Three-dimensional computational fluid dynamics simulations are performed for a 650 ℃ grade 1 000 MW ultra-supercritical swirl-opposed firing boiler with low NO_x combustion in the furnace. The flow and combustion characteristics in the furnace and the NO_x concentration in the flue gas are investigated under various conditions of overfire air. It is shown that setting a staggered overfire air and injecting the lower OFA with the angle 15°downward into the furnace is helpful to reduce NO_x concentration and improve the burnout rate. A lower overfire air ratio results in a higher temperature in the region of the combustor and a shorter distance between the high-temperature region and the heating surface. As the overfire air ratio increases, the NO_x concentration of the flue gas at the exit of the furnace first decreases and then increases, and the optimal overfire air ratio is around 33.9%.

In order to improve the flexibility of coal-fired units, a flexible peaking operation method is proposed by combining the heat storage by extracting the reheated steam and secondary air heating by extracting the main steam. Taking the ultra-supercritical 660 MW indirect air-cooled unit as the object of study, the performance of the unit under different peaking schemes at 30% BMCR operating conditions, the energy reuse rates under different heat release conditions and the performance of the combined load reducing operation mode are analyzed. The results show that the energy reuse rates of extracting main steam heat storage, extracting main steam heating secondary air and extracting hot reheat steam heat storage are 55.13%, 84.74% and 46.24%, respectively, at a peak-load shaving capacity of 20 MW with 75% THA heat release condition. Under the premise of ensuring the safety of boiler combustion and heating surface, the combined load reducing operation mode can achieve a peak peak-load shaving capacity of 46 MW, and the energy reuse rate can reach 74% when the heat release condition is 75% THA. This study can provide a reference for flexible peak-load regulation of coal-fired units.

An effective disposal method of municipal sewage sludge is burning sewage sludge together with coals in coal-fired circulating fluidized bed (CFB) boilers. At an appropriate weight ratio of sewage sludge in fuel blend, the co-combustion of sewage sludge and coals will not cause significant adverse effects on power plant production, and make use of the calorific value of sewage sludge. This study summarizes the progress of research and application in CFB boilers co-firing coal and sewage sludge, involving the emissions and controls of pollutants during the co-combustion of sewage sludge and coals, the weight ratio of sewage sludge in fuel blend under the reliable boiler operation, and sludge drying. The operational problems associated with the co-combustion of wet sludge and coals are analyzed. The applications of coal-water slurry suspension combustion technology and oxygen-enriched combustion technology in the disposal and utilization of sewage sludge are presented. The study points out that the drying and incineration of sewage sludge combined with advanced flue gas purification technology is one of the mature processes for the application of coal-fired CFB boilers in sewage sludge incineration, which can avoid the environmental impact of odours generated during the storage, drying and transportation of sewage sludge, and provide effective controls of a variety of pollutants. The conclusions of investigation and analysis can provide a reference and basis for the design and safe operation of CFB boilers co-firing coal and sewage sludge.

Coupled with the energy storage system can improve the peak shaving capacity of the thermal power unit. To improve the thermoelectric decoupling ability of the combined heat and power unit, a coupled thermal power plant combined heat and power unit with liquid carbon dioxide energy storage system is proposed. The system utilizes the condensate to recover the compression heat of the carbon dioxide during the charge process, and supplies heat to the users together with the heating extraction steam. Besides, the heating extraction steam is employed to preheat the carbon dioxide of the expander inlet during the discharge process. Based on the established thermodynamic models, the thermal performance analysis of the coupled system was carried out with the thermal efficiency, exergy efficiency, and electricity storage efficiency as assessment criteria. The sensitivity analysis results indicate that increasing both the expander inlet temperature and the discharge pressure can obtain a higher system exergy efficiency and electricity storage efficiency; increasing the charge pressure results in a higher system thermal efficiency, while the exergy efficiency first increases and then decreases. The parameter optimization of the corresponding CO₂ energy storage system was carried out under the design parameters. Results show that when the charge pressure is 10.5 MPa and the discharge pressure is 18.0 MPa, the coupled system achieves the optimal efficiency of 64.92%.

There is a common mismatch between heating supply and demand parameters for industrial heating retrofits of pure condensing thermal power units, and the benefits of thermal power plants can be improved by adopting a reasonable matching scheme of supply and demand parameter. Aiming at the phenomenon of energy mismatch caused by the excessively high extraction parameters of the unit in the industrial heating scene, considering that it is suitable for high-parameter industrial heating, this paper proposes a scheme of using the centripetal turbines for cascade utilization of extraction steam. Taking the industrial heating transformation of a domestic 330 MW cogeneration unit as an example, the thermal system model was constructed to comprehensively evaluate the performance indicators changes of the system from three aspects: thermal performance, exergy environment and economic performance. We also comprehensively compared the effect of upgrading the direct heat reduction and pressure reduction method to the radial turbine power generation steam energy cascade utilization scheme. The calculation results show that under the same heating parameters, compared with the traditional method of temperature reduction and pressure reduction, the comprehensive benefits of the industrial heating steam energy cascade utilization scheme based on centripetal turbine power generation is significant. The specific performance is that under rated conditions, the gross coal consumption rate for power generation can be reduced by 0.89 g/(kW·h), the extraction exergy efficiency can reach 97.66%, and the direct economic benefit is relatively increased by 1.04%, and the carbon transaction cost relative reduction of 0.34%. In addition, the relative advantages of all aspects performance of the system will expand with the increase of the amount of steam extracted by the system for industrial heating.

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year, to ensure the safe and stable operation of the power grid, a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly, a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network (1DCNN) and long short-term memory (LSTM) neural networks, to obtain multi location numerical weather prediction (NWP) information and historical power information, using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network (FCNN) is added to the combined neural network model, which is used to learn and assign weights to the two prediction results, achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei, and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.

It is known that the interdiffusion at the aluminide coating/matrix interface during the long time exposure at high temperature would change the microstructure of the matrix and deteriorate the mechanical properties of the matrix. To analyze the high temperature strength of aluminide coating on T92 steel for ultra-supercritical unit, aluminide coating is prepared on the inner wall of T92 steel boiler tube by low temperature powder embedding method, and the tensile test was carried out at room temperature to 625 ℃ and the durability test was carried out at 625 ℃ environment. The effect of aluminide coating on the tensile properties and durability life of T92 matrix are studied by combining scanning electron microscope (SEM), optical microscope (OM) and X-ray diffraction analysis(XRD). The results show that the aluminide coating prepared on the inner wall of T92 boiler tube by low temperature powder embedding aluminizing, which is metallurgically combined with the matrix, has a double-layer structure, and each layer is continuous and uniform. The total thickness of the prepared aluminide coating is about 30.4 μm. The coating has columnar crystal structure and the surface is cracked in the room temperature is increased to 625 ℃. During the creep rupture process at 625 ℃ environment, FeAl coating have many cracks, but the crack depth is shallow and very few cracks extend to the matrix. The coating peels off locally large strains under the high stress state. It can be concluded that even deforms of the aluminide coating occurred during the long-time creep process, it can still has good metallurgical bonding with T92 matrix.

The flow channel structure at the cathode side is one of the main factors affecting the performance of proton exchange membrane fuel cells. The flow channel at the cathode side needs to discharge liquid water out of the fuel cell in time and make oxygen flow to the cathode catalytic layer as much as possible. Thus, the phenomenon of cathode flooding and concentration polarization is avoided. An innovative 3D cathode side channel, sugar gourd type channel, is designed. The sugar gourd type channel is formed by adding arc-shaped side trapezoidal block based on the traditional straight flow channel. The simulation results show that, compared with the traditional straight flow channel, the current density in the high current density region is increased by about 8%. And because of the special structure of the sugar gourd type channel, the air flow advances to the outlet in the form of pulse decline, and the heat and mass transfer are significantly enhanced. In addition, the influence of arc-shaped side trapezoidal height on overall performance is further explored.

Distributed energy power stations are developing rapidly because of their cleanliness, environmental protection, economy and high efficiency. However, there are few data used for fault diagnosis of plant equipment, so a method to predict the health state and aging degree of equipment is urgently needed. Based on this, a prediction model which can analyze the running state of equipment and obtain the deterioration trend of equipment is proposed. Firstly, multi-dimensional data of the equipment is preprocessed, and an improved Mahalanobis distance based equipment health model of distributed energy power station is constructed quantitatively by combining the analytic hierarchy process (AHP) with Gaussian mixture distribution. Then, the combined prediction model based on the improved sparrow algorithm and short and long time memory neural network is established to predict the trend and correlation analysis of the deterioration of distributed energy power plant equipment. The experimental results show that the proposed fusion health model can predict equipment anomalies in the case of insufficient actual fault data of distributed energy power stations.

Aiming at the problem of inaccurate prediction of NO_x emission concentration when the current coal-gas boiler gas mixture is uncertain and changing, a combined online prediction method based on attention mechanism is proposed. First, the characteristic variables of the model are determined by combining the maximum information coefficient method with the Pearson correlation coefficient method; Secondly, vector autoregressive(VAR) model is constructed online with sliding time window for linearly correlated characteristic variables to realize the prediction of NO_x emission concentration under the input of multi-dimensional time series linear correlation variables For non-linear-related feature variables, the relationship between NO_x emission concentration is predicted by constructing an online Recurrent extreme learning machine(OR-ELM) model online learning. Finally, Attention Mechanism(AM) is used to dynamically weight the two forecasting models to achieve trend forecasting. Through field data verification, it shows that the VAR-OR-ELM combined online prediction model constructed in this paper can accurately predict the variation trend of NO_x emission concentration after 10 minutes. Combining prediction accuracy and prediction time, the combined prediction model is better than other single prediction models.