The hydrophobic resin-based solid amine adsorbent was prepared by modifying porous materials with different-molecular-weight polyethyleneimine (PEI), by taking hydrophobic oily macroporous adsorbent resin as carrier. The specific surface area, pore structure, functional group structure and thermogravimetric properties of the resin-based solid amine adsorbent were characterized by N₂ isothermal adsorption-desorption, infrared and thermal analysis. The effects of PEI loading, air humidity (30%~80%), adsorption time and multiple cycles on the adsorption performance of CO₂ were investigated. The results show that, the hydrophobic resin-based solid amine adsorbent has good trapping performance for CO₂ in dry air (air humidity is less than 50%). The SD300 resin-based solid amine adsorbent modified by 30%PEI can reach more than 90% of the total adsorption capacity after one hour adsorption in atmospheric environment. When the molecular weight of the PEI is 1 800, it shows high adsorption capacity and good cycle stability of adsorption and desorption, mainly due to the high pore size and its excellent high temperature resistance.

High-temperature environments can lead to the deterioration of the heat dissipation performance of indirect air cooling towers. Air inflow spray pre-cooling is an effective method to enhance the heat dissipation performance of indirect air cooling towers. Taking a 2×350 MW indirect air cooling unit in northwest China as the research object, a numerical model coupling the spray evaporation with the ventilation and heat dissipation of the indirect air cooling tower is established to study the effect of air inflow spray pre-cooling on the performance of the indirect air cooling tower with different environmental factors. The results show that crosswind can carry the spray downstream, causing the spray to accumulate and benefiting the radiators in the leeward area the most. The performance improvement of the radiators in the windward area decreases with the increasing wind speed, while the radiators in the side area even experience performance degradation at medium to high wind speeds. Additionally, as the wind speed increases, the spray flows out of the annular evaporation zone, resulting in some pre-cooled ambient air failing to enter the radiators and leading to spray waste and reduced effectiveness. The improvement rate of heat dissipation in the indirect air cooling tower after air inflow spray pre-cooling decreases at first and then increases with the increasing wind speed. At an ambient humidity of 40%, the heat dissipation improvement rate decreases from 5.65% at 0 m/s to a minimum of 2.03% at 8 m/s, and then rises to 3.98% at 12 m/s. The effectiveness of air inflow spray pre-cooling weakens with the increasing ambient humidity. Under windless conditions, as the humidity increases from 20% to 80%, the heat dissipation improvement rate of the indirect air cooling tower decreases from 6.4% to 2.4%.

The distribution characteristics of the air flow field inside the natural-draft direct-air-cooling exhaust tower under low-temperature and low-load operating conditions still remain unclear. There is an urgent need to study its variation laws and propose effective measures to ensure exhaust performance and anti-freezing safety. Through the computational fluid dynamics (CFD) numerical simulation, the flow and temperature fields inside the tower at ambient temperatures of –21 ℃ and –30 ℃, and at different wind speeds are analyzed. The results indicate that, based on the symmetrical operation of steam isolation valves for sector switching of the air-cooled condenser, using louvers to regulate airflow in isolated sectors can effectively optimize the internal airflow field, ensure smooth exhaust under low-temperature conditions in winter, and significantly reduce the risk of localized freezing. Field tests verified that this measure can reduce the unit backpressure by approximately 2 kPa and improve the flue gas flow deviation.

In view of the problems that conventional fly ash carbon content prediction models are prone to fall into local optimal solution traps and have insufficient generalization ability, based on the boiler hot-state multi-condition tests, 28 key characteristic parameters are selected through data collection, processing, Pearson correlation analysis of variables, and importance ranking, the sparrow search algorithm (SSA) is used to determine the optimal hyper-parameters of the random forest (RF) model, and an SSA-RF prediction model is constructed. The model verification results show that the root-mean-square error of the SSA-RF model in the training set and the test set decreases to 0.010 8 and 0.019 1 respectively, and the coefficient of determination R² increases to 0.999 7 and 0.998 1 respectively, demonstrating the excellent prediction accuracy and generalization ability of the model. Furthermore, the ISSA-RF-SSA algorithm is proposed. The SSA is improved by integrating multiple strategies to achieve global extreme value optimization of combustion parameters. Engineering verification shows that after optimization, the carbon content in fly ash decreased from 2.500% to 1.345%, and the prediction error was only 0.003 percentage points, verifying the accuracy of the model. The research results indicate that the ISSA-RF-SSA method improved by multiple strategies significantly enhances the optimization performance of the algorithm, providing a new idea for the combustion optimization of coal-fired units.

Abnormal stator core temperatures in generators can lead to serious issues such as aging of insulating materials and winding shorts, thereby affecting the overall performance and lifespan of the generator. This study presents a stator core temperature prediction model for turbo generators based on FFCM-MHDA-iTransformer. It leverages an improved Transformer architecture, namely the inverted Transformer (iTransformer) model, which adopts an inverted time-series encoding approach to address the limitations of the standard Transformer in handling multivariate variable correlations. The model employs fused Fourier convolution mixer (FFCM) to enhance and extract local features from time-series data. Furthermore, the model replaces conventional self-attention with multi-head differential attention (MHDA), effectively reducing attention noise and directing the model’s focus towards critical information. After training and validation, the proposed model demonstrates higher prediction accuracy compared to other mainstream prediction models. It facilitates timely detection of potential faults, preventing shutdowns for maintenance, and holds significant application value for ensuring stable operation of turbo generators. This approach effectively enhances the accuracy and practicality of temperature prediction technology.

The first stage blades of a heavy-duty gas turbine compressor are newly independently developed and designed, which use the advanced 3D modeling technology to design high-performance blade profiles. It is necessary to master the blades’ vibration characteristics to verify the reliability of the blades. The finite element method was used to analyze the vibration frequency of the blades under dynamic frequency testing conditions and actual operating conditions. Meanwhile, the radio telemetry technology has been introduced to verify the dynamic frequency of the blade. The dispersion effects caused by blade material and processing tolerances, as well as assembly tolerances were also considered. The results indicate that the theoretical analysis of blade vibration is consistent with the test characteristics, with a deviation of no more than 1.2%. The theoretical frequency avoidance margin of compressor blades under operating conditions can meet the deviation between numerical analysis methods and experimental testing methods, as well as the frequency influence caused by material, processing, and assembly factors, and still have a large safety margin. The research results provide guidance for the development of gas turbine compressor blades, as well as the upgrading, improvement, and vibration monitoring of blades throughout their entire lifecycle.