A field test and numerical simulation study is carried out on the slagging problem of a 1 000 MW double-tangential coal-fired boiler during the co-firing of high ash melting point coal and low ash melting point coal. The test results show that as the proportion of low ash melting point coal increases, the slagging in the furnace shows a significant aggravation trend. When the proportion of low ash melting point coal is 50%, slight slagging occurs in the furnace. When the proportion increases to 67%, large-scale coking occurs on the bottom of the large screen heat transfer surface. When the proportion reaches 83%, the slagging situation deteriorates significantly, and the proportion of slag blocks in the furnace slag exceeds 40%. The numerical simulation results of slagging are in good agreement with the field operation test results. The results show that slagging is mainly concentrated in the front and rear wall areas, and the degree of slagging on each heat transfer surface increases with the proportion of low ash melting point coal. Although the addition of low ash melting point coal does not significantly change the near-wall temperature, the significant reduction of the ash melting point of the mixed coal is the fundamental reason for the deterioration of slagging. The operation mode of low ash melting point coal in the burner has a significant effect on slagging, especially when the low ash melting point coal is co-fired in layers D and C, the slagging trend is particularly obvious. It is recommended to prioritize the arrangement of low ash melting point coal in layers A and B, followed by layer F, and avoid co-firing low ash melting point coal in layers D and C.

Supercritical carbon dioxide (S-CO₂) printed circuit heat exchangers (PCHEs) are widely used in Brayton cycle power generation system, but PCHE faces problems such as uneven heat transfer and poor comprehensive performance under different working conditions. To improve the overall performance of PCHE in the Brayton cycle, the comprehensive performance (PEC) of S-CO₂ on both the cold and hot sides of PCHE under different parameters was numerically investigated, by using S-CO₂ as the working fluid, and varying the convergent-divergent pitch period (T), cross-sectional area ratio (β), and the ratio of convergent length to divergent length (γ). The results show that when β and γ are fixed, the pitch period on the cold side is inversely proportional to the overall performance, while the optimal pitch period on the hot side ranges from 15 mm to 25 mm. The PEC values of PCHE with convergent-divergent pitch periods are consistently greater than 1, indicating superior performance compared to the conventional straight-channel designs. Under a cold-side operating pressure of 22 MPa, the PCHE shows a relatively high comprehensive performance compared to the hot-side operating pressure of 8.5 MPa. When the cross-sectional area ratio β exceeds 1, all PEC values are greater than 1, and the intensified convective heat transfer between the fluid and the wall enhances the overall performance. With other conditions held constant, the system achieves better comprehensive performance when the ratio of convergent to divergent length γ is 3/7. The results provide a reference basis for optimizing the comprehensive performance of PCHE with gradually varying cross-section flow channels.

A hybrid prediction model combining enhanced grey wolf optimization algorithm (EGWO) and long short-term memory (LSTM) neural network is proposed to address the problem of low accuracy in predicting the mass concentration of NO_x at the outlet of selective catalytic reduction (SCR) denitrification reactors using conventional mechanism modeling methods. Firstly, based on principal component analysis (PCA), the raw data is processed and filtered to achieve dimensionality reduction of input variables. Then, the EGWO is used to optimize the hyperparameters of LSTM. Finally, the input variables are used as inputs for the EGWO-LSTM model to predict the mass concentration of NO_x at the outlet. Taking a 1 000 MW ultra supercritical thermal power unit in China as an example, simulation results show that the proposed model performs the best in error control, with root mean square error reduces by 50.36% compared to the conventional LSTM model, and by 76.14% compared to the BP model, and the mean absolute percentage error of the model is only 1.01%. The EGWO has fewer iterations and higher convergence accuracy compared to the GWO when converging to the optimal solution.

To address the challenges of low diagnostic accuracy and poor interpretability for minority fault classes caused by imbalanced data distribution in coal mill pulverizing systems of coal-fired power plants, a fault diagnosis method integrating SMOTE data enhancement, Dirichlet prior smoothing, and Bayesian networks is proposed. The SMOTE technology expands the feature space of minority fault samples to alleviate data scarcity, while Dirichlet prior smoothing optimizes conditional probability estimation in Bayesian networks, resolving zero-probability issues caused by insufficient samples. A hierarchical Bayesian network architecture is constructed by incorporating domain knowledge and data-driven structure learning, enabling a dual-mode diagnosis strategy that combines rapid fault node inference with indirect attribute node analysis. The experimental results based on real industrial data demonstrate that the proposed method achieves high diagnostic accuracy and interpretability under imbalanced data scenarios. The solution provides real-time performance, precision, and transparency for coal mill fault diagnosis, offering significant engineering value.

As a core control parameter in peak regulation via banking fire, the banking fire duration directly affects the safety and economic efficiency of unit operation. However, due to the complex coupling and dynamic characteristics of thermodynamic parameters during the banking process, it is difficult for existing calculation methods to achieve efficient and accurate calculations. An energy-balance-based method was proposed for banking fire duration calculation in subcritical CFB boilers. A dynamic equilibrium model was established for heat storage and turbine heat utilization and heat dissipation during banking fire, deriving heat storage and release formulas for key heat sources, such as bed material, refractory, metal heating surfaces, working fluid, and carbon combustion. Finally, the banking fire duration was obtained. Taking a 300 MW sub-critical CFB unit as an example, the absolute error between the calculated value and the measured value is controlled within 5 minutes, and the relative error is less than 10%, which can meet the engineering requirements of fire-hold peak-shaving. The results demonstrate that, in terms of heat storage, the heat storage of metal heating surfaces contributes 35%~41% to the banking fire duration. The contributions of bed material and refractory are each approximately 20%, and the contribution of carbon combustion in the bed material is 10%~15%. The contributions of gas and working fluid heat storage are less than 2% and can be neglected. In terms of heat consumption, heat consumption for power generation accounts for the highest proportion, and it increases with the electrical load. The heat required for the steam turbine to overcome its own rotational resistance accounts for approximately 20%, and the proportion of heat dissipation of the unit is less than 5%. By raising the initial temperature of banking fire, increasing the amount of bed material, using coal with high volatile content, and reducing the electrical load of the unit during banking, the banking fire duration can be significantly prolonged. Notably, the banking fire duration exceeds 2 hours only when the average electrical load during banking is reduced to 1% of rated load.

Ammonium bicarbonate is a potential denitrification reducing agent that can efficiently produce ammonia gas through direct solid pyrolysis. The pyrolysis reaction of ammonium bicarbonate solid is numerically simulated, a pyrolysis ammonia production system suitable for coal-fired power plants is designed, and the economic feasibility of the ammonium bicarbonate pyrolysis ammonia production process is analyzed. The simulation results show that, the pyrolysis process of ammonium bicarbonate favors the atmosphere pressure and the conversion rate of pyrolysis rapidly increases when the reaction temperature is above 110 ℃. The pyrolysis system of ammonium bicarbonate for a 660 MW unit has been designed and calculated. An external heating pyrolysis reactor is adopted to realize the utilization of waste heat and stable solid feeding. Steam or flue gas from the coal-fired power plant is used as the heat source for pyrolysis. At 110 ℃, a conversion rate of 95% can be reached within 10 minutes for ammonium bicarbonate feed. Compared with the urea hydrolysis process, the equipment cost, land occupation and operating cost of the ammonium bicarbonate solid pyrolysis process all significantly reduce, showing good prospects for promotion and application.

At home and abroad, the locations suitable for developing concentrated solar power are mainly in desert areas. Dust in these environments may accumulate on the heat absorbing surfaces of the receiver in the solar power tower system, resulting in failure of the wall and coating of the pipe. To protect the heat absorbing walls, a coupled heat transfer model is developed for the sand-pipe, and the effects of several parameters on the wall temperature are investigated, such as the dust particle diameter, the contact areas between the dust and tube wall, and the concentrated solar energy flux density. The results show that, the influence of dust particles on the temperature of the heat-absorbing pipes is limited to a small area, but it will cause local high-temperature hot spots on the pipes. With a high concentrated solar energy flux density, a large dust particle diameter and a small contract area between the dust particles and the heat-absorbing pipes, both the temperature of the dust particle and the hot spot at the pipes will increase greatly. The temperature of the dust particles could exceed their melting point, forming calcium-magnesium-aluminum-silicate (CMAS) deposits, which means the receiver is at risk of CMAS corrosion. Meanwhile, the high-temperature hot spots on the heat-absorbing pipes will affect the local thermal stress distribution, exacerbating the damage to the receiver. Therefore, during actual operation, the cleanliness of the heat-absorbing pipe walls should be regularly inspected to avoid the accumulation of large-sized dust particles. The research results can provide technical guidance for the operation and maintenance of the receiver in the concentrated solar power system.

To enhance the peak shaving performance of heating units, a new process for double-reheat heating unit integrating five thermo-electric decoupling technologies, namely cylinder cut-off, high-/medium- and low-pressure bypass heating, heat pump, hot water tank and electric boiler, has been proposed. A detailed thermodynamic model of the system was established, and the peak shaving performance of the novel power plant is compared with that of a reference power plant. Relying on the electricity market, a systematic economic operation strategy was put forward, and a techno-economic analysis was performed. The results show that, when the heating demand is 1 460 MW, the reference plant cannot meet the heating demand under the extraction-condensing condition. Under the cylinder cut-off condition, the load regulation range of the reference plant is 77.9% to 80.0% of the rated load, and it almost loses its load regulation ability. While under the cylinder cut-off + bypass condition, the load regulation range of the reference unit is 50.0%~80.0%, and its peak regulation ability has been improved. For the novel plant, in the same heating demand, the load regulation range has been expanded to 0~80.0%, and zero-power grid connection can be achieved especially during the low electricity demand period. Compared with the reference plant, the novel plant can reduce the power output during peak shaving periods by 107 600 MW·h per month, save 17 700 tons of coal, achieve an annual net profit increase of approximately 68.988 million yuan during the heating season, and have a payback period for new equipment investment of 5.6 years, demonstrating significant economic benefits.

As the core of thermal power units, the operation efficiency of the direct air cooling system is significantly restricted by the geographical location of the power plant and the surrounding environmental parameters. Taking the direct air cooling system of a power plant as the prototype, a three-dimensional numerical model of the air cooling system and the surrounding buildings and mountain environment is established, and the composite wind prevention measures for the windward side of the air cooling island or the units with unfavorable heat transfer are proposed. The windproof measures and optimization mechanism of the direct air cooling system with strong applicability and good effect are explored, and the influence of air flow field reconstruction inside and outside the air cooling unit on the cooling performance of the direct air cooling system is analyzed. The results show that the internal and external wind-proof measures of the direct air-cooled unit can effectively improve the thermal performance of the unit. When the wind speed is 5 m/s, the large cross wall windshield has a good effect on improving the heat transfer performance of the air-cooled island. The frontal wind speed of the radiator increases by 0.24 m/s, and the surface temperature of the radiator reduces by 2.40 ℃. After the reconstruction of the air flow field inside and outside the air cooling unit, the average surface temperature of the radiator in the direct air cooling system reduces by 5.77 ℃, and the back pressure reduces by 2.92 kPa. The reconstruction of the air flow field inside and outside the air cooling unit can significantly improve the cooling effect of the direct air cooling system and improve the operating performance of the cold end system of the power station. In the future, the optimization design of the diversion device of the direct air cooling system can focus on the improvement of the uniformity of the flow field.

To construct a prediction model for carbon emission from coal-fired power plants and address the problem of general lack of real-time elemental analysis for coal entering the furnace of coal-fired units, according to the in-furnace coal quality information of a million kilowatt unit in 2023, the low calorific value, volatile matter, and sulfur content were used as the basis for coal quality classification, K-means++ algorithm was used for clustering analysis, and correlation analysis was used to screen the input parameters of the carbon emission prediction model. The BP neural network suffered Bayesian optimization was used to construct carbon emission prediction models for each cluster data after clustering, and the models were tested for working conditions such as load increase and decrease. The results show that, the accuracy of the coal quality clustering model in predicting carbon emissions increases significantly. Compared with the non clustered model, the optimal cases of average root mean square error and average relative error reduce by about 53.4% and 49.2%, respectively. Especially under variable load conditions, the predicted results are more in line with the actual values. This indicates that the proposed method can not only effectively predict the carbon emissions of coal-fired power plants, but also maintain high accuracy in the case of complex and variable coal quality.