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.

Biomass contains a high content of alkali metal elements, which can cause serious slagging problems during the combustion process. Coal gangue is a kind of bulk solid waste, and its resource utilization is an urgent need. In order to solve the problem of biomass combustion slagging and coal gangue utilization, coal gangue was used as an additive and mixed with sunflower straw for combustion. Physical and chemical characterization of the burned ash samples were carried out using thermogravimetric analysis (TG-DTG), inductively coupled plasma spectrometry (ICP-AES), and ash melting point tester. The effects of temperature and ratio on the combustion performance and slagging of mixed fuels were also investigated. The research results show that, during the combustion of sunflower straw, the addition of a small amount of gangue significantly increased the alkali metal content in the ash. More of these metals were converted into high-melting-point silica-aluminates, which were fixed in the ash samples, while the gaseous alkali metal content decreased. As a result, the tendency for slagging during combustion was reduced. At the same time, the sunflower straw reduced the ignition point and burnout temperature of the gangue, thereby promoting its combustion. The synergistic effect was obvious. When the proportion of coal gangue was 20%, the flammability index of the fuel reached 9.36×10^–4%/(min·℃), and the comprehensive combustion characteristics index reached 42.6×10^–7%/(min²·℃³). The combustion performance was optimal, with a softening temperature of 1 470 ℃, and the tendency for slagging was significantly reduced.

In order to solve the problems of difficult, high cost and poor accuracy of state-of-charge (SOC) estimation for vanadium redox flow batteries (VFB), a joint SOC estimation method is proposed, based on forgetting factor recursive least squares (FFRLS) and multiple innovation unscented Kalman filter (MIUKF). The FFRLS algorithm is used to identify the equivalent circuit model parameters of vanadium redox flow batteries online, and the MIUKF algorithm is used for SOC estimation, so as to achieve the purpose of accurately estimating the SOC of vanadium redox flow batteries. Finally, a 5 kW/30 kW·h vanadium redox flow battery is taken as experimental platform to verify the method. The experimental results show that, compared with the RLS-UKF algorithm and FFRLS-UKF algorithm, the FFRLS-MIUKF algorithm has lower mean square error and root mean square error in the charging and discharging phases, which are 0.003 7, 0.060 9 and 0.001 3, 0.036 3.

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.

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.

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.

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 understand the inner wall temperature distribution characteristics of boiler heating surfaces during fast peaking operation, this study incorporates the coupling of non-uniform heat flux distribution on the combustion side with a multi-tube flow-resistance model on the working fluid side. This corrects the resistance and mass flow distribution among the rows of tubes in the superheater, forming a comprehensive heat transfer calculation model that couples the non-uniform heat flux on the flue gas side with the actual flow rate on the working fluid side, allowing for more accurate prediction of superheater temperatures. This model is applied to the calculation and analysis of wall temperature characteristics of a 660 MW counter-flow coal-fired boiler’s screen superheater at various loads and swirl angles. The study reveals that during deep load-following, the highest tube wall temperature at 30% load (868.4 K) exceeds that at 50% load (861.9 K), approaching the maximum temperature the material can withstand. The tube wall temperature at 50% load is higher than that at 75% load (849.7 K). Additionally, changes in swirl angle significantly affect the non-uniform distribution of flue gas and the working fluid temperature on the steam side. When the swirl angle is 45°, the high-temperature zone is primarily distributed at both ends of the tube screen. When the swirl angle is 15°, it concentrates in the middle front and middle rear regions. As the swirl angle increases, spatial heterogeneity of the flue gas field in the furnace enhances, leading to greater temperature non-uniformity across the superheater width. The research results can provide technical support for the design and retrofit of boilers during rapid load-following processes.

The rapid and comprehensive determination of coal quality is of great significance for the optimization of boiler combustion and the digital transformation of coal-fired power plants. Laser-induced breakdown spectroscopy (LIBS) has the potential to be applied effectively in the rapid determination of coal quality. In order to meet the application goal of rapid coal inspection, 46 sets of spectral data of coal samples from different power plants were collected by the experimental device of coal particle flow LIBS, and the research of simultaneous rapid inspection of multiple indicators of coal quality by combining LIBS with machine learning was carried out systematically. In view of the considerable spectral fluctuations observed in the particle flow state, the number of single-pulse acquisitions was optimized. In addition, invalid spectral screening, spectral averaging and spectral normalization data preprocessing methods were established. Furthermore, four machine learning algorithms (PLSR, SVR, PSO-SVR, and LSTM) and four spectral feature inputs (full spectra, eigenbands, intensity integration, and PCA extraction) were compared in terms of their performance in predicting multiple indicators of coal quality. The results demonstrate that the uncertainty of the spectral signals can be maintained at a maximum of 5% when 200 single-pulse spectra are collected for spectral averaging in a single test. The PSO-SVR algorithm exhibits the most optimal prediction performance in the quantitative analysis of coal quality indicators, and the PCA algorithm reduces the dimensionality of the spectral data, which reduces the amount of model computation and at the same time improves the prediction performance of the model, and the model established by combining both of them has the best performance, the root mean square error (RMSEP) of the coal heat content is 0.289 MJ/kg, and the mean absolute error (MAE) is 0.231 MJ/kg. The coal carbon mass fraction, ash content and volatile matter content are also predicted satisfactorily, with the RMSEP of 0.987%, 1.310% and 1.612%, and the MAE of 0.839%, 1.014%, and 1.033%, respectively. The results show that, combined with appropriate machine learning algorithms, the LIBS technique can achieve simultaneous accurate and rapid determination of multiple indicators of coal quality, which has a broad application prospect in the scenario of efficient and clean coal utilization.

In China, Xinjiang province has vast reserves of high alkali coal resources. However, in coal-fired boilers, fouling and slagging on heating surface caused by alkali metals significantly limit the efficient utilization of the coals. Based on gas-phase alkali metal detection, combined with flue gas temperature monitoring, heat transfer calculation and other methods, slagging monitoring was carried out on the heating surface of a tangentially-fired boiler burning high alkali coal. The influence of air distribution on flame temperature, gas-phase alkali metal mass concentration and heating surface heat transfer in the furnace was analyzed, and a quantitative relationship between the gas-phase alkali metal mass concentration and the heating surface heat transfer was established. Preliminary monitoring of fouling and slagging on the heating surface was also conducted. The results indicate that, slag sample on the water wall side was quite different from that on the flue gas side. The covered water wall surface was loose and porous, while the flue gas side was dense and hard black coke. Significant macroscopic differences were observed, with sodium crystalline phases mainly in the form of feldspar. The highest alkali metal concentration were observed in the main combustion zone of the boiler. The higher the ratio of upper to lower secondary air, the higher the temperature and gaseous alkali metal concentration in the furnace. An increase in the average gaseous alkali metal concentration by 1 mg/m³ resulted in a decrease in the heat transfer of the water wall by 0.82×10⁸ kJ at 300 MW on a tangentially-fired boiler burning high alkali coal.