The jacking oil system serves as a crucial safety barrier by injecting high-pressure oil into the bearings during the startup and shutdown of heavy-duty gas turbines. Taking the pipeline of the jacking oil system of the first domestically produced 300 MWF-class gas turbine as the research object, a coupled simulation method of computational fluid dynamics and transient structural dynamics was adopted. Focusing on the analysisof the water hammer effect caused by valve opening and the transient structural response of the pipeline, the significant influence of different valve opening times and characteristics on pressure wave propagation and pipeline vibration was revealed. Results show that both short valve opening time and rapid opening characteristics intensify fluid pressure oscillations and induce high-amplitude structural vibrations. Although an excessively long valve opening time can mitigate fluid impact, it also prolongs the reconstruction process of the flow field in the pipeline. To suppress the transient impact during valve operation, a valve opening strategy of "slow-rapid-slow" is proposed. The maximum equivalent stress (250.66 MPa) of the pipeline occurs at the intersection line of the tee, and the pipelinestrength meets the engineering requirements.

Taking market-driven as the starting point, the process of forward design of heavy-duty gas turbine control systems, as well as main tasks, design methods and verification technologies, were put forward. The technical difficulties and corresponding solutions were analyzed in details. Based on engineering practices of indigenous heavy-duty gas turbine development, a complete forward design system for control systems was summarized. Results show that the proposed methodology ensures the design quality, and as a result, safety, availability, reliability, economy and operational flexibility of heavy-duty gas turbines are improved. The first indigenously developed heavy-duty gas turbine prototype achieved the first trial success of ignition, ramp-up and synchronization tests. The control system played a crucial role, which validated the effectiveness of this forward design methodology.

To address the real-time prediction requirements of gas turbine metal temperature in digital twin applications, a fast prediction method of gas turbine metal temperature based on recurrent neural networks (RNN) was proposed. Conventional physics-based whole-engine temperature field models driven by operational curves suffer from high complexity and long computation time, failing to meet the demands of real-time prediction and fault warning in gas turbine testing and operation. Two models were trained using datasets from physics-model simulations and whole-engine experiments, achieving fidelity of 99.14% and 94.32% after optimization. The influence of neural network hyperparameters on model performance was systematically analyzed, and effective tuning methods for model optimization and overfitting reduction were proposed. Model generalization capability was verified through test datasets. Deployment was implemented in a prototype F-class heavy-duty gas turbine using functional mock-up interface (FMI) standards, providing an effective solution for real-time temperature prediction in gas turbine digital twin systems.

To improve the design methodology for composite cooling structures of turbine blades, this study investigates the flow network method applied to the design, analysis, and optimization of cooling configurations. Building upon traditional one-dimensional flow network calculation methods, a coupled design and computation platform was developed by integrating internal and external blade cooling considerations. Taking the NASA GE-E³ first-stage rotor blade as a benchmark, a preliminary cooling scheme was designed and subsequently validated against three-dimensional conjugate heat transfer simulations. Based on the initial design, the blade cooling structure was redesigned, and the ribbed channel flow network was optimized using the NSGA-Ⅲ algorithm. Results show that the relative error between the one-dimensional flow network predictions and the three-dimensional conjugate heat transfer results is below 2%, confirming the computational accuracy of the proposed method. The network optimization was completed in approximately 25 h,and the blade temperature after modification and optimization is obviously reduced. The proposed method demonstrates high computational fidelity and offers practical guidance for designing composite cooling structures in turbine blades.

With the continual rise in turbine inlet temperatures of heavy-duty gas turbines, conventional impingement cooling techniques are increasingly inadequate to meet the growing cooling demands of high-temperature turbine components. Focusing on turbine ring segment components, four combined impingement cooling unit configurations based on traditional impingement cooling structures by incorporating various turbulence-enhancing structures on the target surface were proposed. And they were applied to cooling scheme for ring segment components. The semi-coupled numerical simulation method was used to study the comprehensive cooling characteristics of each scheme under the operating conditions of F-class, G/H-class, and HL-class gas turbines. The results demonstrate that applying discrete turbulence-enhancing structures on the target surface increases heat flux by 13.1%-17.6%. Among these configurations, the pin-fin with dimpled surface (PF) configuration exhibits the optimal cooling performance. Under the operating conditions of H-class, the cooling effectiveness of the ring segment scheme utilizing combined cooling units is improved by 3.3%-9.3%, with a more pronounced enhancement under high Biot number conditions (e.g., HL-class), reaching a maximum improvement of 10.1%. The discharge coefficient of the combined scheme is reduced by 1.2%-7.5%. Compared with traditional approaches, the proposed method has more advantages when applied to the high-temperature components of the turbine in the next-generation autonomous gas turbine products.

To tackle the issues of existing machine learning models, which rely on low-dimensional feature inputs for predicting film-cooling efficiency distributions and lack general applicability, a data-driven framework based on PointNet and conditional generative adversarial network (CGAN) was proposed. In this framework, the irregular mesh nodes within the computational domain were transformed into point cloud data. PointNet was then employed to extract both global and local features from this point cloud. These extracted features serve as conditional information to guide the CGAN generator in reconstructing two-dimensional flow field distributions that closely align with those obtained through computational fluid dynamics (CFD) simulations. Additionally, the adversarial training was conducted by discriminator to improve the detail quality of the generated images. Experiments were carried out through using a sawtooth-shaped channel as the research subject. Results demonstrate that the absolute error between the training and test sets does not surpass 0.05, while the relative error remains below 6%. Moreover, the computational time required is less than that of traditional CFD method. When provided with sufficient training samples, the developed model can be applied to all types of film-cooling structures and effectively address the challenge of accurately reconstructing film-cooling efficiency distributions from sparse point cloud data.

Numerical simulation method was used to transform the outlet of cylindrical holes (CH), double-jet holes (DJH) and big-small holes (BSH) into fan-shaped holes, and different hole types were compared with the original structures under different blowing ratios (M) with the cooling fluid of air and droplet/air mixture, respectively. Results show that transforming the outlet of CH and BSH into fan shape causes an apparent decrease in the film cooling effectiveness (FCE). Transforming the outlet of DJH into fan shape is advantageous when M=0.50 and 0.75, but it reduces the FCE when M=1.25 and 1.50. Compared to the original structures, fan-shaped outlet produces a stronger rotating kidney vortex pair, making it easier for the cooling film to detach from the cooled surface. Meanwhile, fan-shaped outlet contributes to the spanwise expansion of cooling fluid under low blowing ratio condition. Injecting droplet into cooling air can evidently improve the FCE. On real blades, the BSH achieve the best FCE at all three positions.

To obtain an experiment scheme for full-scale static modelling comprehensive cooling efficiency under high-temperature and high-pressure in the first-stage rotating blades of an F-class heavy-duty gas turbine, and simultaneously acquire comprehensive cooling efficiency test data of the rotating blades under conditions close to real-world scenarios based on the high-temperature nozzle cascade test rig of the efficient and low-carbon gas turbine test facility, researches were conducted on the modelling method for the inlet conditions of rotating blades, and the key structural design of the transition section-first-stage stationary blade integrated adapter section and the test section. Subsequently, the validity of the scheme was verified by a combined approach of numerical simulation and experimental validation. Experiment results indicate that the experiment scheme and simulation method are reasonable and feasible, with good repeatability and high reliability of experiment results. This test scheme can provide a reference for subsequent similar comprehensive cooling efficiency experiments of rotating blades in the field of heavy-duty gas turbines.

To achieve the rapid coupling analysis of the vibration characteristics of the rotating and stationary components of turbine, taking the mechanical impedance including the direct stiffness and crosstalk stiffness of the turbine casing and support structure as calculation boundary conditions, researches were carried out on modal analysis and unbalanced response analysis for of heavy-duty gas turbine test component by the developed transfer matrix method, while comparisons were conducted on above method results, finite element method results, and experimental data. Results show that, compared with experimental data, the deviations of critical speeds of the rotor obtained by the developed transfer matrix method and finite element method are within -3.38%-2.31% and -6.08%-7.05%, respectively. The critical speed obtained by the transfer matrix method is closer to the experimental value, while the maximum response peak obtained by finite element method is closer to the experimental value. Meanwhile, calculated vibration response curves are basically consistent with experimental results. The developed transfer matrix method incorporating mechanical impedance can quickly and accurately obtain the critical speed of turbine rotor and the vibration response characteristics of entire unit.

Based on a dedicated test facility (designated TB03B) for rim seal investigations in high-efficiency low-carbon gas turbines, experimental studies focused on a 1.5-stage turbine forward disc cavity with fish-mouth seal configuration using CO₂ tracing technique for gas ingestion monitoring. The rig enabled systematic investigations of sealing efficiency, gas intrusion, cavity flow, and turbine aerodynamic comprehensive efficiency. Results show that 1.5-stage turbine testing capability with maximum rotational speed of 3 000 r/min and mainsteam mass flow rate of 10 kg/s, and performance parameters achieved and mostly exceeded design specifications. Sealing efficiency increases with secondary flow pressure ratio while also improving circumferential uniformity. Sealing efficiency at high radius in the cavity exhibits significant negative correlation with pressure gradient at stator blade root, and the correlation is weakened at low radius due to secondary flow mixing effects.

To meet performance requirements of heavy-duty gas turbines for turbine disks with high temperature capability and large dimensions, a key technology study was conducted to address the high complexity and concentrated risks associated with melting, heat treatment, and forging during the fabrication of ultra-large GH4169 superalloy turbine disks. Taking a 650 ℃-class φ2 440 mm GH4169 superalloy turbine disk as the research object, key issues including shrinkage porosity, elemental segregation, harmful phase distribution, microstructural evolution, and cracking damage during vacuum induction melting (VIM), vacuum arc remelting (VAR), stress-relief annealing, homogenization, cogging, and die forging were systematically investigated. By using finite element simulation combined with secondary development, thermodynamic and kinetic modeling, and other methods, the following models and technologies were developed: a prediction model for shrinkage porosity and safe demolding during electrode ingot solidification; a prediction method for element segregation and Laves phase distribution in the VAR process; a stress relief annealing damage model and a stress relaxation model; a homogenization process design model based on Laves phase dissolution and Nb diffusion; and an integrated forging control technology that couples microstructure control, damage control, and load control. The results show that the proposed full-process control strategy can effectively reduce manufacturing risks and optimize the microstructure of disk forgings, providing important technical support for the manufacturing of key components of ultra-large-sized high-temperature alloys. By using this technology, the largest φ2 440 mm super-large GH4169 alloy turbine disk has been successfully fabricated.[KG)]

As gas turbines progress towards higher efficiency and cleaner performance, the demand for combustion chamber designs grounded in high-fidelity models is becoming increasingly pressing. To meet this demand, a physics-informed neural network (PINN) based on variational auto-encoder flame (VAEF) was proposed for modeling both the forward and inverse problems of laminar premixed flames containing low-concentration chemical species. Following strategies were employed in this method: constrained mapping was implemented based on adaptive upper and lower boundaries or observational data; the computational domain was partitioned using smoothing windows with regional weighting; for inverse problems under sparse observation conditions, the variational auto-encoder reconstruction error and the Kullback-Leibler (KL) divergence term were incorporated into the calculation of the observation condition loss. Results indicate that these measures substantially enhance training stability and prediction accuracy for low-concentration species. A coefficient of determination greater than 0.95 and an relative error below 15% are achieved by PINN model, demonstrating excellent global fitting precision and parameter inversion capability. A PINN solution with better robust performance is thus provided for combustion simulation and emission monitoring under complex chemical mechanisms.

Focusing on the combustion characteristics and engineering application value of the constant-pressure sequential burner equipped in the GT36-S5 gas turbine, the structural principles and combustion adjustment mechanisms of the burner in actual operation were systematically elucidated. The effects of characteristic parameters, such as fuel staging ratio, combustion pressure pulsation frequency, and temperature distribution, on the operational stability of the unit were analyzed. By analyzing the process parameters of combustion adjustments under actual operating conditions, the nonlinear response laws of the gas turbine combustion during dynamic load changes were revealed. Specifically addressing the low-temperature operating scenario in winter, the effects of various factors, including fuel staging ratios at various stages, the MET setpoint, and C₂₊ content, on combustion stability, pulsations in different frequency bands and NO_x emissions were analyzed. Results show that the obtained combustion characteristic analysis provides a critical basis for the operation and maintenance of the unit. It can guide on-site personnel to improve combustion stability and equipment reliability in complex environments by adjusting the gas turbine load, the residence time at different load stages, offering significant engineering reference value for the operation optimization of similar heavy-duty gas turbines.

Experimental and numerical simulation studies were conducted on the zoned combustion characteristics of a multi-nozzle array combustor, so as to uncover the stabilization mechanism of combustion zoning and the flame morphology in such combustors. Through experimental investigations, flame morphologies under varying equivalence ratios and nozzle bulk velocities were obtained, while numerical simulations were employed to acquire the non-reacting and reacting flow characteristics of the combustor. Results reveal that, under non-reacting conditions, a large central recirculation zone is formed. Reacting flow field and experimental results show that interactions among the multiple nozzles lead to the formation of small-scale recirculation zones, which promote the entrainment of flue gas in the pilot zone and enhance overall combustion stability. As the nozzle bulk velocity increases, the extent of the recirculation zone remains relatively stable, enabling the combustor to maintain stable combustion over a wide velocity range. With an increase in the equivalence ratio, the flame intensity in the central pilot zone gradually intensifies, and the surrounding main combustion zone is progressively ignited. Transient OH^* chemiluminescence images demonstrate stable combustion morphologies at different periods. Relevant research results may serve as references for the design of zoned multi-nozzle array combustors.

Infrared imaging radiation thermometry has emerged as a robust approach for achieving precise online two-dimensional temperature measurement of gas turbine combustor wall, which is of great significance to ensure operational safety of unit and accurate fault diagnosis of equipment. Regarding the predominant external interference factors in the process of gas turbine combustor wall radiation thermometry, a comprehensive error correction method that took consideration of both gas effect and background reflection interference was proposed. Through rigorous spectral analysis of high-temperature gas radiation characteristics, the wavelength band of 3.7~3.9 μm was selected for measurement, with particular emphasis on the necessity of compensating for gas radiation interference through experiment. Meanwhile, the equivalent background reflection temperature was fitted as a function of squared gas flow rate and air-fuel ratio. Finally, surface temperature measurement of the specimen was conducted on a simulated combustor test rig across various operating conditions. Results show that the proposed error correction method yields an average measurement accuracy of 0.722%, which satisfies practical application requirements.

Institute of Turbomachinery, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China In the context of global energy transition and carbon reduction and emission mitigation, hydrogen energy has been widely regarded as a crucial component of zero-carbon energy system by the international community. Hydrogen gas turbines, serving as essential support for decarbonization of power system and construction of novel multi-energy complementary system, have emerged as a critical pathway for deep decarbonization of future power system due to the advantages of zero-carbon emission potential, flexible peak-shaving characteristics, and high coupling capability with renewable energy sources. Three aspects were systematically reviewed and summarized: hydrogen-blended fuel combustion and emission characteristics, advantages of hydrogen gas turbines, and cascade aerothermal performance of hydrogen gas turbines. The analysis addressed the difficulties and challenges of efficient and stable combustion of carbon-based fuels blended with hydrogen or pure hydrogen fuel, thermal protection design of turbine cascades in high-temperature and high-humidity operation conditions, and organization of cascade film cooling in strongly fluctuating flow fields. Finally, considering the current research status of cascade aerothermal performance of hydrogen gas turbines, design concepts and prospects were proposed for efficient cascade film cooling of hydrogen-fueled turbines.

The high-quality development of new power systems has necessitated a significant supply of low-carbon and dispatchable power sources. According to the thermodynamic properties of supercritical carbon dioxide, a direct-fired supercritical carbon dioxide gas turbine (SCGT) thermal cycle was developed, enabling zero-carbon power generation and regulation functions. Thermodynamic cycle characteristics, performance optimization strategies and coupled application scenarios with new energy sources were investigated. Results show that SCGT achieves a net power generation efficiency of 55.62% and a specific output power of 794.7 kW/kg. Compared with gas turbine combined cycle equipped with carbon capture and storage (CCS) facility, SCGT shows an improvement of 4.56 percentage points in net power generation efficiency. Compared with Allam cycle, SCGT shows an improvement of 141% in specific output power. The combustor, turbine, and regenerator account for 58.99%, 17.57%, and 7.87% of exergy loss in SCGT respectively, and constitute the primary sources of net power generation efficiency deficit. Increasing gas turbine inlet temperature proves most effective for improving power generation efficiency, and optimizing turbine backpressure enables the optimal power generation efficiency under constant inlet conditions. SCGT coupled with energy storage achieves an energy storage efficiency of around 70% and an energy storage capacity ratio of 28.6%. Owing to its advantages of low fuel cost, high power density and long-duration energy storage capability, SCGT technology is well-aligned with the development requirements of new power systems.

To address such issues as output fluctuation, mismatch between supply and demand, and poor reactive power support caused by the high proportion of new energy in new power system, a solution based on hydrogen gas turbine "power-hydrogen-power" coupled energy storage and peak regulation was proposed, and a double-layer nested optimization model was constructed. Firstly, for outer layer, using the maximization of the project's net present value as the optimization objective, a genetic algorithm was used to determine the optimal capacity configuration of each component within the system. Secondly, for inner layer, aiming to maximize the annual net income as the optimization objective, the outer layer capacity was taken as the input and linear optimization was used to dynamically solve the operation strategies of each component, based on the fluctuation of new energy output and load changes in typical scenarios. Finally, the system performance evaluation was conducted with the meteorological conditions of the large new energy base in China's northwest region and typical industrial load conditions as the calculation boundaries. The results show that the annual abandoned power rate of the power-hydrogen-power system is 4.9%. The system's break-even point is in the 17th year, and the green power rate of the system is 99.9%. The "power-hydrogen-power" system based on hydrogen gas turbine can solve the power balance problem of the new power system. By the established double-layer optimization model, the system configuration and operation mode are optimized, effectively improving the renewable energy consumption rate, reducing system investment and operation costs as well as carbon emissions, enhancing energy supply stability, and it is feasible and economically viable.

In the context of the "dual carbon" goal, in order to promote the low-carbon development of gas turbine power generation industry and achieve large-scale application of hydrogen energy, performance calculation was conducted for a typical F-class gas turbine subjected to hydrogen blending of 0%~65% ratio(volume fraction). The impact of hydrogen blending on gas turbine power, efficiency, exhaust flow rate, exhaust temperature, CO₂ emission, and combined cycle performance was deeply analyzed, with a focus on key technologies for the retrofit of gas turbine combustion system, control system, and auxiliary system. Results show that it is necessary to add hydrogen blending equipment and control device for gas turbine hydrogen power generation transformation. The gas turbine burner, fuel system, combustible gas detection system, fire protection system and hood ventilation system should be upgraded adaptively, and the corresponding control and protection logic such as fuel quantity control, fuel distribution and tempering monitoring should be improved. After the hydrogen blending retrofit, with the increase of hydrogen blending ratio, the output and efficiency of gas turbine and combined cycle increase, and the CO₂ content in exhaust gas is significantly reduced, which improves the carbon emission reduction capacity of the unit.

To broaden the operating range and improve the thermodynamic performance of heavy-duty gas turbines using hydrogen-ammonia blended fuels, the 255.6 MW heavy-duty gas turbine at Banshan Power Plant was used as the research object. A regulation strategy based on adjusting the inlet guide vane (IGV) angle was proposed to investigate the gas turbine performance, supercritical conditions in turbine stages, and flow matching characteristics under different hydrogen-ammonium blending ratios. Results show that the calculated power and efficiency under the rated operating condition by the established model are 254.59 MW and 36.33% respectively. The relative errors compared to the design values are -0.4% and -1.54% respectively, demonstrating that the model calculations possess sufficient accuracy. When operating with hydrogen-ammonia blended fuels, the Mach number at the exit of the last-stage stator exceeds 1, resulting in potential safety hazards and degraded operational performance. As the ammonia volume fraction increases from 0 to 100%, the IGV angle decreases from 81.41° to 72.77°. After flow matching optimization, the Mach number at the exit of the last-stage stator decreases from 0.97 to 0.86, while the gas turbine efficiency decreases from 37.14% to 36.64%.

In response to China's "dual carbon" strategy, hydrogen-blended combustion technology for gas turbines has become an important development direction. Based on the SST k-ω turbulence model and a non-premixed combustion model, the flow and emission characteristics of hydrogen-blended natural gas in a can-type combustor were numerically investigated under fuel inlet velocities ranging from 40 to 120 m/s and hydrogen blending ratios from 0 to 20%. Results show that for both pure natural gas and the 20% hydrogen-blended cases, the combustor outlet temperature peaks at 100 m/s before declining. When the inlet velocity exceeds 100 m/s, the hydrogen-blended cases exhibit higher outlet temperatures and a more uniform temperature distribution. However, the high velocity disrupts the recirculation zone and reduces combustion efficiency. CO emissions increase linearly while CO₂ emissions first rise and then decrease. A comprehensive analysis identifies the optimal fuel inlet velocity as 80 m/s. Under this operating condition, compared to pure natural gas combustion, blending 20% hydrogen expands the recirculation zone area and extends the residence time for complete fuel combustion. Consequently,CO₂ and CO emissions decrease by 14.8% and 54.9%, alongside a 64.1% increase in NO_x emissions and an elevated risk of combustion instability. This research provides important theoretical support for optimizing hydrogen-blended combustion systems in gas turbines.

Due to the urgent need for a method that can objectively reflect the energy efficiency evaluation of novel power systems, based on the viewpoint of systems engineering and a thermo-economic state equation for modern power systems ,the steam-water distribution state equation of the system and general equations for transfer and analysis of output work and cycle heat absorption were constructed, corresponding one-to-one with the structure of the power plant's thermal system. Based on this, carbon capture, energy storage, wind power, photovoltaics, and solar thermal power were treated as auxiliary systems to explore their integration mechanisms with the thermal system of the thermal power units. A novel wind-solar-fire-storage complementary hybrid power generation system considering carbon capture system was proposed, and a general equation for energy efficiency evaluation and calculation of the multi-energy complementary hybrid power generation system was established. Finally, the proposed method was verified through a case study. Results show that the method possesses the characteristics of being universal, precise, and programmable, which makes the overall calculation and local analysis of the complex system energy efficiency clearer, providing theoretical support for the energy efficiency evaluation of clean and low-carbon novel power systems.

With the widespread application of renewable energy, the operational complexity and uncertainty of integrated energy systems have increased significantly. Traditional scheduling methods are insufficient to efficiently address issues such as multi-energy flows and strong coupling. Therefore, an integrated energy system model encompassing photovoltaic panels, wind turbines, hydrogen storage tanks, batteries, heat pumps, and fuel cells was developed. By combining the deep deterministic policy gradient (DDPG) algorithm, the energy scheduling optimization under uncertain conditions was achieved. Results show that the DDPG algorithm can effectively solve the problem of multi-energy flow coordinated scheduling, achieving supply-demand balance and efficient utilization of electricity, heat, cooling, and hydrogen under different load demands and energy supply conditions. Moreover, after training, the system exhibits excellent scheduling performance and adaptability when confronted with diverse uncertain scenarios, enabling flexible responses to energy fluctuations.

To address the issues of complex equipment, high energy consumption and poor durability in the treatment process of biogas and biogas slurry during anaerobic fermentation of biomass, a new type of integrated ammonia recovery and carbon capture device was designed based on superamphiphobic membrane contactor, which integrated the advantages of direct contact membrane distillation, membrane absorption and other technologies. This device effectively enhances the energy utilization efficiency of the overall process while reducing the complexity of the equipment. The ammonia recovery and carbon capture performances of the integrated device was systematically studied through experiments. Results show that when using ammonia of low surface tension as feed side, the droplet contact angle of membrane contactor is greater than 150°, and maintains stable operation within 48 h. When operating with a gas circulation process flow and the temperature on the feed liquid side reaches 70 ℃, the ammonia recovery rate of the integrated device is increased from 45% to 97%, and the CO₂ removal rate exceeds 95%, the CO₂ removal rate on the permeate side can reach above 4.3 mol/(m²·h), thus realizing the high-efficiency resource utilization of wastes in the biogas slurry and biogas treatment processes.

To enhance the prediction accuracy of SO₂ emissions during sludge incineration process and optimize incineration and flue gas treatment conditions, an efficient and stable hybrid prediction model for SO₂ emissions was prorosed. First, using a bubbling fluidized bed sludge incineration system as the research subject, static and dynamic flame features were extracted from flame images and the input features were set combined with distributed control system (DCS) parameters, while SO₂ emission concentration was set as the model output. Subsequently, mutual information (MI) was employed to determine the optimal lag time between SO₂ and each input feature, guiding data reorganization. Finally, the extremely randomized trees (ERT) model based on Bayesian optimization-TPE (BO-TPE) was constructed and compared with multiple mainstream prediction models. Results show that the BO-TPE-optimized ERT model achieves correlation coefficient R² of 0.93 with mean absolute percentage error(MAPE) below 3%, making it suitable for online prediction and process optimization control of SO₂ emissions in sludge incineration systems.

The traditional Riccati transfer matrix method exhitibs significant limitations in accurately reflecting the dynamic characteristics of steam turbine rotors under fault scenarios. Additionally, the identification of critical sections primarily relies on alternating stress amplitude, while neglecting the impact of non-zero mean stress, which may lead to deviations in fatigue damage estimation. An improved method was proposed, in which the steady-state motion parameters before the fault was taken as initial values.A three-point interpolation method was then employed to calculate the motion state of each shaft segment during the non-steady interval in real time, thereby capturing the true response when the fault was detected. Meanwhile, the fatigue damage was refined by incorporating the mean-stress effect based on Goodman's theory.A 600 MW steam turbine generator shafting was analyzed as an example to evaluate the fatigue damage caused by two types of resonance in the subsynchronous frequency range. Results show that the improved response calculation method significantly reduces errors caused by unsteady factors, and the identification of critical sections requires comprehensive consideration of the components and intensity of SSO excitation.

A deterioration trend perception algorithm considering the operating characteristics of wind turbines was proposed. Firstly, a kinetic deterioration state indicator based on damage accumulation theory was proposed, and the bearing deterioration level (DL) was calculated by Hertz theory. Secondly, a hybrid long short-term memory (LSTM)-CatBoost predictor (HLCP) deterioration prediction method was proposed. The deterioration growth value was predicted by considering the influence of wind turbine operating characteristics and introducing the bearing speed as a time series input. Then, the critical value of bearing damage was calculated based on the simulation model, so as to predict the remaining bearing life. Finally, it was validated using the vibration and supervisory control and data acquisition (SCADA) data from a wind farm in the north of China. Results show that the proposed deterioration state indicator has a similarity of about 74.42% in predicting trend and historical trend, with obvious trend and monotonicity. The error between the remaining life prediction results obtained based on the HLCP model and the actual failure time is only 640 h, which is 16 461 h less compared to the prediction method that does not take into account the turbine's operating characteristics.

To address issues of feature redundancy, low prediction accuracy, weak generalization ability, and high false alarm rates in existing gearbox fault warning methods, a fault warning method based on neural basis expansion analysis was proposed. The method combined Sigmoid growth curve with a density-based spatial clustering algorithm to extract normal monitoring data from raw data. An improved grey relational analysis algorithm was used to assign weights to different data points, variable information was deeply explored. Feature variables related to the oil temperature of the gearbox were extracted from the data collected by the high-dimensional monitoring and data acquisition system. Local nonlinear projection was used to decompose the target signal into basis functions to predict the state variables of the gearbox accurately, fault thresholds were set by sliding window and confidence interval to reduce the false alarm rate. The method was verified based on actual wind field data. Results show that the fault prediction method based on the NBEATSx model significantly improves the prediction accuracy in the state prediction of gearboxes, reduces the false alarm rate compared to traditional models, and has the ability to predict faults several hours in advance, thereby effectively ensuring the stable operation of wind turbine units.

To understand the effect of alkali and alkaline earth metals on the gasification reactivity of biomass char under pressurized conditions, the biomass char prepared by pyrolysis at 800 ℃ was used as the research object. The pyrolyzed biomass was pre-treated with deionized water and dilute hydrochloric acid solution, respectively. Gasification study was conducted on the raw biomass char and the two pre-treated samples in a wire-mesh pressurized reactor under various gasification agents, temperatures, and pressures. Results show that most of the potassium and some of the calcium and magnesium can be removed from the water-washed samples, while no alkali and alkaline earth metals are detected in the acid-washed biomass char sample. At 850 ℃, the gasification reactivity is not affected by pressure, whereas at 1 000 ℃, pressure has a promotional effect on gasification reactivity. Water-soluble potassium and calcium significantly enhance the gasification reactivity with both CO₂ and steam, and their catalytic effects are enhanced at high temperatures. The water-insoluble calcium only slightly increases the gasification reactivity. At 850 ℃, the reaction orders with respect to CO₂ partial pressure are close to 0 for all samples, while at 1 000 ℃, the reaction order is greater than 0.

Based on the acoustic-structural coupling method, the modal characteristics of a molten salt storage tank in a commercial 50 MW tower-type concentrated solar power (CSP) plant were simulated. The influence of anchoring methods on the sloshing characteristics of molten salt storage tanks under full liquid level conditions was investigated, and the effects of parameters such as different liquid levels and operating temperatures on the modal frequencies of unanchored molten salt storage tanks were explored. Results show that the first three orders of unanchored molten salt storage tanks under full liquid level are prone to rigid-body displacement modes, and the sloshing frequency values of the 3rd to 20th orders differ significantly from those of anchored tanks. Therefore, the unanchored configuration is more suitable for modal analysis of molten salt storage tanks. The sloshing frequency of the tank increases with the rise of liquid level under different liquid level conditions, and the sloshing frequency of each order is lower than the natural frequency of the empty tank. The natural vibration period of the empty tank ranges from 0.06-0.1 s, while the sloshing period is as long as 2-15 s, indicating that the sloshing frequency of the tank in the salt-filled state is highly susceptible to resonance under the excitation of long-period seismic waves. Operating temperature has little effect on the sloshing frequency of molten salt, but preheating temperature has a significant impact on the natural frequency of the empty tank. When the operating temperature increases by 100 K, the highest-order natural frequency of the empty tank only decreases by 2.5%. To avoid instability of large-capacity molten salt tanks under the action of high-frequency seismic waves in the preheated state, the influence of preheating temperature on the frequency of empty tanks should be taken into account in the seismic design of molten salt tank structures in practical engineering.