The anhydride-cured epoxy resin can be made into structural parts such as wind turbine blades and girders by pultrusion process. After crosslinking, it is difficult to degrade and recycle, which is one of the difficulties in the treatment of wind turbine blades. Methyl tetrahydro phthalic anhydride is a kind of anhydride curing agent. The effects of different liquid organic amines, different catalysts and different process conditions on the degradation of epoxy resin block cured by this anhydride were studied, and the best combination of degradation solution and degradation catalyst and degradation process conditions were determined, which realized the rapid degradation of anhydride cured epoxy system. At the same time, the degraded products were extracted and separated, and detected by infrared ray (IR) and nuclear magnetic resonance (NMR), and the main components of the recovered products were determined. Moreover, the degradation mechanism of the anhydride-cured epoxy resin by liquid organic amine was deduced, which provided a new idea and beneficial exploration for chemical recovery and reuse of the anhydride-cured epoxy resin.

The signals of flame spontaneous emission (passive method) and absorption spectrum (active method) are two commonly used optical measurement methods for reconstructing the combustion physical field. Developing an active and passive combined method by combining the respective advantages of the two methods will provide a new means for combustion detection. By introducing a laser absorption optical path into the passive measurement system to simultaneously obtain the spontaneous emission and absorption spectral signals of the flame, the combustion temperature field and the initial component concentration field reconstructed by the passive method are introduced into the active method reconstruction. The active and passive combined method is developed by combining the double regularization constraints of smoothness and the prior concentration physical field. Simulation reconstructions are carried out for typical single-peak and double-peak axisymmetric flame sections. When the measurement error is 1.00%, the average errors of the single-peak and double-peak flame combustion temperature field reconstructions are 0.92% and 1.32% respectively, and the average errors of water vapor volume fraction are 3.05% and 3.31% respectively. The results show that under the double regularization constraints, the reconstruction accuracy of water vapor concentration by the active and passive combined method is significantly improved compared with the passive method, and the number of required laser optical paths is greatly reduced compared with that of the active method, achieving accurate measurement of the combustion temperature field and component concentration field using a simple measurement system.

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 optimize the process parameters for the preparation of regenerated products by mixing the powder of waste wind turbine blades with high-density polyethylene (HDPE) and remolding, the Six-sigma DoE method was used to design the experiments to study the effects of various process parameters such as barrel temperature in each zone, confluence core temperature, mold temperature, and main engine speed on the performance of regenerated products. The flexural strength and compressive strength were used as the evaluation indexes of the performance of regenerated products, and the optimum remolding process preparation parameters were obtained combined with the given matching value. The experimental results show that when the flexural strength of 44 MPa and the compressive strength of 31 MPa are selected as target matching values, the optimal process parameters obtained by maximizing the preferred parameters are: front zone barrel temperature of 189 ℃, middle zone barrel temperature of 179 ℃, rear zone barrel temperature of 168 ℃, confluence core temperature of 158 ℃, mold temperature of 156 ℃, and main engine speed of 4.0 r/min. Under this process condition, the flexural strength of the regenerated product is 43.15 MPa, and the compressive strength is 30.73 MPa.

As China’s installed capacity of wind power and photovoltaic systems firmly ranks first globally, the first batch of large-scale commissioned equipment is approaching centralized decommissioning. It is estimated that by 2030, the cumulative decommissioned photovoltaic modules will reach 1.5 million tons, and the decommissioned wind power equipment capacity will reach 2 million tons. Through a systematic analysis of the current status of resource utilization of decommissioned wind and photovoltaic equipment in China, and by integrating international typical experiences such as the EU’s WEEE Directive system, the technology innovation-driven model in the United States, and Japan’s forward-looking legislative practices, this paper, in view of the problems existing in China’s standardization development, industrial chain coordination, and regulatory systems, proposes to adhere to the principles of systematicness, coordination, adaptability, and operability, and constructs a four-tier standard framework comprising basic general standards, specialized technical standards, management specifications, and testing certification standards. Moreover, specific policy recommendations are presented from four aspects: standard development, policy coordination, regulatory mechanisms, and international cooperation.

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.

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.

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.

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.

Floating offshore wind turbines (FOWTs) are affected by various factors in the marine environment, which can significantly alter their motion states and thereby affect their power generation performance. The influences of ocean currents on motion response of the FOWTs under different wind and wave conditions are investigated, and their impacts on power output are also studied. A semi-submersible floating platform equipped with a 5 MW wind turbine is selected as the research object, and simulations are conducted using OpenFAST, FAST to AQWA (F2A), and AQWA software. The surge, heave, and pitch motion responses of the floating platform, as well as the power generation output, are calculated under three environmental conditions: steady wind with regular waves, steady wind with irregular waves, and turbulent wind with irregular waves. The motion responses and power generation output are then recalculated after the incorporation of ocean currents. The results indicate that ocean currents primarily have a significant impact on surge motion, with a maximum difference of up to 13%, while their effects on heave and pitch motions are relatively minor. In the calculation of power output under the three environmental conditions, ocean currents show no influence on the average or maximum power generation output of the wind turbine, with differences of less than 1% between cases. Additionally, under more complex conditions, the standard deviation of power generation output exhibits minimal variation, suggesting that ocean currents do not affect the fluctuation intensity of power output.