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.

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.

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.

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.

Based on the design and operational conditions of Guangdong Huaying LNG Terminal and its surrounding industrial environment, a cascade utilization scheme integrating thermodynamic power generation with shallow cold storage was developed. Moreover, key process parameters were modeled and solved using HYSYS software to enhance energy efficiency and maximize cold energy utilization. The results show that, under the condition of minimum daily send-out (228 t/h), the original single-stage thermodynamic cycle coupled with cold storage achieved an annual power generation exceeding 32.83 GW·h while meeting the cooling demand of a 7 500 m³ cold storage facility. The optimized scheme adopts a two-stage thermodynamic cycle with shallow cold storage, via employing a 40% (weight percentage) ethane and 60% (weight percentage) propane mixed working fluid, and elevating heat source temperature, this improved design increased the annual power generation to 62.04 GW·h, and raised the net power output per unit mass of LNG from 17.54(kW·h)/t to 33.02 (kW·h)/t, with estimated annual electricity cost savings of approximately 53.641 million yuan. Although multi-stage heat engine cycles can reduce irreversible losses caused by temperature differences, considering factors such as cost-benefit ratio and operational reliability, the second scheme demonstrates strong engineering feasibility and economic viability by closely aligning with the actual conditions of the Huaying LNG Receiving Terminal. Both cascade utilization designs demonstrate distinct advantages for different development stages of the receiving terminal and different evaluation indicators for LNG cold energy utilization, providing valuable references for post-commissioning cold energy applications.

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.

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.

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.

Based on the concept of high efficiency of gas turbine variable back pressure operation regulation, a high back pressure gas turbine combined cycle power generation system scheme is proposed, in which a pre-compressor and an expander are set in front and behind the main top cycle respectively to maintain and regulate the gas turbine exhaust pressure. Key parameters of the combined cycle are designed based on the initial parameters of the F-class gas turbine, and the case and characteristic analysis are carried out for the temperature of recirculated gas (divided into two conditions: cooling to normal temperature and not cooling), the main top cycle pressure ratio and the gas turbine back pressure. The results show that, the combined cycle efficiency of the recirculated flue gas cooling is not as high as that of the non-cooled flue gas cooling, which is 58.07% and 58.94% when the turbine back pressure is 0.30 MPa. The exergy loss rate of the main compressor is lower because the exit temperature of the main compressor is higher when the recirculated flue gas temperature is higher. When the gas turbine back pressure is 0.30 MPa, the maximum pressure ratio of the combined cycle system efficiency is 17.0, the corresponding combined cycle efficiency is 58.97%, and the specific work is 563.87 kJ/kg. Considering the specific work comprehensively, the recommended main top pressure ratio is 15.4, and when the turbine back pressure is from 0.03 MPa to 0.35 MPa, the variation range of the combined circulation efficiency under the two conditions is about 56.00%~58.57% and 55.81%~59.12%, respectively, which increases at first and then decreases, and the variation range of the combined efficiency is not large at high back pressure. At the same time, based on the practical engineering application, the design of a single waste heat boiler is considered, and its thermal characteristics and possible flexible, low-cost and efficient utilization of renewable energy are analyzed, which provides a new system scheme reference for the flexible and efficient modern combined cycle with multi-energy complementarities.

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.