In order to verify the feasibility of blending a large proportion of ammonia in a coal-fired unit, a numerical simulation model was established using a 660 MW wall type tangentially-fired boiler as an example. The impact of ammonia blending ratio, ammonia injection location, and primary and secondary air distribution methods on combustion in the furnace was analyzed. Results show that blending ammonia in coal-fired boiler leads to a decrease in flue gas temperature and an increase in NO_x mass concentration in the furnace. The greater the ammonia blending ratio, the more significant the decrease in flue gas temperature and the increase in NO_x mass concentration. When the ammonia blending ratio increases to 40%, the average flue gas temperature in the furnace drops by approximately 50 K, and the outlet NO_x mass concentration increases by 29.4%. Measures such as blending ammonia in higher-level burners and increasing the primary air ratio can reduce the NO_x mass concentration at the furnace outlet while ensuring complete combustion of ammonia and coal.

Hydrogen-blended gas turbines represent an innovative energy conversion technology integrating hydrogen with electricity. Developing a dynamic model for these turbines is crucial for understanding hydrogen blending characteristics and precisely adjusting control parameters. Based on mechanism modeling method, detailed component models for the compressor, combustion chamber (for hydrogen and natural gas mixtures), and turbine were established. Using transfer learning methods, models for hydrogen-blended combustion efficiency and other gas turbine component characteristics were developed, which were then integrated to form a thermodynamic dynamic model of hydrogen-blended gas turbines. The model's steady-state and dynamic validation was conducted using on-site operation and test data of a small F-class hydrogen-blended gas turbine. The results demonstrate that the constructed model is not only applicable for simulating gas turbines using hydrogen-blended natural gas fuel, but also for simulating load increase and decrease during dynamic hydrogen blending ratio adjustments. It can provide an accurate simulation model for research on hydrogen blending ratio control strategies for gas turbines.

In order to analyze the flow field, flame structure and emission patterns in the secondary zone of axial staged combustion of methane and ammonia fuels, a high-frequency particle image velocimetry (PIV)analysis and OH^*-based self-luminous techniques were used to investigate the secondary flow field structure, reburning flame morphology and pollutant emission characteristics of the premixed secondary burner with a jet angle of 135° at a momentum flux ratio of 6 using different types of fuels under atmospheric pressure conditions. Results show that when the jet angle is 135°, the secondary flow field will form a large low-speed recirculation zone downstream of the jet outlet, which is conducive to stable combustion of fuels with slower flame propagation speed. When methane mixed with ammonia is used as the secondary fuel, the position of the reburning flame shifts upstream, resulting in a longer high temperature chemical residence time. At the beginning of ammonia-doped combustion, when the ammonia-doped ratio increases, the jet flame transforms from a continuous flame to a disintegrated flame. The reaction zone of the ammonia jet flame is elongated, leaving a heavy sufficient reaction distance to reduce emissions. Therefore, the emission gradually decreases in the detached flame form.

One of the main factors causing unstable flow in the turbine cascade channel is the propagation of upstream blade unsteady wake. The unsteady wake advances the transition from unstable laminar flow to turbulent boundary layer on the surface of turbine blades, leading to an earlier transition. The influence of unsteady wakes on the surface heat transfer characteristics of turbine blades was predicted based on boundary layer transition theory. Firstly, the unsteady wake-induced time-averaged intermittency factor was introduced to correct the turbulence intermittency factor. Secondly, the phase-averaged turbulence intensity was used to predict the onset of transition induced by the unsteady wake. Finally, the turbulent viscosity coefficient generated by the unsteady wake was introduced to correct the effective viscosity coefficient in the laminar region of the blade. Results show that the computational method and improvement for the unsteady wake influence on turbine blade surface heat transfer characteristics have been well validated in relevant experimental systems, significantly improving the accuracy of heat transfer calculations under the unsteady wake influence.

To address the problems of insufficient accuracy and stability of the existing photovoltaic (PV) power prediction models, a PV power prediction model was proposed. This model was based on sky image data, the simple model of the atmospheric radiative transfer of sunshine (SMARTS), and the LK optical flow method. The SMARTS was used to calculate the clear sky irradiance and solar position information at a specified time, and the cloud motion vectors were obtained through the LK optical flow method. The cloud motion vectors were used to infer the shading situation of the cloud to the direct sunlight at future times. Then, the clear sky irradiance and the predicted solar shading situation were used to calculate the final power prediction result. The model was validated using the sky image dataset SKIPP'D. In a sunny environment, the proposed method was compared with the Bird model and the Ineichen model. In a cloudy environment, the proposed method was compared with the long short-term memory (LSTM) neural network model and the convolutional neural network (CNN) model. The effectiveness of this method in short-term PV power prediction has been verified. Results show that, the proposed method can accurately capture the effect of cloud changes on the PV power generation in a cloudy environment, the determination coefficient R² of the model is greater than 90%, and both the prediction accuracy and stability are significantly better than those of the control models.

In order to explore the influence of environmental factors on the performance of photovoltaic/thermal (PV/T) collector with binary tree-shaped cooling channels, based on the energy conversion process of PV/T collector, a theoretical model of system heat transfer was established, the temperature distribution law of PV module surface and cooling fluid was studied, while the influence of environmental factors on the system electrical efficiency was analyzed. Results show that, for every 100 W/m² increase in irradiation intensity, the surface temperature of PV module is increased by about 1.95 K, the system electrical efficiency is decreased by about 0.13 percentage points, and there is a significant difference on the temperature distribution of cooling fluid. As the inclination angle increases from 15° to 60°, the irradiation intensity on the PV module surface shows an upward convex curve trend, rising from 829.26 W/m² to 870.12 W/m² at first and then decreasing to 824.13 W/m², and reaching its maximum value at around 35°. Meanwhile, the average temperature of surface is consistent with the change in the irradiation intensity received by the surface. But the system efficiency shows a downward concave curve trend, reaching its minimum value at around 35°. For every 5 K increase in ambient temperature, the external radiation temperature is increased by about 7.2 K, the average temperature of PV module surface is increased by about 0.5 K, and the system electrical efficiency is decreased by about 0.04 percentage points. As the ambient wind speed increases from 0 m/s to 2 m/s, the variation of system electrical efficiency is only within 0.1 percentage points. However, the changes of ambient wind speed and temperature have no significant impact on the heat dissipation effect of PV module.

Isothermal compressed air energy storage (ICAES) technology does not require heat storage, and the system structure is simple and theoretically efficient, but it is very difficult to realize isothermal compression and expansion. The variation of air temperature can be effectively reduced by liquid spray technology, which makes the compression and expansion process closer to the isothermal process. Therefore, the thermodynamic model of an ICAES system based on liquid spray was developed, and the effect of liquid spray parameters on the thermodynamic properties of the system was calculated and analyzed. The results show that the variation of air temperature can be significantly reduced by the liquid spray technology, with the temperature variation during compression reduced from 49.58 K to 9.85 K, and the temperature variation during expansion reduced from 37 K to 12.03 K. The energy loss in the liquid piston is reduced from 14.32% to 4.43%, the cycle efficiency of the system is increased from 62.11% to 76.60%, and the energy storage density is increased from 1.857 MJ/m³ to 3.473 MJ/m³. The results of this paper can provide a reference for the optimization of the structure and parameters of the ICAES system.

In order to fully implement effect of the energy shifting of energy storage on the consumption of renewable energy, a combined operation optimization mathematical model of wind-solar-fossil fuel-storage system was investigated. The model was utilized to deal with the configuration and optimal scheduling of compressed air energy storage equipment in a circular economy industrial distinction in western China. Optimal storage capacity configuration and hourly operation strategy throughout the year were obtained. The annual operating cost, renewable energy absorption rate, as well as typical daily charging and discharging scheduling of energy storage under four different scenarios were analyzed. The results show that the collaborative participation of compressed air energy storage and effective guidance of environmental policy market can increase the renewable energy absorption rate by more than 6% and reduce the total annual operation cost by more than 2.6%.

To address the requirements of the new power system for the automatic generation control (AGC) regulation performance of cogeneration units,a fast load-changing control strategy integrating electro-thermal coordination and energy balance was proposed. First, the heating extraction regulation was adopted as the main control loop of the power generating load of the unit. Then, an electro-thermal load conversion signal was introduced into the steam turbine's main control system and the integrated response circuit of electro-thermal energy on the steam turbine side was designed. Finally, a precise energy balance control loop on the boiler side was designed. A simulation verification was conducted using 300 MW unit as an example. Results show that with the proposed coordinated control strategy, the initial load-change rate is increased to 5.3% of the rated load per minute, and the overall rate is raised to 2.6% of the rated load per minute. The AGC comprehensive performance indicators of the unit have been improved by 2.2 times compared to traditional coordinated control strategy. At the same time, the heat load deviation is less than 0.15%,ensuring the reliability of heating effectively.

Concerning the problem of high-order large inertia and multiple disturbance in superheated steam temperature system, a linear active disturbance rejection control based on BP neural network assisted by lead compensation model was proposed. Based on second-order linear active disturbance rejection control, a feedforward compensator and a lead compensator were connected in series in the feedforward and feedback paths, respectively, to solve the problems of asynchrony of feedforward and feedback signals and delay of disturbance response in low order linear extended state observers. On this basis, according to the expression of closed-loop system transfer function, the steady-state system performance and the stable domain of closed-loop parameters were analyzed, the parameter adjustment interval was derived, the parameter tuning process was simplified, and BP neural network was further used to obtain the optimal bandwidth. Finally, the proposed control strategy was applied to superheated steam temperature control system and compared the performance with various controllers. Results show that the proposed control strategy has significant advantages in fixed value tracking, disturbance resistance, and robustness for high-order large inertia systems such as superheated steam temperature system.

In order to mitigate the bus voltage fluctuations in flywheel energy storage system during operational mode transition, an improved active disturbance rejection control strategy based on a cascade extended state observer was proposed. The conventional voltage-current loop control was integrated into a single voltage loop control, and the total disturbance experienced by the system during operational mode transition was introduced as a new state variable. A cascade extended state observer was designed to observe and compensate for the control quantity with the new state variable. By implementing real-time observation and compensation of the disturbance, the contradiction between the rapid response and overshoot characteristics of traditional PI control was mitigated. Results show that the proposed strategy significantly enhances the voltage regulation performance during various operational mode transitions compared with conventional active disturbance rejection control. The transition from test operation to charging phase and that from charging to discharging phase both achieve zero overshoot, with the adjustment time reduced by 34.7% and 68.4%, respectively.

In order to solve the problems of rolling bearing unbalance sample diagnosis, a diagnostic method integrating conditional generative adversarial networks with multi-scale attention mechanism convolutional neural networks was proposed. By using this method to fill the unbalanced samples, the best generated samples were screened through fractal box dimension. The multi-scale attention mechanism convolutional neural network was input to complete the fault feature extraction. Then it was compared with the original unbalanced data and the traditional sample filling methods. Results show that the method has a high recognition accuracy, can expand the small sample fault data, and solve the problem of bearing fault classification under the unbalanced data effectively.

In order to improve the efficiency and accuracy of gear fault identification under variable speed conditions, an intelligent fault identification method of variable speed gear, namely INGO-CSA-LSTMN, was proposed based on improved northern goshawk optimization (INGO) algorithm to optimize convolutional self-attention long short-term memory network (CSA-LSTMN). In response to the problems that the traditional northern goshawk optimization algorithm taking too long training time and being easy to fall into local optimization, an INGO algorithm was proposed by introducing sinusoidal pulse modulated chaotic map and random Levy flight strategy, and it was applied to optimize the key parameters of the CSA-LSTMN model to improve the stability and training efficiency of the model. The validation through test functions demonstrates that the INGO algorithm has a faster convergence speed and can find the optimal solution more accurately. The analysis of gear datasets from two different experimental rigs reveals that compared to other commonly used network models, the INGO-CSA-LSTMN model has higher recognition accuracy for gear faults under different operating conditions, with the accuracy rates exceeding 99.9%.

To investigate the mercury removal effect of calcium bromide in municipal sludge incineration flue gas, mercury removal experiments using calcium bromide were conducted both in the laboratory and at the engineering site. Results show that the addition of calcium bromide can effectively oxidize elemental mercury in the flue gas. At 800, 900 and 1 000 ℃, when the chemical equivalence ratio of bromine to mercury is 2 000, the oxidation rate of elemental mercury can reach 80%. Moreover, the addition of calcium bromide has a minor effect on the emission characteristics of NO_x and SO₂, but it causes the increase in HCl emissions, especially when the chemical equivalence ratio of bromine to mercury exceeds 2 000, the conversion rate of HCl rises sharply. When the chemical equivalence ratio of bromine to mercury is 1 000 and 2 000, the removal rate of gaseous mercury in the flue gas can reach 60% and 73% respectively, and the addition of calcium bromide has no effect on the emission of conventional pollutants (NO_x, SO₂, HCl, and particulate matter) and the operation of the boiler.

Experimental study was conducted on the concentration and evaporation characteristics of desulfurization wastewater in a high-humidity environment, based on a single droplet evaporation experimental platform and a counter-current spray low-temperature flue gas concentration device. The effect of gas humidity, gas velocity, and water quality on the evaporation characteristics of wastewater droplets were investigated. The morphology of the precipitated crystals and the characteristics of chlorine release, as well as the key process parameters affecting the low-temperature flue gas waste heat concentration process during wastewater concentration evaporation were studied. Results show that the low-temperature concentration and evaporation process of the single desulfurization wastewater droplet can be divided into three stages: contraction, expansion, and crystallization. As the humidity of the flue gas increases, the evaporation rate of droplets decreases, the wet-bulb temperature rises, and the final degree of crystallization decreases to some extent. The increase in gas velocity shortens the time of the droplet contraction phase, which is beneficial for accelerating the evaporation rate. The increase in solid content in the wastewater will increase the critical particle size when the droplet contracts to its smallest size, while the overall temperature of the droplet rises. Increasing the flue gas temperature, flow rate, and appropriately increasing the wastewater inlet flow rate are beneficial for improving concentration efficiency.

An investigation was conducted on a 50 kW fluidized bed coal combustion experimental setup to study the effects of two liquid additives on low-temperature SNCR denitrification efficiency and N₂O emission characteristics at different temperatures and additive amounts. Results show that the SNCR denitrification efficiency reaches 35.6% at ammonia to nitrogen oxides molar ratio of 1.5, ethanol to ammonia molar ratio β of 0.8, and temperature of 700 ℃. Additionally, as the amount of ethanol added increases, the N₂O emissions gradually rise, reaching a maximum of 35×10^-6. Within the temperature range of 690 to 850 ℃, when the phenol to ammonia molar ratio α is controlled at 1.0, phenol exhibits the best promotional effect on SNCR denitrification efficiency, which is 38%±4%. The addition of phenol also leads to gradual increase in N₂O emissions.

In order to improve the utilization of renewable energy and carbon reuse, a low-carbon virtual power plant optimization scheduling model with multiple types of thermal power units and power-to-gas was proposed. With the goal of minimizing the economic operating costs in the virtual power plant, the carbon emissions and operating costs of the three different equipment combinations were compared, and finally the wind, solar, fire, biomass, waste incineration, gas boiler, cogeneration, and power-to-gas units were selected for joint operation. Results show that compared with plan 3, the carbon emissions under plan 1 and plan 2 have increased by 237.78% and 4.12%, and the costs have increased by 39.77% and 3.72%, respectively. The proposed operation plan not only achieves efficient utilization of renewable energy, but also reduces carbon dioxide emissions, realizing the utilization and storage of carbon dioxide.

To address the issue of strong heat-power coupling of the unit with low-pressure cylinder zero-output, and reduce the frequent switching of the unit between the extraction-condensation and zero-output modes, the operation mode of the unit integrated with the heat storage tank was proposed. With a 600 MW unit as the research object, the thermodynamic model of the unit with low-pressure cylinder zero-output was developed based on the Ebsilon software, and the heat storage tank was coupled in the model. The changes of the feasible heating domain, energy utilization efficiency and exergy efficiency of the unit in different operation modes were comparatively analyzed. Results show that the integration of the heat storage tank within the low-pressure cylinder zero-output unit can significantly broaden the heat-power feasible operation domain of the unit, and the maximum heat supply capacity is increased from the 799.28 MW to 1 016.35 MW. The deep peaking capacity of the unit is increased by 122.38 MW when the base heating load is supplied. The unit's energy utilization efficiency and exergy efficiency are positively correlated with the boiler load and the heat release power of the heat storage tank, whereas the heating power presents different influence laws on the energy utilization efficiency and exergy efficiency of the unit. The heat storage and release power of the heat storage tank has a greater influence on the energy utilization efficiency and exergy efficiency at lower boiler loads.