability, severe load fluctuations, and temperature field reconstruction under deep peak shaving conditions, this study focuses on the precise detection of cross-sectional temperature fields in opposed-fired boilers. A dual-band furnace temperature field reconstruction system that integrates the inverse Monte Carlo method with the Tikhonov regularization algorithm is proposed.
This system innovatively incorporates wireless detectors, breaking through the limitations of conventional wired devices that are difficult to route in complex boiler spaces, and providing hardware support for real-time monitoring under deep peak shaving conditions. On-site furnace tests conducted under multi-load conditions (25%, 33%, and 66% load) of a 630 MW opposed-fired boiler revealed that there were significant differences in temperature fields between deep peak shaving and conventional operation.
At low loads in the main combustion zone, the high-temperature center deviates from the geometric center (towards the left and front walls), while at high loads, it tends to be evenly distributed. The high-temperature zone in the burnout zone is concentrated near the walls of the front and rear sections. At low loads, there is a significant difference in the area of the high-temperature zones on the front and rear walls, indicating poor combustion uniformity. Meanwhile, extinction coefficient analysis further indicates that the burnout zone (0.91~0.94) is significantly higher than the main combustion zone (0.26~0.51), verifying the differences in flame radiation characteristics between deep peak shaving and conventional operation.
Through collaborative analysis of algorithm optimization, hardware innovation, and the structural characteristics of opposed-fired boilers, a high-precision temperature field monitoring system has been constructed, providing key data support and engineering pathways for combustion state diagnosis, operation optimization, and safety regulation under deep peak shaving conditions. This has important practical value for the deep peak shaving operation of coal-fired boilers.
Under low-temperature conditions in winter, the fin-tube bundles of air-cooled radiators are prone to freezing. Exploring the variation patterns of the critical anti-freezing ambient temperature and critical anti-freezing circulating water flow rate of the indirect air-cooling system at different wind speeds and directions is of great guiding significance for ensuring the safe and stable operation of power plants. Taking a 2×350 MW indirect air-cooled unit as the research object, this study adopts the numerical simulation method, combining with the louver opening adjustment strategy, to investigate the critical anti-freezing characteristics of the indirect air-cooling system under different operating conditions. Through the analysis and calculation of the variation patterns of the flow and heat transfer performance of the indirect air-cooling system under different ambient meteorological conditions in winter, the variation laws of the critical anti-freezing ambient temperature and critical anti-freezing flow rate of the indirect air-cooling system at different wind speeds, wind directions and louver openings are revealed. The results show that an increase in ambient wind speed leads to a decrease in the critical anti-freezing ambient temperature and an increase in the critical anti-freezing flow rate. Reducing the louver opening can lower the critical anti-freezing ambient temperature, while the critical anti-freezing flow rate shows a trend of decreasing at first and then increasing. However, under the condition of high wind speed in the 270° wind direction, the critical anti-freezing flow rate decreases continuously with the reduction of louver opening. The research conclusions can provide operational guidance for the safe and stable operation of indirect air-cooled units in low-temperature winter environments.
Against the problem that the conventional timed and quantitative soot blowing mode is prone to cause local over-blowing and under-blowing of the waterwall, studies are carried out by relying on effective monitoring methods. As slag deposition on waterwall is a key factor affecting the safe and economic operation of thermal power boilers, long-term unresolved local over-blowing or under-blowing will not only accelerate the corrosion and wear of the waterwall, but also increase energy consumption and operational costs of power plants. Therefore, the core goal of this research is to establish a precise soot blowing algorithm to replace the conventional timed and quantitative soot blowing mode and realize adaptive and efficient soot blowing control.
A new type of waterwall slagging monitoring sensor was used to monitor the in-furnace waterwall surface temperature, which can collect real-time, continuous and high-precision temperature data to lay a reliable foundation for subsequent model construction. Three machine learning methods, including eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and random forest regression (RFR), were compared to construct theoretical in-furnace waterwall surface temperature models under clean waterwall conditions, and a calculation method for waterwall slagging factor was proposed. On this basis, a precise soot blowing algorithm was established. To verify the optimization effect of this algorithm, a 3-month practical application test was carried out in a 1 030 MW thermal power unit, and the operation data was compared with the original timed and quantitative soot blowing mode.
The research shows that the theoretical in-furnace waterwall surface temperature model established by the random forest regression method performed the best, with an R2 of 0.92, δMSE of 73.77, and δMAPE of 1.16%. The implementation of the precise soot blowing algorithm is significantly better than the original quantitative soot blowing mode, with a significant reduction in soot blowing frequency and no deterioration of the waterwall slagging state, and the local maximum temperature of the waterwall is controlled within the safe range, avoiding the risk of tube explosion caused by overheating.
This algorithm reduces the consumption of soot blowing steam while ensuring the safety of boiler operation, which directly reduces the daily operation cost of the power plant. Moreover, the reduction of soot blowing frequency also reduces the influence of high-temperature steam on the waterwall, effectively extending the service life of the waterwall and reducing the maintenance cost of the boiler. It can be popularized and applied in thermal power plants of different capacities, and has extremely high application value.
The oily dust on photovoltaic (PV) modules significantly reduces power generation efficiency, but conventional cleaning agents suffer from poor cleaning performance, cause environmental pollution, and bring component corrosion risks. This study analyzed the dust composition via XRF, XRD, and ignition methods, revealing that organic matters with a mass fraction of 17.52% cause high adhesiveness. An eco-friendly cleaning agent was developed by optimizing the formulation via a four-factor three-level orthogonal experiment using Class A eco-friendly components. Its degradation and corrosion properties were verified through the continuous activated sludge method and immersion experiments. The results showed 10 g/m² oily dust could reduce the PV module power by 30.28%, while the cleaning agent achieved a cleaning efficiency of 99.84% at 30-fold dilution. The cleaning wastewater with a COD of 3 000 mg/L achieved a biodegradability of over 90.41%. Field application on oil plant rooftops restored 79.4% power efficiency and raised surface temperature by 5.6 ℃. Costing only 0.5 yuan per square meter and causing no corrosion to PV modules, the proposed cleaning agent outperforms commercial alternatives. It enables efficient and eco-friendly cleaning, ensuring stable operation of PV power stations and improving power generation efficiency.
To meet the high requirements of selective catalytic reduction (SCR) systems in coal-fired power plants for accurate and low-latency prediction of nitrogen oxides (NOx) mass concentrations, this study designs and proposes a soft sensing and deployment framework that balances high accuracy and real-time performance. Using more than 110 000 sets of high-dimensional operational data from a 660 MW coal-fired unit, a systematic comparison of deep learning (DL) and XGBoost models was conducted on a unified platform. Time series cross-validation combined with grid search was employed to optimize hyperparameters, and model performance was comprehensively evaluated in terms of predictive accuracy, computational efficiency, and interpretability via local interpretable model-agnostic explanations. On this basis, an “edge-embedded” collaborative deployment strategy was proposed, in which the DL model is deployed on edge servers to deliver high-accuracy predictions, while the XGBoost model is embedded into the distributed control system (DCS) to ensure real-time responsiveness. The results show that the DL model outperforms XGBoost in dynamic response and predictive accuracy, achieving root mean square errors approximately 10% lower than that of the XGBoost, and maintaining stability under highly fluctuating conditions. Variable importance analysis highlights flue gas oxygen content, burner wall temperature, and total air volume as the dominant factors affecting NOx formation. The proposed collaborative architecture can theoretically achieve millisecond-level inference and provide offline fault tolerance, offering a practical pathway for intelligent ammonia injection control and combustion optimization.
To address poor control performance of indirect air cooling systems under external disturbances, where conventional PID control fails to meet requirements of large time-delay, strong coupling, and nonlinear systems while generalized predictive control (GPC) offers superior performance but suffers from parameter tuning difficulties, an improved PSO-based GPC parameter tuning method is proposed. Transfer function and environmental wind speed disturbance models are established with a GPC controller using circulating water outlet temperature as the controlled variable. Targeting limitations of conventional PSO prone to local optima and low convergence accuracy, a DE-VPPSO hybrid algorithm combining differential evolution and velocity pausing mechanisms is designed, employing dual-population collaborative search to enhance search capability and convergence precision. The algorithm simultaneously optimizes four key GPC parameters: prediction horizon, control horizon, control weighting, and softening factor. Simulation results demonstrate that under step disturbances, wind speed disturbances, parameter mismatches, noise interference, and comprehensive operating conditions, the proposed method exhibits excellent control performance and robustness, effectively enhancing system performance.
In view of the limitations of excessive reliance on subjective experience in reactor fault risk assessment, a comprehensive evaluation method based on fault tree and fuzzy Bayesian network is proposed. A fault tree model covering three fault manifestations, abnormal vibration, abnormal temperature rise, and abnormal oil chromatography, is built to clarify the causal logic of 22 risk factors and then mapped into a Bayesian network. Trapezoidal fuzzy numbers are used to handle linguistic uncertainty in expert judgment, and expert weights are combined to obtain basic event probabilities. Forward reasoning yields the overall reactor fault probability, while backward reasoning and fault importance indices are simultaneously involved to identify key fault causes. Case analysis shows that the method accurately assesses fault risk, identifies fault causes, and provides a basis for operation and maintenance decisions. The proposed comprehensive evaluation method based on fault tree and fuzzy Bayesian network effectively reduces subjective dependence in reactor fault risk assessment, offering high practical value and broad application prospects.
The 800H alloy is a critical high-temperature material used for the main steam header of high-temperature gas-cooled reactor steam generators and is prone to creep damage and failure under prolonged high-temperature and high-pressure service conditions. In this study, tensile and creep tests were systematically performed on 800H alloy under various temperatures and stress levels to obtain its mechanical properties and detailed creep curves. The results indicate that the alloy exhibits an anomalous creep behavior in the early stage of creep, characterized by an initial decrease followed by an increase in creep rate, which is more pronounced under lower stress conditions. The true steady-state creep rate, accounting for this anomalous behavior, was determined using the Monkman-Grant relationship in combination with stress exponent analysis. Further investigation shows that this anomalous creep behavior significantly reduces the creep rate and influences the subsequent creep process. Quantitative analysis reveals that the creep rate at the anomalous point increases with increasing temperature and stress, while the duration of the anomalous stage increases with applied stress. These findings provide valuable insights into the creep behavior and strengthening mechanisms of the 800H alloy.
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