[Objective] This study aims to solve the problems of easy failure of back pressure valve seals and poor stability of wide pressure difference pressure regulation in supercritical carbon dioxide closed cycle systems. 

[Methods] In this paper, sealing failure and pressure regulation characteristics tests were carried out to compare the service performance of two types of rubber seals, quantify the valve pressure regulation law and dynamic response, and provide experimental basis for sealing material selection and stable operation of high-pressure S-CO2 back pressure valve. Based on the self-built closed S-CO2 cycle experimental platform, the step-by-step pressure rise, pressure stabilization and pressure relief cycle tests were completed in the range of 5~30 MPa. The macroscopic damage morphology of nitrile rubber (NBR) and hydrogenated nitrile rubber (HNBR) O-ring after service was compared. The pressure before and after the valve, the temperature of the outer wall and the number of handwheels were collected synchronously. The linear relationship of pressure regulation, dynamic pressure fluctuation, pressure rise and fall hysteresis and throttling thermal effect were analyzed. The repeatability of pressure regulation was tested four times under the condition of 30 MPa. 

[Results] The NBR sealing ring produces bulges, cracks and holes under the action of high pressure CO2 infiltration, swelling and rapid decompression, and the failure mechanism is explosive decompression failure. The HNBR only swells slightly, the sealing surface is intact, and the ability to resist decompression failure is stronger. The pressure in front of the valve is approximately linear with the number of handwheel rings, and the adjustment slope is 3.56 MPa/ring. The pressure behind the valve fluctuates significantly under the high pressure difference of 30 MPa, and the maximum temperature difference of the outer wall of the valve body is 15.5 °C. When the pressure is increased, the pressure behind the valve goes down for a short time, and rises instantaneously when the pressure is relieved. There is obvious pressure regulation hysteresis in the process of pressure increase and decrease. HNBR is more suitable for high-pressure S-CO2 sealing conditions, and NBR cannot withstand 30 MPa long-term cycles. The back pressure valve can achieve basic pressure stabilization in the range of 5~30 MPa, and the linear adjustment law can be used to predict the opening of the project. The unsteady flow of the fluid under high pressure difference, the friction of the valve core and the nonlinear thermal properties of S-CO2 together aggravate the fluctuation and hysteresis. It is necessary to optimize the throttling structure, sealing pair and thermal management scheme to improve the long-term operation reliability of the valve. 

[Conclusion] The research results can provide experimental basis for the selection of sealing materials, structural optimization, and operational stability evaluation of high-pressure S-CO2 back pressure valves.

[Objective] In order to raise the energy utilization efficiency of Carnot battery systems and improve the economic performance of waste incineration power plants, a Carnot battery energy storage system coupled with waste incineration power units is proposed. 

[Methods] First, the system integration scheme and equipment configuration are formulated. Second, a thermodynamic simulation model of the coupled system is established on Ebsilon Professional, and the rationality of each subsystem is verified. Finally, the overall performance of the coupled system is comprehensively evaluated from multiple dimensions, including energy analysis, energy flow, exergy efficiency, economic performance, sensitivity analysis and technical feasibility. 

[Results] Energy analysis shows that the coupled system yields a net power output of 15.435 MW and a net power generation efficiency of 33.18%, an increase of 12.69 percentage points compared with the reference unit. The net power generation from the Carnot battery subsystem is 7.135 MW with a round-trip energy efficiency of 118.9%. The prominent efficiency improvement mainly results from the significant enhancement of main steam and reheat steam parameters as well as waste heat recovery from the condenser, which effectively boosts power output and system energy utilization efficiency. Exergy analysis reveals that the overall exergy efficiency of the entire system reaches 31.63%, growing by 12.23 percentage points against the original unit. Such improvement is attributed to reduced extraction loss of the steam turbine and full recovery of waste heat from the condenser. Economic analysis indicates that the dynamic payback period of the coupled system is 7.39 years, and the project net present value (NPV) hits 290.92 million CNY over a 25-year service life. This result verifies the sound economic viability of the coupled system, and considerable economic returns can be expected in the short term. Sensitivity analysis demonstrates that when the curtailed grid electricity supplied to the Carnot battery is less than 6 MW, the total power output and economic benefits of the coupled system rise almost linearly with the increasing input power. Further growth in input power slows the rising rates of power generation and NPV gradually. The maximum NPV of 292.87 million CNY is achieved at an input power of 6.553 MW. 

[Conclusions] In conclusion, the proposed coupled system remarkably improves the power generation efficiency and energy utilization of the waste incineration generating unit. With a reasonable payback period and attractive economic profits, the system boasts outstanding technical feasibility.

[Objective] The radiators of indirect air-cooling towers in inland nuclear power plants are prone to freezing and cracking under the coupling effect of low winter temperature and crosswind. To solve this practical engineering problem, this study fills the research gap regarding the performance of anti-freeze roller shutters under complex wind conditions. It quantitatively clarifies the correlation between the shutter drop distance and anti-freezing performance, aiming to ensure the safe, stable and economic operation of nuclear power units in winter. 

[Methods] A typical large-scale industrial indirect air-cooling tower for inland nuclear power generation is selected as the core research object in this study. A refined full-scale three-dimensional numerical model of the cooling tower is established and verified through mesh independence analysis based on FLUENT fluid simulation software. Combined with real field meteorological monitoring data, the typical winter temperature and wind field parameters of the plant site are accurately configured. Two representative operating conditions, namely the no-wind static condition and the 4 m/s typical winter crosswind condition, are adopted to systematically simulate and analyze the effects of varying drop distances of anti-freeze roller shutters on the overall outlet water temperature and the spatial water temperature distribution characteristics of each cooling sector of the tower. 

[Results] The simulation results indicate that the drop distance of the anti-freeze roller shutter presents a significant positive linear correlation with the tower outlet water temperature. For the investigated tower type, every 1-meter increase in the shutter drop distance steadily raises the outlet water temperature by approximately 0.5 ℃. Under both no-wind and 4 m/s crosswind conditions, the outlet water temperature exceeds the critical anti-freezing threshold of 20 ℃ when the shutter drops by 36 m, covering about 75% of the total air inlet height. Crosswind severely disturbs the uniformity of the internal flow field, leading to unbalanced heat exchange among different sectors with a maximum water temperature difference of 3 ℃. The 110° windward area and 65° leeward area are identified as freezing-vulnerable sectors. Increasing the shutter drop distance can effectively weaken the wind-induced water temperature attenuation and greatly balance the water temperature distribution across all sectors. 

[Conclusion] Based on extensive numerical simulation data and statistical analysis, empirical formulas for calculating the optimal overall shutter drop distance corresponding to different target outlet water temperatures under the two typical winter wind conditions are derived. A targeted local anti-freezing enhancement strategy is proposed for freezing-prone sectors, which requires an extra 6 m drop distance on the basis of overall unified regulation. The research findings provide a reliable quantitative basis and practical technical support for precise anti-freezing regulation and stable high-efficiency winter operation of indirect air-cooling towers in inland nuclear power plants.

[Objective] In photovoltaic-concentrating solar power-battery energy storage system (PV-CSP-BESS) coordinated frequency regulation, unresolved response sequence coupling, control instruction conflicts, and state-of-charge (SOC) divergence remain critical challenges. This paper proposes a temporal coordinated control strategy based on state quadrant perception and adaptive frequency-domain decoupling to enhance system disturbance rejection capability.  

[Methods] First, an adaptive high-pass filter driven by the rate of change of frequency (RoCoF) is introduced into the BESS control loop to achieve temporal decoupling between transient high-frequency damping and steady-state low-frequency support. Second, to address long-term steady-state bias, a non-symmetric quadrant isolation mechanism based on Sigmoid function is proposed. This mechanism blocks reverse power feedback through the product of frequency deviation and SOC features, effectively preventing SOC drift in BESS. The adaptive time constant of the high-pass filter is dynamically adjusted in real time according to RoCoF magnitude, ensuring that BESS provides enhanced power support during severe transient disturbances while smoothly withdrawing during frequency recovery phases. The quadrant perception module monitors the algebraic signs of frequency deviation and RoCoF simultaneously: when both share the same sign, the system is in a perturbed state requiring immediate BESS intervention; when their signs differ, the system enters the recovery phase and BESS should withdraw to avoid power conflicts with CSP, thereby suppressing reverse power injection.  

[Results] Simulation results demonstrate that under step disturbances and low-inertia conditions, the proposed strategy effectively suppresses system transient extrema, with improvements in maximum frequency deviation and RoCoF reaching 21.6% and 43.2%, respectively, while reducing the risk of CSP main steam pressure violations. Under continuous random fluctuation scenarios, the equivalent cycling mileage of BESS decreases by 85.4%, the depth of discharge converges to 0.007%, and the steady-state SOC drift remains below 0.001%, achieving asymptotic stability. In scenarios where CSP output is constrained, photovoltaic reserve capacity is triggered to compensate for the steady-state active power gap, reducing the integral absolute error of frequency by 32.3%. Comparative analysis reveals that fixed-parameter decoupling control exhibits transient improvement at the cost of overall performance deterioration, whereas the proposed adaptive mechanism maintains robust performance across diverse operating boundaries.  

[Conclusion] The proposed strategy effectively mitigates power response conflicts between transient support and steady-state recovery, enhancing the dynamic complementarity and frequency stability support capability of multi-source heterogeneous equipment under complex operating boundaries. The approach provides a systematic solution for temporal coordination in hybrid renewable energy systems with diverse dynamic characteristics.

【Objective】The effective identification of irreversibility losses in the thermal systems of coal-fired power plants is important for improving energy efficiency and enhancing waste heat utilization. However, in practical operation, the distribution of irreversibility losses is difficult to determine accurately, and the utilization efficiency of waste heat is still relatively low. To address these problems, a waste heat potential evaluation method based on entropy generation and exergy loss analysis is proposed. The method aims to identify the weak links in the thermal system and provide a thermodynamic basis for waste heat utilization scheme selection. 

【Methods】A 660 MW coal-fired power unit and a 300 MW coal-fired power unit are selected as the research objects. Based on the Ebsilon Professional platform, thermal system models of the two units are established according to their heat balance diagrams and operating parameters. The models are verified by comparing the calculated extraction steam flow rates with the design values. Then, entropy generation analysis and exergy loss analysis are conducted for the main heat exchange equipment in the regenerative heating system. Combined with off-design condition analysis, the distribution of irreversibility losses and the potential for waste heat utilization are quantitatively evaluated. 

【Results】The results show that the irreversibility losses of the two units are mainly concentrated in the regenerative heating system. For the 660 MW unit, the main loss sources are the #2 and #3 high-pressure heaters and the #7 low-pressure heater. For the 300 MW unit, the main loss sources are the #2 high-pressure heater and the #7 low-pressure heater. The main reason is the large heat transfer temperature difference between steam and water, which increases heat transfer irreversibility and exergy loss. Based on these results, an energy cascade utilization scheme combining extraction steam preheating and flue gas waste heat utilization is proposed. The scheme improves the temperature matching relationship in the regenerative heating system and reduces irreversibility losses under different operating conditions. For the 660 MW unit, the maximum thermal efficiency improvement is 1.88%, and the maximum reduction in coal consumption rate is 2.50 g/(kW·h). For the 300 MW unit, the maximum thermal efficiency improvement is 0.47%, and the maximum reduction in coal consumption rate is 1.31 g/(kW·h). 

【Conclusion】The proposed method can effectively identify the main irreversibility loss sources in coal-fired power units and provide a thermodynamic basis for waste heat utilization scheme selection. The proposed scheme has good off-design adaptability and can provide guidance for waste heat potential exploration and system optimization in coal-fired power plants.

 [Objective] With the increasing penetration of renewable energy and the growing demand for flexible operation of coal-fired power units, thermal power equipment is experiencing more frequent load variations, deep peak shaving, and complex operating conditions. These changes have introduced new challenges to equipment condition monitoring, fault diagnosis, and intelligent maintenance. Conventional approaches based mainly on fixed alarm thresholds and expert experience are often insufficient for identifying early-stage abnormalities, locating fault sources, and supporting maintenance decisions under variable operating conditions. This paper aims to systematically review the recent progress of artificial intelligence (AI) applications in condition monitoring and fault diagnosis of thermal power equipment and provide insights into the development of intelligent operation and maintenance technologies. 

[Progress] The development and application status of AI-based condition monitoring and fault diagnosis are reviewed from two perspectives: technological evolution and typical equipment scenarios. From the perspective of intelligent methods, the characteristics, application tasks, and limitations of machine learning, deep learning, large language models, and time-series foundation models are analyzed. Traditional machine learning approaches, including principal component analysis, support vector machines, random forests, Bayesian networks, and multivariate state estimation techniques, have demonstrated advantages in small-sample modeling, feature-based diagnosis, and integration with expert knowledge. Deep learning methods, such as convolutional neural networks, recurrent neural networks, autoencoders, Transformers, and graph neural networks, have further improved the capability of extracting nonlinear and high-dimensional features from multi-source monitoring data, including process signals, vibration signals, frequency-domain features, time-frequency representations, and images. Recently, large models and foundation models have extended intelligent diagnosis from pattern recognition toward knowledge-enhanced reasoning, time-series representation learning, diagnostic explanation, and maintenance decision support. From the perspective of equipment applications, representative studies on boilers, steam turbines, generators, coal pulverizing systems, fans and pumps, and flue gas desulfurization and denitrification systems are summarized. The major monitoring data sources, typical fault types, and corresponding AI-based diagnostic methods are discussed. Existing studies indicate that intelligent monitoring has gradually evolved from single-parameter alarm systems to multi-source data fusion, online condition assessment, fault prediction, and integrated diagnosis. However, several challenges remain, including false alarms caused by operating-condition changes, insufficient fault samples, limited model interpretability, weak engineering evidence for diagnostic results, and inadequate integration between diagnostic outputs and practical maintenance actions. 

[Prospect] Future research should focus on improving data quality management, operating-condition identification, and cross-condition generalization capability. The integration of equipment mechanisms, operation and maintenance knowledge, digital twins, knowledge graphs, and time-series foundation models is expected to enhance the robustness, interpretability, and reliability of AI-based diagnostic systems. Through mechanism-data fusion, knowledge-enhanced reasoning, and closed-loop integration with operation and maintenance processes, AI technologies can further promote the transformation of thermal power equipment management from passive fault response toward proactive warning, intelligent diagnosis, and predictive maintenance. 

[Objective] To address the issues that wind turbine rotor imbalance fault features are easily submerged by noise under complex turbulent wind conditions, multi-channel independent signal decomposition tends to cause frequency misalignment, and samples of mild and coupled imbalances are prone to confusion. 

[Method] To systematically address these issues, this paper proposes a collaborative feature extraction and noise reduction method that synergistically integrates Multivariate Variational Mode Decomposition (MVMD) and Permutation Entropy (PE). Instead of treating sensor data in isolation, four-channel multi-modal response signals are synchronously acquired, specifically including blade flapwise load, blade edgewise load, fore-aft acceleration, and lateral acceleration of the nacelle. By leveraging the shared center frequency mechanism inherent in MVMD, the proposed approach constrains multiple channels to share the same mode center frequencies during optimization, thereby realizing precise alignment of homologous fault frequencies and preserving intrinsic physical correlations among multi-source signals. Following decomposition, PE is utilized as a nonlinear dynamical measure to quantify the randomness and complexity of each decomposed Intrinsic Mode Function (IMF). An adaptive entropy threshold is applied to intelligently screen valid IMFs for reconstructing high-fidelity denoised signals, effectively amplifying low-frequency fault-sensitive components. Furthermore, comprehensive indicators covering time-domain statistical parameters, frequency-domain characteristics, and spectral distribution features are extracted. Principal Component Analysis (PCA) is subsequently applied to eliminate data collinearity and redundancies, fusing the extracted metrics into a compact 22-dimensional sensitive feature set. To ensure engineering relevance, a refined aeroelastic model of a 3 MW wind turbine is established via GH-Bladed software, and a representative dataset encompassing 10 typical operating conditions—covering normal states, single imbalance types, and various coupled imbalance scenarios—is constructed for rigorous validation. 

[Results] Quantitative verification demonstrates the superiority of the proposed framework. When the MVMD-PE-PCA fused features are fed into a Support Vector Machine optimized by the Sparrow Search Algorithm (SSA-SVM), the diagnostic model achieves a remarkable average accuracy of 94.11%. Comparative analyses reveal significant performance gains: the accuracy is improved by 21.61 and 11.25 percentage points compared to models utilizing raw, un-denoised features and those relying on conventional single-channel VMD denoised features, respectively. Additionally, the PCA-based feature fusion strategy proves far more effective than using isolated single-modal features or simply concatenating multi-channel features, yielding further accuracy improvements of 17.59 and 11.17 percentage points, respectively. These results confirm that the fused feature set possesses strong discriminative capabilities even for samples with subtle fault signatures. 

[Conclusion] The proposed method effectively resolves the issues of multi-channel frequency misalignment and noise interference. It significantly mitigates the confusion between samples of mild aerodynamic imbalance and low-level coupled imbalance, providing a highly robust feature extraction scheme for the health monitoring of wind turbine drive systems.

【Objective】To address the high lighting energy consumption of high-rise buildings and the thermal problem caused by the infrared spectral band during full-spectrum solar energy utilization, this study proposes an integrated transmission illumination photovoltaic system, namely the ITIPV system, cooled by composite phase change materials. The proposed system aims to improve the comprehensive utilization efficiency of solar energy, reduce building lighting energy consumption, and enhance the thermal stability and power generation performance of photovoltaic cells. 

【Methods】In the proposed system, a Fresnel lens is used to concentrate incident solar radiation, and a beam splitter is employed to divide the solar spectrum into visible light and near-infrared light. The visible-light component is transmitted through optical fibers for indoor lighting, while the near-infrared component is utilized for photovoltaic power generation. To control the temperature rise of the photovoltaic cells under concentrated solar irradiation, a nanographite/paraffin composite phase change material combined with cooling fins is adopted for thermal management. The addition of nanographite improves the thermal conductivity of the paraffin-based phase change material, while the cooling fins enhance heat dissipation and temperature uniformity. Geometrical optical analysis is carried out to determine the optimal arrangement of the three-lens unit and the relative positions of the optical components. Based on this analysis, visible-light and near-infrared-light transmission models are established. In addition, an indoor multi-light-source simulation experiment is conducted to evaluate and verify the photoelectric performance and temperature control characteristics of the system under different irradiation intensities. 

【Results】The results show that, under an irradiation intensity of 800 W/m², the average light transmission efficiency of the system reaches 18.9%, and the average photovoltaic power generation efficiency reaches 9.5%. With the composite phase change material cooling strategy, the stable temperature of the photovoltaic cells is reduced to 43.5 ℃, which is approximately 25 ℃ lower than that under natural convective air cooling. Moreover, within the irradiation intensity range of 200–1000 W/m², the photovoltaic cell temperature remains below 50 ℃. This indicates that the proposed cooling method can effectively suppress temperature rise, reduce heat accumulation, and ensure stable electrical output of the photovoltaic cells during operation. 

【Conclusion】Through the synergistic design of spectral splitting and composite phase change material cooling, the proposed ITIPV system realizes the efficient utilization of different solar spectral bands. Visible light is used for indoor illumination, while near-infrared light is converted into electricity by photovoltaic cells. Meanwhile, the nanographite/paraffin composite phase change material coupled with cooling fins effectively maintains the photovoltaic cells at a relatively low operating temperature, thereby supporting efficient and stable power generation. Therefore, the proposed system provides a promising energy-saving solution for buildings such as underground garages and other spaces with high lighting demand. The system exhibits stable performance, broad application potential, and practical significance for improving solar energy utilization efficiency in buildings.

[Objective] To support steam-turbine blade  inspection without opening the cylinder during shutdown and enable deployment  on a confined-space micro-robot, this study proposes an acoustic  fingerprint-based damage identification method that combines low-energy impact  excitation with a lightweight one-dimensional convolutional neural network  (1D-CNN). The method uses the acoustic response produced by structural  excitation to distinguish damage states while addressing the constraints on  model size and computational cost associated with edge deployment. 

[Methods]  Acoustic responses were collected from healthy and damaged steel bars, steel  plates, and last-stage blades from the low-pressure rotor of a 1 000 MW steam  turbine; the blade defect was a naturally occurring crack. A  short-term-average/long-term-average (STA/LTA) trigger was used to extract the  effective response segments automatically. Separate lightweight 1D-CNN models  were developed for the three specimen types, and online data augmentation was  restricted to the training data. The training and test sets were divided by  excitation position rather than by randomly assigning individual signal  samples. All signals acquired at a given position were placed in only one  subset, and every test position remained unseen during model training. This  design evaluated recognition performance for acoustic responses acquired at  previously unseen excitation positions and reduced the possibility of  optimistic estimates caused by distributing signals from the same position  across both subsets. Test samples remained unchanged and were excluded from  model parameter updates. 

[Results] Under position-separated evaluation,  identification accuracy was 97.97% for the turbine blade, 100.00% for the  steel bar, and 96.67% for the steel plate. Recall for damaged samples reached  100.00% for all three specimen types. The network contained approximately 1.77  × 10⁴ trainable parameters and required 8.04–10.15 MFLOPs per input sample.  Numerical simulation showed that the crack altered the local absolute-velocity  amplitude and subsequent oscillation pattern, whereas the first-arrival-time  difference remained below the output time resolution. These simulation results  indicate that, at the selected monitoring point, the crack-related difference  was represented mainly by response amplitude and waveform evolution rather  than by a resolvable shift in arrival time. 

[Conclusion] The three  specimen-specific models identified damage at excitation positions that were  not used for training, indicating that the learned acoustic features were not  confined to the training positions under the tested conditions. By combining  low-energy excitation with a compact model and low computational cost, the  proposed method provides a technical basis for micro-robot-based edge  inspection of steam turbine blades during shutdown without cylinder opening.  The present validation covers the tested specimens and experimental  conditions; applicability across individual blades, turbine types, and  operating conditions should be examined using broader sample sets and field  tests.

[Objective] To elucidate the multi-field coupling mechanisms of gas-solid hydrodynamics, heat transfer, and chemical reactions within an industrial-scale biomass circulating fluidized bed (CFB) gasifier, and to investigate the intensification mechanisms of oxygen-enriched gasification. This study aims to provide a theoretical foundation for engineering scale-up and operational optimization of biomass clean conversion. 

[Methods] A full-scale 3D CFD numerical model was established based on the QFD-6-S industrial gasifier. The gas-phase turbulence was modeled using the standard k-ε model, while the fluidization of quartz sand bed material was simulated via the Eulerian-Eulerian two-fluid model. The complex thermochemical conversion processes of biomass, including drying, pyrolysis, combustion, and gasification, were modeled using the discrete phase model (DPM) in a Lagrangian framework. A key methodological highlight of this work is the precise characterization of non-spherical biomass particles. Recognizing that biomass particles naturally exhibit non-spherical geometries, we derived four distinct drag coefficient correlations for spherical, cylindrical, blocky, and ellipsoidal shapes based on Direct Numerical Simulation (DNS) results. These correlations were subsequently incorporated into the CFD solver through User-Defined Functions (UDFs), significantly improving the accuracy of the gas-solid interactions. Furthermore, thermogravimetric analysis (TGA) was conducted to obtain intrinsic kinetic parameters. The numerical predictions were rigorously validated against experimental data from a laboratory-scale CFB gasifier. 

[Results] The validation results demonstrated that the maximum relative deviation between the simulated gasification efficiency and the experimental data was less than 2.5%. In terms of performance comparison, three operating conditions were comparatively analyzed. Pure rice husk air gasification achieved an efficiency of 72.5%. When blending 75% rice husks with 25% pine wood under air conditions, the efficiency decreased to 66.8% due to the high volatile matter content (75.66%) of pine wood, which led to concentrated oxygen depletion in the dense-phase zone. In contrast, 40% oxygen-enriched gasification yielded the best performance, boosting the gasification efficiency to 76.6%. 

[Conclusion] Based on the above findings, the study reveals the synergistic strengthening mechanisms of 40% oxygen-enriched gasification: it eliminates the heat absorption and dilution effects of N₂, elevates the overall furnace temperature by approximately 80℃, and establishes a positive feedback loop of "enhanced combustion → temperature rise → promoted endothermic reduction reactions" (such as C+CO₂→2CO and C+H₂O→CO+H₂). Furthermore, the results strongly demonstrate that for industrial CFB simulations, the correction for non-spherical particle drag (where ellipsoidal and blocky particles exhibit substantially higher drag coefficients than spherical ones) is indispensable for accurately predicting particle trajectories, residence time distributions, and consequently, the degree of conversion. These findings offer a robust theoretical and data-driven foundation for the industrial-scale application of oxygen-enriched co-gasification of agricultural and forestry solid wastes, and provide critical insights for optimizing reactor design and mitigating alkali-induced slagging.