The rapid expansion of the cumulative installed capacity of wind power in recent years has driven the continuous growth of market demand for wind power operation and maintenance (O&M). Lubricating oil for wind turbine gearboxes is an indispensable and crucial component in O&M. The waste lubricating oil for wind turbine gearboxes is classified as hazardous waste due to its harmful components such as heavy metals and degraded additives. Its efficient recycling and harmless treatment are crucial for achieving clean development in the wind power industry. Firstly, based on the characteristics and composition of waste lubricating oil, the variation laws and underlying causes of typical performance indicators during operating are expounded, including kinematic viscosity, pour point, moisture content, particle contamination level, acid value, and elements (e.g. Fe, P, and S). These variations are primarily induced by factors such as external contamination, oxidation at high temperatures and pressures, and additive degradation. On this basis, the latest research progress in current waste mineral oil regeneration processes is systematically summarized, covering mainstream technical routes such as pretreatment, molecular distillation, solvent extraction, and hydrofining. Furthermore, the feasibility and limitations of the aforementioned methods in extending the service life of waste lubricating oil and realizing its recovery and regeneration are analyzed. The identified limitations include issues such as low treatment efficiency, insufficient processing depth, and restricted application. Finally, the future development directions of high-value waste oils represented by synthetic lubricating oil for wind turbine gearboxes are discussed from the perspectives of industrial development, technological advancement and life management. It emphasizes that establishing a classified recycling system, developing green and efficient regeneration processes, and implementing life management will be the key pathways to realizing a circular economy in the wind power industry.

With the expansion and continuous development of the photovoltaic power generation industry, photovoltaic modules will face large-scale decommissioning in the coming years, and the recycling of modules has become an important issue for global energy sustainability and the circular economy system. Decommissioned photovoltaic modules are rich in high-value precious metal elements such as silicon, silver, and aluminum, but also contain toxic substances such as lead and fluorides, which can cause significant harm to the environment and human health if not properly handled. The current development status and decommissioning status of the photovoltaic power generation industry, as well as the structure and main components and values of photovoltaic modules, are summarized. The principles, advantages, and disadvantages of recycling technologies such as physical methods, thermal treatment methods, and chemical methods are analyzed and compared. Moreover, the progress of recycling technologies in China is investigated. Finally, the recycling of waste photovoltaic modules is systematically summarized and prospected, providing a scientific basis and decision-making support for the low-carbon development and resource recycling of the photovoltaic power generation industry.

The resource-recycling of retired crystalline-silicon photovoltaic cells is of great significance for the recycling of materials and the green and healthy development of renewable energy. Currently, most recyclers of decommissioned photovoltaic modules mainly focus on the recycling of aluminium frames, while aluminium resources in crystalline-silicon solar cells have not been effectively recycled, leaving valuable silver within the cells. Therefore, a wet-recycling process for preparing silver powder was proposed, which involves leaching aluminum with liquid alkali, leaching silver with nitric acid, and liquid-phase reduction. By optimizing the experimental parameters, under the conditions of a sodium-hydroxide concentration of 1.0 mol/L, a temperature of 20 ℃, a liquid-to-solid ratio of 10:1 (mL/g), and a reaction time of 40 min, the leaching rate of aluminum from the retired crystalline-silicon photovoltaic cells reached 98.88%, and the leaching-loss rate of silicon was only 0.46%. Under the conditions of a nitric-acid concentration of 4.0 mol/L, a temperature of 70 ℃, a liquid-to-solid ratio of 5:1 (mL/g), and a reaction time of 60 min, the leaching rate of silver reached 98.00%. Using ascorbic acid as a reducing agent, silver powder with a purity of over 99.9% was successfully prepared under the conditions of a molar ratio of ascorbic acid to silver ions of 1.25:1.00, a temperature of 30 ℃, and a stirring speed of 250 r/min. This process achieves high recovery rates and product purity, offering a practical pathway for crystalline silicon solar cell wafers recycling.

The efficient valorization of CO2 and N2 from power plant flue gas represents a critical pathway toward advancing the low-carbon transition of energy systems. Electrochemical synthesis of urea by directly coupling CO2 and N2 in flue gas under ambient conditions offers dual benefits: converting greenhouse gases into high-value fertilizers while achieving carbon mitigation and resource recycling. This review systematically summarizes recent advancements in this field, highlighting optimized reaction pathways through novel catalyst designs such as heterojunction catalysts and conductive MOFs, which enhance the synergistic activation of N2 and CO2 and improve C-N coupling efficiency, achieving a record Faradaic efficiency of 48% for urea production. Future breakthroughs should focus on developing bioinspired catalytic materials, integrating photo-electrocatalytic systems, and innovating renewable-powered integrated processes for carbon capture-conversion-product separation. By analyzing technical principles, engineering challenges, and industrial linkages, this work underscores the pivotal role of electrocatalytic urea synthesis in coordinated carbon-nitrogen management for coal-fired power plants, providing a forward-looking strategic thinking for scaling up this transformative technology.

To address the intermittent and unstable power output issues in hydrogen production from renewable energy sources such as wind and solar power, it is crucial to achieve the optimal configuration of green power hydrogen production equipment. The discrete combinatorial optimization algorithms and multi-objective shuffled frog leaping algorithms are study introduced to conduct optimization research on the planning of parks with pure photovoltaic, pure wind power, and photovoltaic-wind power hybrid systems for renewable energy generation. Models of electrolyzer system efficiency, operating power, cost, and capacity are constructed. The results show that in a hybrid system with a photovoltaic capacity of 2.60 MW and a wind power capacity of 3.80 MW, the lowest hydrogen levelized cost is 17.83 yuan/kg, and the full-load operating hours of the electrolyzer are approximately 3 400 hours. After optimization by the multi-objective shuffled frog leaping algorithm, the optimal configuration is a photovoltaic capacity of 1.50 MW and a wind power capacity of 0.55 MW, with a maximum hydrogen production of 2 949.62 kg. The photovoltaic-wind power hybrid system can not only reduce the hydrogen levelized cost but also increase the full-load operating time, providing a theoretical reference for the scientific planning of hydrogen production from renewable energy in the future.

The existing flame radiation image temperature measurement technology has measurement errors due to the coking problem of the detector lens. There is an urgent need for an online monitoring method that can intelligently eliminate the coking interference. An online monitoring method for the temperature field of power station boilers that integrates flame radiation images and convolutional neural network (CNN) is proposed. Firstly, the detector is calibrated via a blackbody furnace, and the relationship between the monochromatic radiation intensity of the detector and the image intensity is established. Secondly, a CNN model suitable for flame image processing is designed, and the training set is constructed by using the non-coking flame radiation intensity images of the boiler collected on-site to establish the flame radiation intensity image restoration model. Finally, the measurement accuracy of this method is verified by using the simulated coking flame images. The results show that the temperature measurement accuracy decreases with the reduction of the number of training sets. When the number of flame images in the learning set is 3 000, the relative error of temperature measurement is 1.4%. The temperature measurement accuracy decreases as the coking area increases. When the coking area is 30%, the maximum relative error of temperature measurement is 0.7%. Furthermore, studies show that when the model of the detector trained by the learning set calculates the coked images of other detectors, the temperature measurement error will increase, with the maximum relative error reaching 34.6%. This indicates that when applying this method, the detectors of each burner need to be trained separately. The proposed method can intelligently eliminate the interference of coking on the flame radiation image, achieve high-precision online monitoring of the temperature field, and provide reliable technical support for the safe operation and combustion optimization of power station boilers.

With the continuous expansion of the scale of urban heating network systems and the sustained growth of intelligent demands, the conventional centralized control systems based on the single-controller mode have gradually exposed technical bottlenecks in terms of computing power support, system fault tolerance, equipment compatibility, and deployment costs, and have been unable to meet the application requirements of multi-domain collaboration and intelligent optimization control. To this end, a new type of redundant computing engine for multi-domain collaborative control of the heat network is proposed. A hierarchical architecture is adopted and the computing engine is decoupled into two major modules: the management program and the kernel program, achieving the separation of overall management and core computing functions. Through the task hierarchical management and coordination scheduling mechanism, the efficient collaboration of periodic and aperiodic mixed computing tasks has been achieved, improving the operational efficiency and real-time response capability of the system. Adding a redundancy mechanism and proposing a hot standby redundancy synchronization scheme have improved the reliability and stability of the system in high-load scenarios. A unified system supporting the flexible access of multi-language heterogeneous intelligent algorithms has been constructed. Through the dynamic loading and interface mapping mechanism, the two-way interaction between graphical configuration and the underlying algorithm code has been achieved. This computing engine effectively enhances the deployment flexibility and execution efficiency of intelligent algorithms, improves the stability and reliability of system operation. In actual deployment, the loading success rate of intelligent algorithms is 100%, the primary and backup switching time is ≤200 ms, and at the same time the conventional hardware deployment cost is reduced, providing a high-performance and low-cost solution for the intelligent transformation of industrial control systems. Furthermore, it has the potential to be promoted in multiple fields such as energy and transportation.

To address the challenges of meteorological-power response inaccuracy, difficulty in capturing abrupt features, and data scarcity in photovoltaic power prediction under extreme weather conditions, a hybrid prediction framework is proposed based on fuzzy C-means (FCM), maximum information coefficient (MIC), time variational auto-encoders (TimeVAE), 1D convolutional neural network (1DCNN), and simple-Mamba (S-Mamba). Firstly, meteorological features are clustered using FCM to categorize weather into four types: sunny, cloudy, snowy, and rainy. Subsequently, MIC is employed to select the optimal subset of meteorological features. To mitigate the scarcity of extreme weather samples, TimeVAE is adopted for data generation, leveraging its decomposed reconstruction mechanism to synthesize realistic time-series data. Finally, a 1DCNN-S-Mamba combined model is utilized, where 1DCNN captures short-term abrupt features through local convolution, while bidirectional state-space modeling in S-Mamba enables long-range dependency analysis for prediction. Experimental results demonstrate that the proposed model enhances both timeliness and accuracy in PV power prediction under complex weather conditions. Compared to S-Mamba, it reduces the mean absolute error (MAE) and root mean square error (RMSE) by 3.65% and 5.10%, respectively, in snowy weather scenarios.

Co-firing sludge is one of the important approaches to address the challenges of urban sludge accumulation. The field test of co-firing sludge dried by flue gas was conducted in a 350 MW supercritical coal-fired power unit to investigate the effects of blending amount and unit load on system operation and energy consumption. Wet sludge was dried using extracted boiler tail flue gas, with the dried sludge subsequently carried into the furnace for co-combustion. The results show that a positive correlation exists between the wet sludge amount and the temperature/flow rate of drying flue gas. Drying 11 t/h wet sludge required 71 t/h flue gas at 597 ℃. As the sludge blending ratio rose, the boiler thermal efficiency decreased, while the auxiliary power consumption ratio increased. The rise in sensible heat loss in exhaust gas mainly led to the decline in boiler thermal efficiency, and the rises in both unburned carbon heat loss in residue and sensible heat loss in residue were secondary factors. The rise in auxiliary power consumption ratio was primarily attributed to the high power consumption of the sludge drying system, with sludge co-combustion system dominating the rise in auxiliary power consumption ratio. The rise in net coal consumption rate was caused by the decline in boiler thermal efficiency and the rise in auxiliary power consumption ratio, and the rise in auxiliary power consumption ratio contributed more significantly. At 262 MW and with a sludge blending mass ratio of 8.76% (sludge moisture fraction: 83%), the boiler thermal efficiency decreased by 0.260%, the auxiliary power consumption ratio increased by 0.466%, and the net coal consumption rate increased by 2.38 g/(kW·h). The study provides a reference foundation for the energy consumption evaluation and optimization of co-firing sludge dried by flue gas in a coal-fired power unit.

The Brayton cycle-based tower solar thermal power system features a flexible layout and operates at high receiver temperatures. However, fluctuations in solar irradiance can lead to thermal fatigue of receiver materials or excessive surface temperatures, necessitating effective strategies to mitigate temperature fluctuations. This study develops a manganese-based thermochemical thermal protection coating utilizing a reversible redox reaction. When solar radiation intensifies and the temperature exceeds 978 ℃, the coating undergoes a reduction endothermic reaction, reducing the heating rate. Conversely, when solar radiation decreases and the temperature drops below 878 ℃, an oxidation exothermic reaction occurs, slowing the cooling rate, thereby stabilizing receiver surface temperature fluctuations. Experimental results indicate that when the mass ratio of the coating material to the binder is 4:3, the adhesion strength reaches the highest national standard level, and the solar weighed average absorptivity achieves 94.93%. After undergoing 500 hours of thermal aging at 950 ℃, 100 cycles of thermal cycling, and 200 cycles of redox reaction tests, the coating’s weighed average absorptivity decreased by only 0.82, 0.98, and 2.61 percentage points, respectively, while maintaining the highest adhesion strength. Under a sudden change in concentrated solar radiation flux of ±9.7 kW/m², the heating and cooling rates in the first 100 seconds were reduced by 59.66% and 67.09%, respectively. Additionally, the time required for a 20 ℃ increase and decrease was extended by 182.50% and 438.60%, respectively. The manganese-based thermochemical coating demonstrates excellent aging resistance and effectively suppresses absorber temperature fluctuations, making it highly promising for applications in Brayton cycle-based tower solar thermal power systems.