With the increasing demand for flexible operation of power plant boilers, frequent variable-load operation leads to a wide range of fluctuations in pollutant concentrations and flue gas parameters. Modeling of key indicators such as single pollutant or flue gas parameter can no longer meet the actual demand, so it is necessary to consider the coupling of multiple key indicators for synergistic predictive modeling. Based on the historical operation data of coal-fired power plants, feature extraction is performed through kernel function mapping, and a long short-term memory neural network with a hard parameter sharing structure is constructed for multi task prediction modeling. The prediction model is optimized using uncertainty loss methods. The experimental results show that, the proposed prediction model exhibits high prediction accuracy under variable load conditions, and the prediction errors for the key metrics involved in this study are reduced by 25.5%, 41.8% and 4.7%, respectively. The proposed method is capable of predicting several key indicators of utility boilers under variable load conditions, which can assist power plants to achieve pollution control and optimize the thermal efficiency of combustion, and provide technical support for intelligent operation of power plants.

The porous media methodology is used to describe the filter bags and their surface ash layer seepage flow. On this basis, the discrete particle model (DPM), as implemented in Fluent software, is employed to simulate the dynamics of dust particles movement and deposition in a baghouse filter. The effects of dust particle size on evolving morphology of the cake on the filter bags are investigated, and the influence of additional resistance of the filter cake on subsequent ash particle deposition is explored. The results show that, when the duration of dust removal is sufficiently long, the cake thickness and pressure loss in the baghouse filter increase in a linear fashion over time. For a constant dust mass flow rate, the pressure loss becomes higher with smaller dust particles. The smaller ash particles are transported more effectively by the upward airflow to the top of the vertical filter bag, whereas the larger particles, due to their weight, tend to settle in the middle area. This results in significant differences in the distribution of cake thickness. In contrast to conventional algorithms that neglect cake resistance, the proposed model, which incorporates this resistance through advanced development in commercial software, predicts a more uniform distribution of dust thickness, which is more consistent with the actual situations. This conclusion can provide references for improving the operational efficiency of baghouse filters.

The experimental data and simulation studies on ammonia co-combustion in coal-fired power plants in recent years are investigated, with a focus on the effect of ammonia fuel on NO_x generation. Through experimental observation, numerical simulation, and theoretical analysis, the spatial distribution of soot and polycyclic aromatic hydrocarbons (PAHs) during combustion is directly observed using high-speed cameras, laser-induced ignition (LII) method, and laser-induced fluorescence (PAH-LIF) method. It finds out that, the generation of NO_x during ammonia combustion is significantly higher than that of the conventional hydrocarbon fuels. Reasonable design of ammonia nozzles and selection of appropriate injection positions can significantly reduce the NO_x generation. The ammonia blending combustion technology provides a promising approach for achieving low-carbon transformation of coal-fired power plants. Although there are still challenges in the industrial application of large-scale ammonia blending combustion technology, the application prospects of ammonia fuel in coal-fired power plants are broad through continuous experimentation and technological optimization. Future research should continue to focus on the generation and emission of other pollutants during the process of ammonia combustion, and explore in depth the transformation behavior of minerals in coal in the combustion environment, providing theoretical and practical support for the industrialization of ammonia combustion technology.

The hardware equipment and monitoring systems used in various stages of production of renewable energy stations are provided by different manufacturers, resulting in the data format and communication protocols between various business systems can’t be standardized. This data non-uniformity makes the business systems can only run independently of each other, bringing a lot of inconvenience to the operation and maintenance and coordinated control of the field operation and maintenance personnel, and part of the system even falls into the situation of no one maintenance. Based on the data characteristics of renewable energy stations, an integrated monitoring scheme of renewable energy is proposed based on pre-processing data framework. By introducing the architectural design and workflow, a comprehensive data processing driver applicable to the integrated monitoring of renewable energies and data storage technology based on a hybrid architecture is proposed, the implementation of the data acquisition, standardization and storage ideas are described in detail, and application cases is combined to reflect the system performance improvement brought by this solution. This solution can be adapted to renewable energy monitoring needs of different scales, ensure the real-time and security of massive data processing under high concurrency processing scenarios, and significantly improve the data analysis capability of renewable energy stations, providing an important reference for the development and improvement of related systems.

In order to realize cascade utilization of energy in the heating system, in view of the problem that the heat load of industrial users does not completely match the heat consumption for activated carbon regeneration, the steam-air heater has been installed in flue gas desulpherization and denitration demonstration unit for No.2 coal-fired generation unit in a thermal power plant. Steam becomes primary heat source to realize activated carbon regeneration. Steam extracted from the turbine heats circulating hot air in the FGD unit firstly, then supplies remaining heat energy to different terminals outside the plant after desuperheating. Hereby this article thoroughly describes how to select control valve in steam supply system, so as to realize coupling control of heat energy between two different users. The rationality of this design has been verified during actual operation of the demonstration project, and it provides a reference for the selection of control valves in future engineering practices.

It is a common problem that the direct air-cooled unit cannot be fully charged during the high temperature period in summer, and the widely used solution is to install a sprinkler device in the air-cooled unit for humidification and cooling. Through analysis on the air flow field in the cooling unit, it is found that the air flow field in the silo is non-uniform due to the influence of fans and bridges. The method of non-uniform arrangement of nozzles in the air flow field is used to evenly mix the air and spray water, and the high-pressure spray is used to achieve a uniform temperature drop in the overall air field to minimize the temperature of the cooling air. This method effectively solves the problem of cooling and improves the efficiency of air-cooled units in summer, and is of great significance for peak operation of thermal power units in summer.

Supercritical carbon dioxide (S-CO₂) power generation technology offers better flexibility, and its substitution for steam power generation technology in the field of thermal power generation is of significant strategic importance for constructing a new type of power system, establishing a modern energy system, and achieving the “dual carbon” goal. Through numerical simulation and experiment, the flow and heat transfer characteristics of S-CO₂ boilers within the actual operating range are analyzed, and the influence of working fluid flow states and physical properties on heat transfer and resistance performance is also investigated. The results show that, the heat transfer coefficient of CO₂ decreases with the thermal conductivity under the same flow conditions. This is because the thermal resistance of the fluid boundary layer increases as the thermal conductivity decreases. Under the same thermal conductivity conditions, the heat transfer coefficient of CO₂ increases with the Reynolds number (Re). The reason is that the fluid boundary layer becomes thinner as Re increases, reducing the boundary layer thermal resistance. For CO₂ working fluid inside the pipe with pressures ranging from 3 MPa to 30 MPa, enthalpy values of 500~1 150 kJ/kg, and Re between 1.1×10⁵ and 2.1×10⁶, a correlation formula for heat transfer considering boundary layer property corrections is derived. The average deviations are 3.33%, demonstrating it has high precision. The research lays a solid foundation for the design and research of subsequent S-CO₂ boilers.

The power generation by co-firing of coal and biomass is the most economical and efficient technology for existing coal-fired power plants to achieve CO₂ emission reduction and large-scale efficient utilization of biomass. However, due to the significant alkali metal content in biomass, serious issues such as ash deposits, slagging, and corrosion arise during co-firing of coal and biomass, posing substantial threats to safe and economically viable operation of boiler equipment. Comparative analysis on slagging characteristics of heating surfaces in coal-fired boilers, biomass-fired boilers, and co-firing boilers of coal and biomass are conducted, with their influencing factors investigated. The slagging characteristic evaluating indicators for boilers firing different fuels and co-firing boilers are comprehensively discussed and evaluated. Furthermore, the applicability of these slagging evaluation indicators in specific boiler equipment is assessed. A comprehensive analysis reveals that the ash components in the fuel determine the physical and chemical properties of ash residues. The ash fusion characteristics reflect the tendency and temperature of solid-liquid transformation of ash residues, whereas ash viscosity relates to ash flowability and its propensity to deposit as slag on heating surfaces. A comprehensive consideration of these aspects enables a more accurate evaluation of boiler heating surface slagging characteristics.

Due to its high parameters and high efficiency, ultra supercritical units have become a powerful support for deep frequency regulation, peak shaving, and suppression of power grid fluctuations. The optimization and transformation of control strategies for ultra supercritical units are of great significance for the safe and stable operation of the power grid. Aiming at the optimization problem of coordinated control system for ultra supercritical units, an intelligent control strategy based on error self-disturbance rejection control strategy and reinforcement learning algorithm is proposed. Firstly, in framework of the error-based self-disturbance rejection control strategy, the controlled object model of the machine furnace coupling process is simplified according to operating characteristics of the unit’s turbine-boiler coupled process, and an extended state observer is designed to estimate and compensate for the unmodeled dynamic characteristics and external disturbances of the unit in real time. Secondly, a reward function is constructed and the flexible actor-critic algorithm is used to achieve self-adaptive adjustment of controller parameters. Finally, the effectiveness of the proposed control strategy is verified through simulation based on actual historical operating data of a certain ultra supercritical 1 000 MW secondary reheating unit.

To achieve simulation validation for the control software and hardware platform of a gas turbine control system, the co-simulation method based on real-time and virtual environments is studied. A real-time simulation hardware platform is built using a real-time simulator, signal conditioning devices, and fault injection devices. Additionally, a detailed simulation model of the gas turbine and fuel system is developed based on multi-domain physical modeling methods, taking into account dynamic factors such as thermal soak effects, volume effects, and rotational inertia. A virtual simulation environment is constructed using a virtual controller for the control system, and logic modeling methods are used to create simulation models for auxiliary systems such as the lubrication and electrical systems. Signal interaction between the real-time simulation platform, virtual simulation platform, and control system hardware platform is achieved through hardwiring and communication methods, enabling integrated co-simulation operation. The results show that, the co-simulation method, combining real-time and virtual environments, not only provides a lightweight simulation environment for the gas turbine control software, encompassing all critical link elements, but also allows for functional and performance testing of the control logic. Moreover, it offers a validation environment for the control system hardware platform under various gas turbine operating conditions, enabling functional and performance testing of the hardware in multiple operational scenarios. This research can be applied for integrated validation of both software and hardware in gas turbine control systems, supporting the development of domestic gas turbine control systems and the retrofitting of control systems in existing units for domestic applications.