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.

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.

The real gas characteristics of supercritical carbon dioxide (S-CO₂) make it difficult to obtain stable convergence results in three-dimensional numerical simulation of S-CO₂ centrifugal compressor, and the time cost of numerical calculation further increases the difficulty of compressor design optimization. To establish a flow calculation method suitable for S-CO₂ centrifugal compressor, three-dimensional numerical simulation is carried out on the compressor to obtain the flow field information and corresponding loss distribution. Then, the one-dimensional loss model is superimposed in the conventional flow line curvature method, and the CO₂ compression factor is calculated in segments along the flow line, thus to reflect the real gas compression process. Comparison between the calculation results and the three-dimensional numerical simulation results shows that, the distribution of meridian relative velocity field and enthalpy obtained by streamline curvature method are consistent with the computational Fluid dynamics (CFD) results. The temperature and pressure data at the blade outlet are close to the CFD results, with errors of 0.23% and 1.08%, respectively, and a difference of 1.5% in total static isentropic efficiency. The results indicate that the performance parameters of S-CO₂ centrifugal compressor impeller can be obtained quickly and accurately by using streamline curvature method.

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.

With the advancement of intelligent mining construction, the number of application systems is gradually increasing, and problems such as incomplete data unification, lack of overall management of the system, and repetitive construction of functions are becoming prominent. To avoid these issues, this article takes the Yimin open coal mine data center as an example to analyze the overall architecture of the coal mine data center in detail. Based on the industrial private cloud development platform and big data technology, an open, continuous integration and deployment data center is established, and it supports high load elastic scaling and meets the concept of intelligent open-pit mine management. The data center solves problems such as difficulty in data collection in the mining field, inability to store data uniformly, isolated business systems, and inability to share data conveniently and in a timely manner among mining surveying professionals. It also comprehensively utilizes the collected equipment and system data to achieve real-time control of production status and equipment status, improving management decision-making ability and means.

Chloride ion is the key operating index in desulfurization system of power plants. Rapid and accurate monitoring of chloride ion content in desulfurization slurry is of great significance for anti-corrosion of desulfurization equipment, optimization and adjustment of operation process, as well as desulfurization waste water discharge and water saving. By studying the pretreatment method of sample, a high-efficiency composite medium filter is developed, and the method suitable for the desulfurization system sample pretreatment is studied, and the laboratory test is carried out. At the same time, by studying the principle of online measurement method of chloride ion in desulfurization system, a solid polymer membrane chloride selective electrode is developed, and on-line monitoring technology of chloride ion in desulfurization system of power plant is developed, and the accuracy test in laboratory and field industrial application test are carried out. The results show that, the technology can realize continuous and accurate online monitoring of chloride ion content in desulfurization slurry, reduce the workload of laboratory personnel greatly, and improve the accuracy of chloride ion measurement. It is of great significance to realize the economic and intelligent operation of desulfurization system equipment.

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.

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.

Optimization control strategies for hydrogen production system coupled with photovoltaic and energy storage are studied, and a power allocation strategy based on model predictive control that considers multi-objective optimization problems with game relationships is proposed. Firstly, the architecture of the hydrogen production system coupled with photovoltaic and energy storage is constructdd, and the power balance equation that needs to be met during the operation of the hydrogen production system coupled with photovoltaic and energy storage is clarified. Secondly, a composite algorithm model is established by combining the self-adaptive multi-objective particle swarm optimization algorithm with the MPC algorithm, and three objective functions that consider both alkaline electrolyzer (AEL) and energy storage battery characteristics are provided, then the weight coefficients of the optimal control increment are calculated. Finally, the MPC controller model is constructed using the MATLAB-function module, and the calculated weight coefficients of the optimal control increment are applied to the MPC optimization process, thus the online power allocation for the hydrogen production system coupled with photovoltaic and energy storage is ultimately achieved. Through simulation analysis and comparison with two optimization control methods, it is proven that the proposed method in this paper improves the operational indicators of the energy storage system to a certain extent while reduces the fluctuation of AEL input power, it enhances the dynamic power balance ability of the hydrogen production system coupled with photovoltaic and energy storage.

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.