The cooling efficiency of flat film was measured using a high-precision infrared thermal imager, and the film cooling efficiency between double-cross-row holes and single row holes is compared. The interaction between film holes and the influence of blowing ratio (M=0.65, 1.0, 1.5) and density ratio (DR=1.0, 1.5) on the cooling efficiency was analyzed. Moreover, the flow field with film cooling was compared using numerical calculation methods. The results show that, with the increase of the blowing ratio, the cooling efficiency of the single row holes decreases while that of the double-cross-row holes improved greatly, but the film coverage effect at the spanwise direction deteriorates. Increasing the density ratio will improve the cooling performance. However, the influence of double rows of film holes and blow ratio is much higher on the cooling effectiveness, compared with the density ratio. For the double rows of film holes cooling, the cooling jet forms a reverse kidney-shaped vortex downstream of the holes, which will prevent the jet blowing away from the cooling wall.

In wind turbines, the aerodynamic efficiency of wind turbines is closely related to the aerodynamic performance of excellent airfoils. Taking the conventional airfoil of wind turbine as the research object, combined with airfoil parametric modeling and self-adaptive genetic algorithm, the high performance optimized airfoil is obtained. The fitting accuracy of the conventional NACA63418 airfoil is compared between the CST method and the improved Hicks-Henne type function method, and then the Hicks-Henne type function method is selected to model the NACA63418 airfoil. The automatic calculation of aerodynamic characteristics of airfoil is realized by the coupling of self-adaptive genetic algorithm and XFOIL software, and the design efficiency of airfoil is improved. It broadens the train of thought and improves the design efficiency for the theoretical design of airfoils.

In order to meet the demand of estimating regional heat load for cogeneration enterprises, an estimation method using elastic network regression model is proposed. Firstly, the influencing factors of the actual heating heat index are analyzed to determine the input parameters of the model. Then, based on the actual operation data of 123 residential areas in Xi’an in the heating season from 2022 to 2023, the estimation model is established, and it is proved that the accuracy of the model is higher than that of Lasso regression and ridge regression models. Finally, part of the communities in Xi’an are selected to form a verification set to verify the elastic network regression model. The verification results show that, the elastic network regression model combines the advantages of Lasso regression and ridge regression, and has higher prediction accuracy than the conventional machine learning model. The MAE and goodness of fit of the model are 1.150 and 0.953, respectively, indicating that the method can accurately estimate the actual heating heat index with different parameters, and can meet the actual engineering needs of cogeneration enterprises.

Rapidly growth of organic solid waste (OSW) has caused serious social and environmental issues. Co-combustion is an economical and environmental method to treat OSW. However, there will be a variety of heavy metals discharged with flue gas during combustion as OSW contains them. Therefore, this study conducted an experiment on the migration of heavy metals and generation of micro particles during the co-combustion of OSW and lignite. The results show that, the release of heavy metals during combustion not only depends on the concentration of heavy metal the fuel contains, but also on the chemical mechanism at high temperature. The content of Pb, Cd and As in the flue gas of blending fuel is obviously lower than that of sole OSW, but Cu, Zn, Co and Mn shows opposite tendency. In addition, the particle size distribution of the four fuels all shows a normal distribution at 800 ℃ and 850 ℃, but large particle size generates when the combustion temperature rises to 900 ℃.

Infrared spectroscopy detection technology has been widely used in petrochemical, pharmaceutical and other industries due to its fast detection speed, no damage to the sample, no pollution, easy to operate and other characteristics. The infrared spectroscopy detection mainly includes qualitative detection and quantitative detection. The qualitative detection usually uses comparative method, which compares with standard substances or consults standard spectra. The quantitative detection calculates the corresponding components content by measuring the intensity of characteristic absorption bands and combining with chemometrics methods. This article elaborates the applications of chemometric methods (including artificial neural networks and partial least squares) in detection scenes such as acid value, moisture, antioxidants and furfural for electric power oil. Finally, some proposals of infrared spectroscopy in oil detection are put forward.

Adiabatic compressed air energy storage technology (A-CAES) can be used for peak shaving and frequency regulation of renewable energy electricity, which is an effective means to achieve the goal of “Dual Carbon”. In order to study the influence of key parameters such as the number of stages, hot side temperature difference, and throttling valve pressure on thermodynamic efficiency and economy of the system, and achieve the lowest levelized cost of energy (LCOE), an A-CAES model based on MATLAB is constructed for calculation. The results show that, within the range of simulated working conditions, the efficiency decreases with the increase of the number of stages and the hot side temperature difference, while increases with the throttling valve pressure, and the highest efficiency can reach over 70%. The LCOE of the secondary compression and secondary expansion is the lowest, which is 0.041 3~0.045 0 dollars/(kW·h). The LCOE decreases with the increasing throttling valve pressure. When the hot side temperature difference is greater than 2.5 K, the LCOE increases with the hot side temperature difference. Therefore, the A-CAES can realize efficient and low-cost energy storage.

With the increasing annual growth of wind and solar power generation, the issue of power consumption has become increasingly prominent. Meanwhile, high-capacity thermal power plants face relatively high auxiliary power loads, resulting in additional operating costs. To address these issues, a joint optimization dispatch model for wind-PV-thermal-storage for auxiliary power system of thermal power plant is developed based on the concept of multi-energy complementarity. Firstly, the compositional structure of the wind-PV-thermal-storage integrated power supply for auxiliary power of thermal power plants is outlined, prioritizing the supply of auxiliary power loads with wind power and photovoltaic power. Secondly, a wind-PV-thermal-storage integrated power supply optimization scheduling model is developed, taking into account the operating costs of thermal power units at different load rates and the costs associated with wind power, photovoltaic power, and energy storage. A hierarchical analysis method is employed to establish a multi-objective function based on the total cost, wind and solar curtailment costs, and environmental costs, while considering corresponding constraint conditions. Finally, various scenarios are set up to compare and analyze the optimization results of the integrated power supply system for auxiliary power. Experimental results demonstrate that the proposed model for the integrated power supply system can effectively reduce unit operating costs and environmental costs, as well as promote the integration of wind and photovoltaic power.

Clarifying the driving factors for promoting low-carbon technology innovation in the power generation industry, and exploring incentive mechanisms for low-carbon technology innovation in the power generation industry, is of great significance for achieving the unity of economic, environmental, and social performance in the power industry, and ultimately achieving the “dual carbon” goal of the country. This article focuses on the characteristics of technological innovation in the power generation industry, starting from the two stages of low-carbon technological innovation, and based on external driving forces and internal driving forces of enterprises, it proposes 11 incentive factors to construct an internal and external collaborative incentive mechanism for low-carbon technological innovation in the power generation industry. The results show that, with the synergistic effect of the low-carbon technology innovation incentive mechanism in the power generation industry, the innovation research and development level of low-carbon technologies of enterprises can be promoted, and the economic, environmental and social performance of the enterprises can be improved through the transformation of low-carbon technology achievements. Therefore, an organic unity of enterprise development, environmental improvement and social progress is realized ultimately.

At present, China’s thermal power plant steam/water pipeline design standard stipulates that the load variation coefficient of spring hangers should not exceed 25%. Accordingly, the designed proportion of the constant support hanger is too high, and the “illegal” transfer of the load will cause the pipe operation deviates from the design line, and the stress increases. This paper analyzes the relationship between the constant degree and the load variability factor of constant hanger. The optimization design case shows that, properly increasing the load variation coefficient of spring hangers in the design of steam/water pipelines can increase the proportion of spring hanger configuration and reduce the occurrence of abnormal pipeline expansion.

To solve the difficult problems of renewable energy consumption, hydrogen energy storage and transportation, an off grid integrated system for wind, solar, hydrogen and ethanol is proposed. The system operates offline, utilizing wind and photovoltaic power generation to provide electrical energy. By using batteries and hydrogen storage tanks as energy storage and hydrogen storage equipment, electricity and hydrogen energy is stably supplied in a peak shaving and valley filling manner, ensuring the stable and continuous production of methanol in the electrolytic cell and methanol generation equipment. A mathematical model for solar energy hydrogen storage alcohol is constructed with the goal of maximizing the total system revenue, and the optimal equipment capacity and operation scheduling of the system is determined through mixed integer linear programming algorithm combined with real solar energy data analysis. The operating strategy and the system energy of the system on a typical day is analyzed, and finally the economic performance of the produced green methanol is investigated. The results indicate that, the system can switch operating states reasonably based on changes in external conditions, thus to achieve energy balance in the system. On the premise of meeting various constraints, the utilization rate of renewable energy is improved and the leveling cost of methanol production in the system is reduced. This study proposes a feasible technical route for the consumption of new energy and the utilization of hydrogen energy, and provides certain guidance for the construction of related demonstration projects.