To improve the efficiency of biogas power generation system, this paper proposes a biogas power generation system based on solid oxide fuel cell (SOFC). A model of the biogas SOFC power generation system is established, and the effects of fuel utilization rate, water-to-carbon ratio, methane content in biogas, and oxygen content in biogas on system's power generation performance are analyzed. The results show that: when the methane content in biogas is 70% and the carbon dioxide content in biogas is 30%, the power generation efficiency of the system can reach 47.8%; increasing the fuel utilization will increase both the system's power output and efficiency; reducing the water -to -carbon ratio will increase the system's power outputbut also raise the risk of carbon deposition; higher methane content in the biogas leads to higher power output, though with a slight decrease in efficiency. A small amount of oxygen in the biogas does not compromise the safety of SOFC anode material and has minimal impact on the system's power generation efficiency.

This study employed municipal sludge (MS), polypropylene (PP), and polyethylene terephthalate (PET) as feedstocks to investigate the effects of plastic co-feeding on the pyrolysis characteristics and product distribution of MS. The mass changes and kinetic properties associated with the co-pyrolysis of MS/PP and MS/PET blends were obtained by thermogravimetric analysis coupled with the Coats-Redfern integral method. Additionally, the compositions of the resulting co-pyrolysis products were analyzed by pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). The results indicated that the synergistic effects observed during the co-pyrolysis of MS/PP and MS/PET shifted the pyrolysis process toward lower temperatures and enhanced the overall conversion efficiency. The apparent activation energy required for the co-pyrolysis of both MS/PP and MS/PET blends was lower than that for MS mono-pyrolysis. Moreover, the addition of PP and PET led to a more pronounced reduction in the apparent activation energy associated with the pyrolysis of heavy molecular-weight species in the high-temperature region. Compared to the mono-pyrolysis of MS, co-pyrolysis of MS/PP increased the relative contents of C30+ organics and aliphatic hydrocarbons, whereas co-pyrolysis of MS/PET not only primarily enhanced the relative yield of C10~C15 products, but also increased the relative contents of alcohols, aliphatic and aromatic hydrocarbons.

With the burgeoning development of the hydrogen energy industry, research endeavors focused on electrochemical hydrogen evolution catalysts have intensified significantly. Molybdenum disulfide (MoS₂) has garnered substantial attention owing to its remarkable electrocatalytic activity toward the hydrogen evolution reaction (HER) and inherent cost-effectiveness. In this study, a heterojunction engineering strategy was adopted to further optimize its catalytic performance. Specifically, MoS₂ was hybridized with varying quantities of porous graphitic carbon nitride (pg-C₃N₄) to modulate the electronic structure of MoS_2, thereby enhancing its HER kinetics and determining the optimal loading mass of pg-C₃N₄. Experimental results reveal that the composite incorporating 40 mg of pg-C₃N₄ exhibits superior electrical conductivity. Furthermore, chronopotentiometry tests conducted over ₂ hours duration demonstrated that the hydrogen yield of the composite exceeded that of pristine MoS₂ by more than 10%. Collectively, these findings offer novel insights into the rational design of low-cost electrocatalysts for hydrogen production.

In order to investigate the co-pyrolysis characteristics of paper sludge and corn straw, thermogravimetric analysis (TGA) was used to study the pyrolysis process of paper sludge, corn straw and mixtures at different heating rates (10, 15, 20 and 25 ℃/min). The Coats Redfern method was used to analyze the pyrolysis kinetics. The results indicated that there were great differences in pyrolysis characteristics between paper sludge and corn straw. Compared to corn straw, the residue yield of paper sludge at 800 ℃ was 36.59% higher. Whereas the difference of activation energy and the maximum weight loss rate between paper sludge and corn straw were 24.97 kJ/mol and 16.78%/min, respectively. As the mass ratio of corn straw increased from 20% to 80%, the initial co-pyrolysis temperature of the mixtures increased from 259.9 ℃ to 281.8 ℃, the maximum weight loss rate increased from 5.29%/min to 15.36%/min, the residue yield decreased from 56.25% to 34.09%, and the activation energy increased from 56.66 kJ/mol to 71.27 kJ/mol. By comparing the theoretical and experimental values, it was concluded that there was an interaction between paper sludge and corn straw during co -pyrolysis, which inhibited the pyrolysis process in the low temperature region and promoted the decomposition process in the high temperature region. The interaction was significantly existed when the mass ratio of corn straw was 60%. The findings can provide basic support for the utilization of paper sludge and corn straw.

The U-bend deep borehole heat exchanger (UDBHE) has a promising application potential. This paper is based on the G functions of infinite cylindrical source (ICS) and infinite line source (ILS) models, four heat transfer models of UDBHE are established, and they adopt the G function of ICS or ILS model, and consider or ignore the time-varying heat flow at the borehole wall, which are abbreviated as ICS-q, ILS-q, ICS-c and ILS-c models, respectively. The accuracy of the four models is studied by comparing with a more accurate two-dimensional numerical model and experimental data under two boundary conditions. The results show that the ILS-q model is the most accurate on the whole, and that the ICS-q model is the most accurate in analyzing the long-term performance of UDBHE. The four models have large errors in the early stage, but after enough long time, the errors of them are small except that the ILS-c and ICS-c models have large errors under the inlet temperature boundary condition. The errors mainly result from two aspects: the G functions of ICS and ILS models have some errors; ignoring the time-varying heat flow will cause some errors. The results can provide reference for the heat transfer model study of UDBHE.

As floating photovoltaic systems gradually extend into deeper waters, shared mooring anchoring systems have gained attention for their adaptability to complex marine environments. This study aims to investigate the bearing performance of finned suction caissons under non-coplanar combined loading conditions. Based on finite element software ABAQUS, the study examines the impact of different horizontal and moment load angles on the ultimate bearing capacity of finned suction caissons in homogeneous and normally consolidated soils, and explores the influence patterns of fully three -dimensional loading on bearing performance within the combined loading space. The results indicate that as θ_H and θ_M (θ_H and θ_M represent the loading angles of horizontal load and moment load, respectively.) increase from 0° to 45°, the ultimate horizontal and moment capacities of the four-fin suction caisson decrease, with a more pronounced reduction observed in homogeneous soils. Additional fins significantly expand the dimensionless failure envelope in V -H (vertical-horizontal load application), V -M (vertical-moment load application), V -T (vertical -torsional load application), T -H (torsional -horizontal load application), and T-M (torsional-moment load application) loading spaces. As θ_H and θ_M increase from 0° to 45°, the dimensionless failure envelope in V -H, V -M, T-H, and T-M loading spaces contracts inward. For non-coplanar H and M conditions, as increases from 0° to 90°, the H-M failure envelope expands outward in the first quadrant, and its symmetry increases as decreases. This study provides theoretical support for understanding the bearing performance of finned suction caissons under fully three-dimensional loading, aiding in the enhancement of design accuracy and structural safety.

Influenced by anisotropic in -situ stress and natural fractures, deep geothermal reservoirs often exhibit preferential fracture development during hydraulic stimulation, resulting in the formation of artificially created geothermal reservoirs that approximate a single-fracture system. Whether such single-fracture geothermal reservoirs can provide commercially viable thermal energy remains a topic of considerable debate. This paper, based on the assumption of a single-fracture geothermal system, investigates the system's heat generation capacity under the influence of varying fracture structural parameters and well location parameters. The simulation results indicate that reducing the fracture aperture from 1.0 to 0.1 effectively delays thermal breakthrough and decreases the heat loss rate by 37.7%. Furthermore, increasing the distance between injection and production wells from 141 m to 424 m, or optimizing the injection flow rate from 1 m³/min to 3 m³/min, enhances the system's heat recovery capacity by 3.1 MW and 10.6 MW, respectively. With detailed characterization of fracture structures and optimization of well field configurations, single-fracture geothermal systems can achieve favorable thermal recovery performance. Assuming a horizontal fracture with an aperture less than 0.1 mm, the system can maintain a thermal loss rate of less than 15% over 30 years of operation and sustain a heat extraction capacity above 12 MW, under the conditions that the well spacing exceeds 420 m, the injection rate is below 1 m³/min, and the injection temperature is above 30.0 ℃.

Addressing the limitations of traditional feedforward neural networks and convolutional neural networks in effectively extracting nonlinear spatial and temporal features from wind turbine operational data, and the current wind turbine fault diagnosis methods that can only perform state monitoring without effective fault localization, this paper proposes a wind turbine fault diagnosis method based on Chebyshev Graph Convolutional Networks and Gated Recurrent Units. First, a graph structure is constructed based on Dynamic Time Warping algorithm; second, Chebyshev Graph Convolutional Networks are used to extract nonlinear spatial correlations from wind turbine operational data; then, Gated Recurrent Units are employed to extract temporal features from the operational data; finally, the wind turbine fault status and fault location are output through fully connected layers and Softmax activation function. Experimental validation shows that this method can not only diagnose potential faults in wind turbines but also effectively determine the specific components where faults occur, achieving an accuracy of 99.33%, with a low false alarm rate of 0.38% and a low missed detection rate of 0.41%.

In order to explore the strain characteristics of wind turbine blades during continuous crosswind, a multi -sensor data fusion method for wind turbine crosswind state evaluation is proposed in this study. In this method, ensemble Empirical Mode Decomposition (EMD) and composite multi-scale permutation entropy-wavelet algorithm are used to jointly reduce the noise of wind turbine blade strain signals, and then Kernel Principal Component Analysis (KPCA) is used to integrate the multiple groups of strain signals after noise reduction. The Squared prediction error (SPE) statistic was used as the evaluation index to effectively divide the wind turbine crosswind process. The results show that the proposed method is effective in reducing the noise of non-stationary wind turbine blade strain signal, and can accurately reflect the strain variation law in continuous crosswind process. In addition, through the combination of KPCA and SPE statistics, the process of wind turbine crosswind is segmtioned, and the operating state of wind turbine crosswind under different influencing factors is analyzed.

The blade down conductor of a wind turbine is a critical component for lightning protection. When the down conductor fails, it can impede the discharge of lightning current, potentially leading to severe consequences such as blade deformation, cracking, or even explosion. This paper focuses on the blade down conductor of wind turbines and designs an online monitoring system for down conductor breakage based on single-wire power transmission technology, radio frequency technology, GPS positioning, and 4G communication technology. The system can monitor the status of the blade down conductor in real time, enabling timely shutdown and maintenance by the staff to prevent more severe damage to the wind turbine. Simulation and experimental results demonstrate that the single-wire power transfer system can achieve signal transmission over 55 m, the radio frequency transmission system can achieve signal return over 100 m, and the communication system can upload detection results in real time. The system can efficiently, stably, and accurately detect down conductor breakage faults.