The denitration efficiency is closely related to the uniformity of flue gas and reductant agent within the selective catalytic reduction (SCR) reactor for the coal-fired unit. Based on the established mathematical model for SCR denitration reaction, a user-defined subprogram is used to couple the multi-component flue gas flow with reaction process. The reliability and effectiveness of the CFD model are verified by comparing the measured and simulated data of the SCR performance of 330 MW level coal-fired units at different loads. According to the hydrodynamics of flue gas and reductant agent together with the chemical reaction process in SCR reactor, a new intensification scheme is proposed by optimizing the structure of deflectors in front of the ammonia injection grids. Furthermore, the effects of operational conditions on emission mass concentration of NO and NH₃ are investigated. The results indicates that, the maldistribution of the incoming flue gas to the ammonia injection grids leads to the poor mixing behavior of flue gas and reducing reagent. However, the denitration efficiency of the SCR reactor can be improved by about 3.37% through adjusting the upstream guiding plate structure and installing the baffle around the flue duct wall. Taking the SCR denitration device in this work as an example, the appropriate molar ratio of NH₃ to NO is 0.94 when the initial NO mass concentration is 650 mg/m³, which could meet with the emission limit for air pollutions of 50 mg/m³ for NO_x and 2.5 mg/m³ for NH₃, respectively.

Through the analysis of the principle of the existing methods for determination the water content in oil and the components of gear oil, as well as monitoring needs of water content in gear oil, it is found that the existing methods can not accurately and quickly detect the water content in gear oil. the new gear oil and running gear oil for wind power was selected as experiment object, through study of selection of test conditions and the accuracy of the results, it is found a method that can accurately and quickly detect the water content in the gear oil. This method is a combination of the Karl Fischer coulometry water analyzer and the heating furnace. With compressed nitrogen or air as the carrier gas, after the carrier gas is dried through the molecular sieve, it cross the sealed test bottle containing 1g oil sample and 1mL n-heptane by the sleeve air needle, Heat the bottle at 150 ℃, and transfer the water in the oil sample to enter the Karl Fischer water analyzer for detection.

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.

A novel hybrid design that combines solid waste plasma gasification, gas turbine, absorption heat pump, and coal-fired combined heat and power plant has been proposed. In the integrated scheme, medical waste is sent to the plasma gasifier and converted to syngas, which is conveyed into the gas turbine system after the necessary treatment. In terms of waste heat utilization of syngas and flue gas, some are used by the absorption heat pump for heating, and the rest are used to heat the feedwater of the coal-fired combined heat and power plant directly. Based on a typical coal-fired combined heat and power plant, the benefits of this system are examined in terms of both thermodynamics and economics. Once the heat supply and the net electricity from coal remain the same, the net power generated by the waste in the hybrid design is 7.47 MW, while the net waste-to-energy efficiency reaches 47.96%. In just 5.23 years, the initial investment in the proposed system is recouped, and in its 25-year lifetime, the system achieves a net present value of 50 362.94 thousand CNY.

With the promotion of China's "carbon peaking and carbon neutral" strategy, thermal power units are more involved in deep peak regulation. Under the conditions of deep peak regulation, the thermal power unit is insufficient in heat storage, and the primary frequency regulation capability decreases, resulting in a large deviation between the unit's primary frequency regulation capability calibrated under the rated operating condition and the actual frequency regulation capability, threatening the frequency security of the power grid. Aiming at this problem, an online estimation method of primary frequency regulation capability of deep peak regulation thermal power units based on LSTM neural network is proposed. The static model of steady-state unit design was improved to a dynamic model, considering the dynamic operation process of the unit by using the time sequence memory ability and nonlinear feature extraction ability of LSTM neural network, and the errors caused by the disturbance factors such as the load changing process and the historical action of primary frequency regulation were corrected. Based on the hierarchical modeling method, the sub-models with different neural network structures were designed for the different characteristics of the factors affecting the frequency regulation capacity, such as heat storage of the unit and steam turbine work performance, and the effects of furnace side were taken into account to improve the accuracy of frequency regulation estimation results. Compared with the traditional method used in the power system, the estimation result of this method has higher accuracy, and has better performance under different working conditions such as steady state and variable load.

In view of the current lack of dynamic modeling and analysis of steam modeling in the integrated energy system of industrial parks, the numerical method needs to balance between accuracy and computational efficiency. Based on the basic equation of steam flow, this paper establishes a mathematical model of the hydrothermal process of steam flow. Hydrothermal model is carried out by using a mixture of analytical and numerical methods, and the thermal model is solved by finite difference method for simulation. Compared with the simulation data of commercial simulation software, the solution error of this method is about 0.43%, and the influence of different solution conditions on the thermal process is explored. It was found that an increase in spatial step size would increase the volatility of the temperature curve. As the inner diameter of the pipeline increases, the temperature transfer slows down and the response speed of the model decreases; The initial pressure does not have a significant impact on the solving process and accuracy of the model.

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%.

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.

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.

In view of the research situation of backpressure extraction steam turbine technology for ultrasupercritical power generation units, the off-design condition calculation model and exergy analysis model of the unit was established, and the influences of steam mass flow, temperature, pressure, steam turbine back pressure and the BEST heat extraction stage on the thermal performance of the unit were analyzed. The exergy efficiency of unit increased first and then decreased with main steam flow rate, reaching the maximum value (52.42%) when main steam flow rate was 750 kg/s. Exergy efficiency of unit increased with the increase of main steam temperature and pressure but decreased with the increase of turbine back pressure. The thermal performance of unit was more affected by back pressure at low load. In addition, the BEST series has a significant effect on the thermal performance of the unit, and the BEST match degree with the system design scheme is achieved when the series is 4, and the thermal performance is optimal. The calculation method and BEST series selection scheme can provide reference for the scheme design and operation optimization of ultra-supercritical units.