ObjectivesTo enhance the operational flexibility of carbon capture coal-fired power plants and thus support the power grid in integrating renewable energy generation, this study proposes an integrated model of compressed carbon dioxide energy storage and carbon capture coal-fired cogeneration system.MethodsThe study establishes a performance analysis model of a carbon capture coal-fired cogeneration system integrated with compressed carbon dioxide energy storage.The system thermal performance and sensitivity of key parameters are investigated using exergy analysis method.ResultsThe research shows that compared with the original car by integrating the compressed carbon dioxide energy storage system, the output power of the carbon capture coal-fired power generation system decreases by 22.59% under the charging condition and increases by 6.19% under the discharging condition. The system power efficiency (SPE) of the compressed carbon dioxide energy storage system reaches 86.32%, the exergy efficiency is 64.82%, and the energy storage density reaches 46.60 kW⋅h/m3. In addition, the integrated system can effectively recover the waste heat from the carbon capture process to supply heat users, with waste heat recovery rates of 24.9% and 26.1% under the charging and discharging conditions, respectively.ConclusionsThe proposed system enhances the operational flexibility of power generation. Key measures to enhance system performance include improving the expander’s isentropic efficiency, reducing the heat transfer terminal temperature difference, and improving heat transfer efficiency.
ObjectivesTo address challenges of short-term electricity price prediction arising from high penetration of renewable energy into the grid, this study proposes a multi-modal, multi-task short-term electricity price prediction model, EP-VLM, based on a vision language model (VLM).MethodsThe model integrates three modules: a patch memory augmenter, a frequency-domain convolutional encoder, and a structured text embedder. These modules process temporal, visual, and textual multi-modal features, respectively, and convert them into tokens that can be embedded into the VLM. By extracting multi-modal fused features, the model can comprehensively capture the complex factors influencing electricity prices.ResultsExperiments based on real data from a provincial electricity market in northern China demonstrate that EP-VLM significantly outperforms existing time-series prediction models across different prediction horizons and exhibits strong few-shot learning capability.ConclusionsThe model effectively improves electricity price prediction precision, validating its accuracy and robustness in ultra-short-term and day-ahead electricity price prediction.
ObjectivesWith the continuous growth of the proportion of renewable energy in the energy structure, the application of electricity-based hydrogen production coupled with fuel cell power generation technology in the electricity-hydrogen-heat integrated energy system has received widespread attention from the industry. These systems are gradually evolving from single independent systems to clustered ones, which makes comprehensive control technology play a crucial role in them. To this end, the research progress of coordinated control technology for electricity-hydrogen coupling system clusters is systematically analyzed.MethodsThree main modeling methods—mechanistic modeling, empirical modeling, and semi-empirical modeling—are comprehensively reviewed in terms of their roles in optimizing the electrolyzer performance. An in-depth analysis is conducted on the response capabilities of three control strategies, namely chain allocation strategy, average allocation strategy, and rotation control strategy, under fluctuating operating conditions. The application potential of integrated energy systems, including electricity-hydrogen coupling systems in fields such as transportation, construction, and industrial parks, is explored, with particular emphasis on their critical role in power production and supply. Finally, an economic analysis is conducted to evaluate the cost-effectiveness of different models in practical applications, in order to provide a theoretical basis and decision support for the optimization design and efficient operation of electricity-hydrogen-heat integrated energy systems.ConclusionsIn the future, the integrated application of artificial intelligence and machine learning technology in the electricity-based hydrogen production system is expected to further improve modeling accuracy, optimize operation strategies, enhance system stability and intelligence level, thereby promoting the development of electricity-based hydrogen production technology towards high efficiency and sustainability.
ObjectivesPhotovoltaic power generation is characterized by randomness, intermittency, and volatility. Its large-scale grid integration may impact power systems, thereby posing serious challenges to grid operation and scheduling. Therefore, developing a high-precision photovoltaic power prediction model and accurately characterizing the fluctuation patterns of photovoltaic power under varying meteorological conditions are of great significance for ensuring the stable operation of the power grid. To this end, this study proposes a photovoltaic power prediction method based on an intelligent optimization algorithm and a combined prediction model.MethodsFirst, a residual network (ResNet)-LSTM-Dropout combined photovoltaic power prediction model is constructed with the long short-term memory network (LSTM) as the core model. In this model, the ResNet is used to extract deep nonlinear features from complex meteorological data, LSTM is employed to learn the temporal variation patterns of photovoltaic power sequences, and the Dropout layer is introduced to reduce the risk of overfitting during model training on complex samples, thereby improving the generalization performance of the model. Second, to address the problems that the rime optimization algorithm (RIME) easily falls into local optima and has slow convergence speed, the cosine strategy is introduced to improve local exploration capability, the role strategy is adopted to enhance global search capability, and the Cauchy mutation strategy is incorporated to avoid premature convergence. Accordingly, an improved rime optimization algorithm (IRIME) is proposed. Finally, IRIME is used to optimize the key hyperparameters of the ResNet-LSTM-Dropout combined model, and the optimized model is applied to short-term photovoltaic power prediction.ResultsThe effectiveness of the proposed model is verified using measured data from a photovoltaic power station in Northwest China. The experimental results show that, under different weather conditions, the IRIME-ResNet-LSTM-Dropout model outperforms other comparison models in prediction performance, with more significant advantages under complex weather conditions such as cloudy and rainy days.ConclusionsThe proposed method effectively improves the accuracy of photovoltaic power prediction, providing important theoretical support for ensuring safe and stable grid operation and optimizing coordinated planning in power systems.
ObjectivesThe multimodal fault signals such as vibration anomalies with nonlinear and multi-scale characteristics may be generated during the operation of hydroturbine units. It is difficult for traditional time-series models to capture long-range dependencies, and the single-modal analysis method cannot integrate heterogeneous data features effectively. In response to the above issues, this study proposes a multimodal heterogeneous graph hybrid feature extraction model consisting of Transformer-Gramian angular summation fields (GASF)-recurrence plot (RP)-two-dimensional (2D)-gated recurrent unit (GRU), aiming to improve the reliability and generalization ability of fault diagnosis.MethodsFirstly, the time-series data of hydroturbine units are converted into 2D images, GASF and RP methods are used to extract spatial features of time-series data, and Transformer model is constructed. Meanwhile, GRU is used to capture dynamic time-series features, and multi-modal feature fusion is used to combine temporal features, image spatial features, and heterogeneous image features. Thus, the accuracy and robustness of fault identification are significantly improved.ResultsThe proposed method shows higher accuracy and stronger generalization ability in the fault diagnosis task of hydroturbine units, and the diagnosis accuracy reaches 100% on multiple test sets.ConclusionsThe proposed method can effectively fuse time-series data and image features, significantly enhance the model's ability to recognize nonlinear fault modes, and accurately capture the abnormal state of the device.
ObjectivesThe high-temperature superconducting (HTS) D-shaped coil is one of the core components in nuclear fusion devices. It features a large operating current, high magnetic field, and significant Lorentz force, which has a significant impact on the stable operation of the magnet. Therefore, an investigation is conducted on the mechanical stability of D-shaped magnets and toroidal field (TF) coils based on the conductor on round core cable-in-conduit conductor (CORC-CICC) at 20 K.MethodsFirstly, a three-dimensional finite element model is established and analyzed, revealing that the force per unit volume reaches its maximum at the bend of the D-shaped magnets (1.74×108 N/m3) and the central section of the TF coils (2.24×108 N/m3). Next, the three-dimensional thermo-mechanical coupling model is analyzed, and the mechanical stability at different positions of the magnets is studied. The quench energy and quench propagation velocity of the magnets are calculated, and it is found that both attain their highest values at the strongest magnetic field position, with quench energy measured at 1.03 MJ/m3, and the axial and transverse quench propagation velocities at 142.86 mm/s and 2.77 mm/s, respectively. Finally, a method of incorporating AlN and epoxy hybrid material is proposed to improve the mechanical stability of magnets.ResultsThe minimum quench energy at the strongest magnetic field position increases. The axial quench propagation velocity is slower than that without the hybrid material, while the transverse velocity is faster than that without the hybrid material.ConclusionsThe D-shaped magnets and the TF coils have the weakest mechanical stability points. The proposed method can not only effectively improve the mechanical stability of magnets, but also has significant reference value for the design and operation of large superconducting magnets operating in the 20 K temperature range.
ObjectivesAccurate prediction of photovoltaic power is of great significance for the safe, stable, and economic operation of the power grid. To improve the accuracy of photovoltaic power prediction, this study proposes a photovoltaic power prediction model based on sample entropy clustering decomposition-inverted Transformer.MethodsFirsty, a sample entropy clustering decomposition method is proposed to optimize the sample entropy reconstruction process by introducing a hierarchical clustering algorithm and the silhouette coefficient to construct an adaptive assessment system. Secondly, the optimized variational mode decomposition algorithm is used to perform targeted secondary decomposition of the clustered components with noise interference. Then, the decomposed components are predicted separately using the inverted Transformer model to deeply mine the correlations among variables in long time series. Finally, the prediction results of each component are superimposed to obtain the final prediction results.ResultsCase analysis shows that the proposed method improves coefficient of determination by 0.7%, 3.1% and 3.2% on sunny, cloudy, and rainy days, respectively, compared with the benchmark iTransformer model. The coefficient of determination improves by 4.2%, 4.7% and 4.6% for different prediction sequence lengths, which fully verifies the superiority and feasibility of the proposed model.ConclusionsThe proposed method effectively solves the problem of low prediction accuracy caused by the weak characterization of highly fluctuating and intermittent photovoltaic power data in traditional models, as well as the insufficient capability for multivariate long-term time series modeling, and significantly improves the prediction accuracy.
ObjectiveRecent advances in large language models (LLMs) have demonstrated breakthroughs in semantic understanding and reasoning-based generation, providing new technological pathways for multi-source information integration, complex scenario decision-making, and cross-agent coordination. However, a systematic understanding of the mechanisms and application frameworks of LLMs in climate-adaptive power systems remains lacking in both academia and industry. Against this background, the application potential and core issues of LLMs in enhancing the climate adaptability of power systems are systematically reviewed.MethodsFirst, starting from the multi-scale impacts of climate change on power systems, the key capability constraints of power systems in information integration, decision generation, and coordinated interaction are analyzed. Second, a unified analytical framework of "information understanding–decision generation–coordinated interaction" is established, and the typical methodological paradigms of LLMs in semantic understanding, reasoning-based generation, and multi-agent coordination are systematically summarized. Furthermore, key challenges of LLMs in terms of output reliability, consistency with physical constraints, real-time responsiveness, data security, and adaptability to non-stationary environments are analyzed based on engineering application requirements. Finally, future research directions and implementation pathways for LLM-empowered power systems are envisioned to address system evolution requirements under climate uncertainty.ConclusionFacing the challenges of climate change, the deep empowerment of LLMs will drive power systems from passive response toward proactive adaptation, advancing toward a new paradigm of coordinated development characterized by security, low-carbon development, and high resilience.
ObjectivesWith the large-scale integration of new energy sources such as wind and photovoltaic power into power grid, their randomness and volatility pose challenges to the secure grid connection and consumption of new energy. Distributed compressed air energy storage (DCAES) can achieve rapid tracking of power commands due to its high flexibility and variable rotational speed, thereby smoothing the power fluctuations of new energy. However, the expander-generator system of DCAES typically uses the conventional perturbation and observation method for power tracking, which struggles to balance tracking speed and steady-state accuracy. To address this issue, a variable step power tracking control strategy for DCAES with flexible power scheduling and excellent tracking performance is proposed.MethodsThe power variation rate and rotational speed deviation rate of the DCAES expander-generator system are incorporated into the perturbation step size. By adaptively adjusting the rotational speed variation step size in real time, the system achieves rapid and reliable tracking of power commands. A simulation model of the DCAES expander-generator system is established using MATLAB/Simulink to verify the effectiveness of the proposed control strategy.ResultsCompared with the small-step power tracking control strategy, the proposed control strategy can improve the system tracking speed by more than 25%. Compared with the large-step power tracking control strategy, the two have almost the same tracking speed, but the power oscillation amplitude is reduced to less than 1% of that of the large-step strategy.ConclusionsThe proposed control strategy effectively improves the influence of rotational speed step size selection on power tracking performance, enabling the system to achieve faster dynamic tracking speed and higher steady-state accuracy.
ObjectivesThe particle size distribution of incoming coal directly affects the combustion efficiency, operational stability, and environmental indicators of circulating fluidized bed boilers. Therefore, achieving real-time, online, and accurate detection is crucial. To address the challenges of severe overlap, blurred edges, and missed detection of small objects in existing image processing methods for coal flow particle detection, a cascade detection architecture integrating YOLOv5 and the segment anything model (SAM) is proposed.MethodsCoal flow images captured by an industrial camera are preprocessed and contrast-enhanced. An improved YOLOv5m model, incorporating a CoordAttention mechanism and optimized anchor box design, is introduced to rapidly and coarsely locate and mask significant coal particles, effectively masking identified regions. The SAM is then fine-tuned using a transfer learning strategy to refine the segmentation of small and blurred particles within residual complex regions, significantly reducing computational redundancy.ResultsThis YOLOv5m-SAM collaborative approach outperforms comparison methods in detection accuracy, stability, and efficiency. The combined model achieves an average intersection over union (IoU) of 0.94, with the mean particle size count close to the true value and a standard deviation of only 0.39. The single detection time is approximately 9.6 s, an 88% reduction compared to the single SAM, significantly improving real-time performance.ConclusionsThe research results validate the generalization ability and robustness of the proposed method in complex industrial image scenarios. This provides a reliable technical path for online, fully automated, and high-precision analysis of coal flow particle size on coal conveyor belts, and has promising prospects for engineering application and promotion.
ObjectivesThe performance of lithium batteries is highly sensitive to temperature changes. To address the heat generation during the charge and discharge process of energy storage batteries under extreme operating conditions, a thermal management system for batteries based on paraffin/expanded graphite composite phase change material (CPCM) is designed.MethodsBased on Bernardi’s heat generation theory, the user-defined function (UDF) source term of the battery numerical model is developed to investigate the effects of different discharge rates, CPCM arrangement types, densities, and thicknesses on the thermal management performance of the system. The temperature control performance of the system is further optimized by coupling it with an aluminum metal casing.ResultsCompared with low discharge rates, CPCM has a more significant effect on battery thermal management at high discharge rates. The anisotropy of the internal thermal conductivity of the battery and the contact area between the CPCM and the battery surface play key roles in heat transfer efficiency and temperature distribution. Appropriately increasing the density and thickness of the CPCM helps enhance the temperature control performance of the system. In addition, coupling the 1.6 mm-thick aluminum casing enhances the cooling performance while maintaining good battery pack efficiency.ConclusionsThe research findings can provide a reference for the design and optimization of thermal management systems for batteries in related energy storage applications.
ObjectivesWith the widespread application of energy storage devices such as batteries and supercapacitors worldwide, real-time online monitoring of their performance has become increasingly critical, and the importance of battery sensing systems is also pronounced. Traditional sensors are susceptible to electromagnetic interference, while optical sensors have the advantages of reduced electromagnetic interference, small size, and light weight, which can significantly improve the accuracy of estimating state of charge (SOC) and state of health (SOH). Therefore, it is necessary to study the application of fiber-optic sensing technology in battery SOC and SOH monitoring. Therefore, it is necessary to analyze its research progress.MethodsThe working principles and application cases of fiber-optic evanescent wave sensors, fiber Bragg grating sensors, and fiber-optic localized surface plasmon resonance sensors are introduced in detail. In addition, it discusses how to use advanced data processing technologies and algorithms to extract valuable information from a large amount of raw data to further optimize battery performance, predict failures, and improve overall system efficiency. By applying advanced data analysis technologies, such as feature selection, pattern recognition, and predictive modeling, it is possible to effectively improve the understanding of battery performance and identify potential problems in advance, thereby enhancing the overall performance and safety of the system.Conclusions. Future research should focus on further improving the performance of the sensor itself, including sensitivity, stability, and cost-effectiveness. Additionally, the development of data processing algorithms should be promoted to better adapt to the rapidly changing market needs.
ObjectivesThe generator bearings of wind turbines are prone to damage during operation, and traditional spectrum analysis, which relies on human experience, often fails to accurately and promptly detect faults. To reduce operational risks and minimize economic losses caused by fault-induced downtime, a data processing and analysis method based on the BERT large model is developed for the condition monitoring system (CMS) for wind turbine generator bearings, which can be used for precise fault data identification.MethodsUsing CMS data collected from an actual wind farm, 17-dimensional features containing bearing outer race characteristic frequencies, along with time-domain and frequency-domain information, are generated. These features are structurally processed and input into the BERT large model. A fault data identification model is obtained through data training.ResultsThe model’s fault data identification capability is tested using 240 sets of actual data from other wind turbines in the same wind farm, achieving an identification accuracy of 98.75%.ConclusionsThe proposed method enables accurate analysis and identification of CMS data under complex working conditions and strong‑noise environments, providing references for improving the intelligence and refinement of wind turbine condition monitoring.
ObjectivesWhen large-scale circulating fluidized bed (CFB) boilers are involved in deep peak regulation, the fluidization quality inside the furnace deteriorates. To enhance the fluidization effect of particles in CFB boilers during peak regulation, the concept of pulse-type hood is proposed.MethodsBy adding a Helmholtz oscillation chamber to the inlet pipe of the hood, the outlet airflow exhibits pulse variations at certain frequencies, thereby forming the designed pulse-type hood. Based on a visualized test platform, cold-state tests on a single pulse-type hood are conducted. By analyzing the time-domain and frequency-domain variations of pressure at different positions of the hood, the pulse characteristics are obtained. The resistance characteristics are determined by examining the pressure drop between the inlet and outlet of the hood. The fluidization characteristics are obtained by analyzing the disturbance range and the gas-solid flow of particles around the hood.ResultsThe pulse frequency predominantly concentrates within the 18 to 24 Hz range, and the pulse intensity remains notable even at loads below 40%. The pressure drop at both ends of the pulse-type hood increases with increasing load, reaching 2.2 kPa at full load. Compared with bell-type hood, at 40%, 70%, and 100% loads, the pulse-type hood increases the disturbance range of particles by 8.0%, 11.1%, and 15.7%, respectively, while reducing the accumulation height of large particles by 2.1%, 2.5%, and 2.9%, respectively.ConclusionsThe integration of a Helmholtz oscillation chamber allows the pulse airflow to be generated at the hood outlet, enhancing gas-solid contact. This increases the disturbance to the bed particles, reduces the accumulation height of large particles, and significantly improves the fluidization quality of the particles.
ObjectivesAddressing the problem in the direct application of general large language models in the vertical domain of power grid dispatch and control, such as domain adaptation difficulties, prominent “hallucination” risks, high training costs, and lagging knowledge updates, this study proposes and designs a rapid construction technology for a specialized large language model for power grid dispatch and control that deeply integrates domain knowledge and large model capabilities.MethodsFirstly, based on general large models, an integrated collaborative strategy of “domain low-rank adaptation fine-tuning-retrieval-augmented generation knowledge enhancement-optimized ranking” is designed by fusing low-rank adaptation and retrieval-augmented generation techniques. In the fine-tuning stage, “question-retrieval evidence-answer” triplet training samples are introduced to achieve collaborative training between the knowledge invocation strategy and the knowledge content itself. Secondly, a two-layer hybrid retrieval architecture tailored to the strong spatiotemporal-device constraints of power grids is constructed. This architecture effectively compresses the search space through a cascade strategy of structured attribute filtering and semantic vector retrieval. Based on this, a knowledge grouping and re-ranking strategy is designed to elevate the retrieval results from “most similar text” to “optimal knowledge structure”. Then, structured prompt templates that guide spatiotemporal cognition, topological reasoning, and multi-step reasoning are designed, and an “evidence-threshold-human” triple reliability assurance mechanism is constructed throughout the entire model generation process.ResultsExperimental verification demonstrates that the model proposed in this study significantly outperforms general large models and existing domain models in terms of domain-specific knowledge capability and retrieval enhancement performance. The accuracy rate of answers in high-risk scenarios reaches 98.1%, effectively suppressing hallucination.ConclusionsThe methods and strategies proposed in this study provide a feasible engineering solution for the safe and controllable application of large models in high-risk scenarios of power grid dispatch and control.
ObjectivesThe increasing integration of distributed renewable energy with higher uncertainty and fluctuation into power systems leads to greater frequency deviations and exacerbates active power imbalance in the systems. To effectively address the active power imbalance caused by renewable energy integration, this study proposes a Transformer and Q learning dual-driven smart generation control (TQDD) algorithm.MethodsThe smart power generation controller based on the TQDD algorithm consists of a digital-analog dual-drive loop and a proportional loop. Within the digital-analog dual-drive loop, complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) performs mode decomposition on the acquired frequency deviation signal. Transformer is used to predict a series of modal components after mode decomposition. K-means clustering classifies the predicted signals into large and small fluctuation signals. Q learning tracks the large fluctuation signals after classification. The fractional-order PID (FOPID) rapidly tracks the small fluctuation signals after classification.ResultsThe proposed TQDD method and five other comparative algorithms are simulated in a two-area power system case in which the output proportion of renewable energy generating units is 80%. The results show that under the control of the TQDD algorithm, frequency deviation, total power generation cost, and carbon emission cost are reduced by 45.44%, 12.04%, and 10.25%, respectively, compared with other comparative algorithms.ConclusionsThe proposed algorithm enables precise control of the output power from each generating unit in new-type power systems, thereby reducing frequency fluctuations in the power systems.
ObjectivesIn recent years, the integration of thermochemistry and concentrating solar power technologies has led to novel applications for solar thermal technologies. However, the coupling between thermochemical reactors and solar concentrators such as solar furnaces remains insufficient. Therefore, the energy flux density on the receiving surface of thermochemical reactors is studied, and an optimized design method for concentrators specifically for receiving surface is proposed.MethodsTaking a solar furnace with tilted receiving surface as the research object, a numerical simulation model using the ray-tracing method is established to explore how design variations of receiving surface of reactors affect the energy flux density distribution at the target surface. The effect of ray incident angles at the target surface on the concentrating contribution of solar furnace concentrators is studied and used as a constraint to optimize the design of concentrators. Additionally, the performance of optimized concentrators is analyzed.ResultsThe edge region of the concentrator causes significant distortion of the focal spot and a notable decrease in energy flux density contribution. By constraining the incident angle of concentrated rays at the target surface, the peak energy flux density of the focal spot can be increased by 18.52% and the power at the target surface improved by 6.53%, while maintaining the same mirror area.ConclusionsThis study provides the influencing pattern of energy flux density distribution for the design of thermochemical reactor receiving surface. The findings contribute significantly to the coupling and matching of solar thermochemical systems.
ObjectivesThe lack of grid support in offshore islands, significant seasonal variations in renewable energy output and load levels, and the absence of long-term energy storage methods result in large-scale wind and solar curtailment and high carbon emissions. Coordinating multiple flexible resources between different island microgrids via tie lines to achieve power sharing and economic operation has become a current challenge. To address this, a distributed coordination optimization strategy based on the locally adaptive alternating direction method of multipliers (LA-ADMM) is proposed.MethodsFirstly, the microturbine, battery energy storage, and flexible load within the island microgrid cluster are modeled. Secondly, an economic optimization model is established, considering operation and maintenance costs, environmental costs, power transaction costs, and network costs. Finally, the model is solved using LA-ADMM algorithm.ResultsWhile ensuring the power supply-demand balance of the microgrids, when the flexible load, battery energy storage, and microturbine are individually integrated into the scheduling, the maximum reduction in the comprehensive operation costs of the island microgrids are 4.223%, 5.316%, and 6.326%, respectively. Additionally, the time required to solve the optimal operation strategy using the LA-ADMM algorithm is 41.38% shorter than that using the ADMM algorithm.ConclusionsThe proposed method can not only accelerate the solution speed of the distributed optimization strategy, but also achieve the economic operation of the system.
ObjectivesUnder the background of flexible deep peak shaving, conventional fixed-time and fixed-quantity boiler soot-blowing methods are unable to adapt to dynamic operating conditions, often leading to over-blowing or under-blowing. Therefore, it is necessary to carry out the research of monitoring ash deposition and slagging on boiler heating surfaces.MethodsThe medium and low temperature reheater of a 660 MW ultra-supercritical coal-fired boiler is selected as the research object. Firstly, the actual heat transfer rate is calculated using the working fluid parameters at the heating surface inlet and outlet. Then, a multi-factor prediction model for ash deposition and slagging was constructed based on a back propagation neural network (BPNN). Finally, by integrating multiple prediction models with different time‑series and multiple influencing factors, a comprehensive monitoring model for ash deposition and slagging on the boiler heating surfaces was established.ResultsThe model achieves a root mean square error of 5 635.33 MJ/h and a mean absolute percentage error of 0.62%, significantly outperforming conventional models.ConclusionsThe proposed method can fully exploit useful information from historical data, enabling online monitoring of ash deposition and slagging degree on boiler heating surfaces. It provides data support for intelligent soot‑blowing in boilers.
ObjectivesPower amplification represents a necessary step in transitioning burners from laboratory experiments to engineering applications. Micro-mixed combustion is a highly promising approach for methane hydrogen-doped combustion. However, there is limited research on power amplification for this type of burner within current design and development frameworks. Therefore, research is conducted specifically on megawatt-scale burners.MethodsBased on field experiments, the amplification characteristics of methane hydrogen-doped micro-mixed combustion in a megawatt-scale gas boiler are investigated using numerical simulation. Comparisons between experiment and simulation of a micro-diffusion burner show that increasing hydrogen doping ratio increases the overall combustion reaction rate, resulting in increased outlet temperature and NOx emissions. Based on the characteristics of hydrogen combustion, a power amplification design principle using constant nozzle flow velocity and constant furnace volumetric heat load is proposed. Additionally, two design schemes—fixed nozzle number and fixed nozzle diameter—are compared in terms of combustion thermal characteristics of micro-mixed hydrogen-doped fuel at different power levels.ResultsWith the power amplification of the burner, the overall combustor temperature and NOx emissions decrease, and the rate of decrease gradually diminishes. Differences are observed in axial temperature, velocity distribution, and outlet NOx emissions between the two schemes. However, as the power increases, the difference between the two schemes gradually diminishes. Selecting appropriate burner nozzle and furnace scale-up design schemes can effectively reduce NOx emissions.ConclusionsThe findings can provide insights for the design of high-power micro-mixed hydrogen-doped combustion equipment.
