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.

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 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.

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.

The existing alkaline electrolysis hydrogen production technology primarily focuses on performance testing of electrolyzers and optimization of flow fields in electrolysis cells, and little attention is paid to overall description of the hydrogen production system as well as the mechanism modeling and simulation of key equipment. To solve this problem, using gPROMS process simulation software and referencing chemical process simulation methods, a distributed parameter model based on mechanism analysis was established for a 200 m³/h (standard condition) alkaline water electrolysis hydrogen production system. The key equipment of the system was finely modeled and simulated. By comparing the simulation results with experimental data, the results show that the simulated values of the main performance parameters of the system have good consistency with the measured data. The calculated average error is less than 5%, which verifies the effectiveness of the model. The established model can describe and predict the changes in system parameters, providing methods and support for subsequent system design, optimization, and control.

Accurate modeling of main-steam temperature is the foundation for studying its control strategy. To address the contradiction between mechanism completeness and model practicality of the conventional modeling methods, a hybrid modeling method for main steam temperature integrating mechanism model and system identification was proposed. A mechanistic model of main steam temperature was established based on the lumped parameter method. The model includes the effects of flue gas heat transfer, steam flow rate and desuperheating water flow rate. According to the closed-loop operation data of a thermal power plant boiler, the model parameters were identified using differential evolution algorithm. Compared with the boiler design values, the identification results show an increase in thermal resistance of ash layer and a decrease in convective heat transfer coefficient of flue gas, which conforms to physical laws. The model was then verified using operational data from different time periods. The results indicate that the mean absolute error between the calculated results and the operational data is less than 1 ℃, which proves the accuracy of the model. By utilizing the model, the dynamic characteristics of the main-steam temperature were further analyzed, and several improved control strategies were proposed, such as increasing the feedforward signals of the coal feeding rate and the attemperator inlet steam temperature, and adopting a load-based fuzzy controller. The simulation results show that the deviation of the main-steam temperature is reduced, which proves the established model has guidance effect on optimizing the main-steam temperature control system.

Biomass fuel is a renewable and clean energy source that can replace fossil fuels and reduce carbon emissions. However, during storage and transportation, microbial metabolism in biomass can cause self-heating, which, through the “chimney effect”, accelerates air circulation and promotes aerobic reactions, potentially leading to thermal runaway and fires. Given that biomass typically has rod-like and flake-like shapes, conventional porous media gas flow resistance models are poorly adapted for assessing these processes. In this study, a testing platform for the gas flow resistance characteristics of biomass porous media was established. Gas flow resistance tests were conducted on rice straws and soybean shells at different bulk densities. The parameters of the conventional Ergun model and the modified Ergun model were optimized. The results show that the resistance experienced by gas flowing through biomass porous media significantly increases with bulk density and gas velocity, exhibiting a pronounced upward parabolic relationship. As the load applied to the biomass increases from 50 kg/m² to 2 800 kg/m², its bulk density can increase by approximately two times. Ignoring the changes in bulk density and porosity caused by stacking height in biomass self-heating numerical simulations can lead to prediction deviations. Both the Ergun model and the modified Ergun model can be used to evaluate the gas flow resistance of typical flake-like or rod-like biomass. The modified Ergun model, with fewer parameters and direct calculation based on bulk density, significantly enhances engineering applicability.

Steam ejector technology integrated into combined heat and power systems enables effective thermal-electric decoupling and deep load following, with ejector performance directly influencing overall efficiency and operational stability. A one-dimensional thermodynamic design model for high-temperature and high-pressure steam ejectors is developed by incorporating the development characteristics of the compressible mixing layer. The concept of compressible mixing layer thickness is introduced based on the entrainment mechanism to determine the radial dimensions of the ejector. Numerical simulations are performed to evaluate ejector performance and flow field characteristics, which guide the optimization of axial dimensions. The optimal structural parameters are identified as a nozzle-to-mixing chamber distance of 6 mm, a mixing chamber length of 42 mm, and a diffuser angle of 4.4°. An experimental system is constructed to validate the proposed design method, and the results show an average relative error of 6.6% between the predicted and measured entrainment ratios, demonstrating the model’s accuracy. The results provide a theoretical foundation for the structural design of high-temperature and high-pressure steam ejectors and hold significant potential for practical engineering applications.