[Objective] In view of the prominent supply chain security risks associated with the FF(Foundation Fieldbus) master station in power industrial control systems, where the relevant chips, protocol stacks, and configuration tools have long been heavily reliant on imports, a full-chain localization research and development of the FF H1 fieldbus master station was carried out based on the Ruiwo DCS technical platform. The aim of this work is to break the foreign monopoly on key components and basic software, achieve completely independent and controllable master station hardware and software, and provide solid technical support for the supply chain security of power industrial control systems and for national energy security.

[Methods] In terms of hardware, domestic discrete components are adopted to construct the MAU(Medium Attachment Unit) physical layer circuit. In the transmitting path, precise modulation of bus signal was realized through reference voltage selection combined with a constant-current closed-loop control circuit, which ensures that the transmission current accurately tracks the command value and thereby improves the accuracy of signal modulation. In the receiving path, frequency-selective amplification was adopted to extract the modulated bus signal from background noise, and hysteresis comparison circuits are used for receiving and shaping bus signals to guarantee the integrity of signal transmission. For the software side, the FF communication protocol stack was programmed on an embedded processor, realizing such core functions as system management services, link scheduling, and cyclic and acyclic data communication based on virtual communication relationships, which fully meet the requirements of real-time performance and reliability for field-level control. In addition, the supporting configuration tool was developed based on the Kylin operating system, covering the complete engineering application workflow , including device description file parsing, function block configuration, network configuration, online monitoring and maintenance. 

[Results] Systematic tests showed that, with 16 slave devices mounted on each of the two network segments, device configuration and data communication could operate stably and reliably. The read and write status of the four typical types of process data, namely AI(analog input), AO(analog output), DI(digital input), DO(digital output), remained normal throughout the tests; and no error frames were detected by continuous message monitoring under long-term operating conditions, which fully verifies the correctness of master station’s communication functions and data interaction as well as the reliability of the independently developed hardware and software. 

[Conclusion] The developed FF-H1 master station achieves full-chain independent control ranging from chips and protocol stacks to configuration tools, and its communication performance satisfies engineering application requirements. The results indicate that the master station can support large-scale supporting application of the Ruiwo DCS, and the work is therefore of great practical significance for improving the supply chain security of power industrial control systems and for safeguarding energy security.

[Objective] To confront the enduring challenges in governing multi-source heterogeneous data from power generation equipment-such as the frequent occurrence of missing fields, semantic ambiguity, and the persistent problem where completion results violate complex business rules-this paper introduces an innovative data quality enhancement method that seamlessly integrates semantic disambiguation with knowledge graph constraints, aiming to achieve highly accurate missing-field completion and standardized normalization of fault-related texts. 

[Methods] Using equipment unique identifiers and timestamps as primary keys, a multi-source corpus is constructed by associating equipment ledgers, SCADA alarm records, and operation-maintenance logs. DeepSeek-V3 is then employed to generate multiple types of candidate completions based on existing fields and similar historical samples, while a domain-adapted BERT model-fine-tuned on power generation datasets with a partial freezing and layer-wise unfreezing strategy-evaluates the semantic consistency between candidates and contexts. The fine-tuning process uses a batch size of 16, the AdamW optimizer, and a maximum sequence length of 512. A local module further calculates candidate support degrees. Concurrently, a specialized knowledge graph for power generation equipment is built to explicitly define mandatory rules-including attribute ranges, equipment hierarchies, and temporal logic-alongside soft constraints. A dual-factor dynamic gating mechanism is designed to dynamically fuse semantic scores, candidate support degrees, and knowledge graph soft compliance scores using a semantic complexity factor S and a domain constraint strength factor C, where hard constraints strictly filter out invalid candidates and comprehensive reranking optimizes the final output. 

[Results] Extensive evaluations are conducted on seventy-six thousand three hundred and sixty actual operational records from thermal power, wind power, and photovoltaic systems of a regional subsidiary of a major power generation group spanning 2020 to 2024. The dataset is divided into training, validation, and test sets in a ratio of 7:1:2. The proposed method achieves macro-average completion accuracy, semantic consistency, and business compliance rates of ninety-five point zero percent, ninety-three point nine percent, and ninety-five point eight percent respectively, significantly outperforming standalone BERT, DeepSeek zero-shot, and fixed-weight fusion methods. Ablation studies further indicate that removing the knowledge graph constraints causes the business compliance rate to plummet to eighty-four point three percent, and removing the dual-factor gating degrades all three metrics, fully confirming the effectiveness of each core module. 

[Conclusion] By deeply synergizing semantic understanding with explicit business rules, the proposed method effectively overcomes semantic ambiguity and rule conflicts in data completion for power generation equipment, significantly improving data quality and text normalization levels, and providing a reliable technical reference for data governance in the energy and power sector.

[Objective] The supercritical carbon dioxide (sCO₂) Brayton cycle has become a promising power‑generation technology owing to its high cycle efficiency and compact component layout. Nevertheless, condensation phase‑change near the critical point inside centrifugal compressors severely threatens operational reliability. Severe working fluid condensation will not only lead to mass flow degradation and shrinkage of the stable operating envelope, but liquid droplet impingement upon high speed spinning blades may also trigger hidden risks concerning blade structural integrity and mechanical strength. The suction‑side leading‑edge grooving modification can mitigate local condensation, yet it introduces extra flow perturbation and aerodynamic performance degradation. It remains insufficiently understood how to coordinate the condensation‑suppression benefit and efficiency penalty brought by grooved blades. This work aims to explore the collaborative optimization between the grooved‑blade structure and inlet‑temperature adjustment for sCO₂ centrifugal compressors. 

[Methods] Numerical simulation based on computational fluid dynamics was carried out for the sCO₂ centrifugal compressor with suction‑side leading‑edge grooving. The homogeneous binary mixture model was adopted to capture two‑phase condensation behavior, while the k‑ω SST turbulence model was employed to resolve complex boundary‑layer flow and local separation. After grid‑independence examination and model validation against Sandia National Laboratory’s experimental data, the internal flow field, condensation characteristics, and overall aerodynamic performance were systematically investigated under inlet temperatures ranging from 310 K to 319 K at the fixed mass‑flow rate of 61 kg/s, which corresponds to the peak‑efficiency working point of the prototype compressor. 

[Results] The grooved‑blade configuration effectively elevates static pressure and static temperature within the core condensation zone close to the blade leading‑edge, drastically cutting the dimensionless condensation volume. However, local vortices and flow disturbance induced by the groove reduce the maximum isentropic efficiency from 84.60 % of the baseline smooth blade down to 83.20 %. Raising the inlet temperature properly compensates for such performance loss. Within 310 K‑315 K, compressor isentropic efficiency climbs continuously from 82.1 % to 83.5 %. As inlet temperature increases from 310 K to 314 K, the dimensionless condensation volume drops sharply and approaches nearly zero beyond 314 K, proving that reasonable inlet‑temperature elevation eliminates most impeller internal condensation regions. When inlet temperature exceeds 315 K, the condensation‑suppression capability barely improves further. Even so, reduced working‑fluid density impairs the pressure‑boosting capacity, leading to continuous decline of total pressure ratio and gradual deterioration of isentropic efficiency. 

[Conclusions] Balancing condensation‑control effectiveness, isentropic efficiency, and pressure‑ratio characteristics comprehensively, 315 K is identified as the optimal inlet operating temperature for this grooved‑blade sCO₂ centrifugal compressor. At this condition, the condensation risk is well restrained, and the aerodynamic penalty originating from blade grooving is largely recovered. The present findings provide practical references for blade‑structure optimization and operating‑parameter matching of sCO₂ centrifugal compressors in Brayton‑cycle power systems.

[Objective] Deep peak regulation has become an essential operational requirement for thermal power units to adapt to the large-scale grid integration of new energy sources. Lean coal-fired subcritical boilers frequently encounter unstable combustion and require auxiliary oil ignition during ultra-low load operation, which restricts the flexible peak-shaving capacity of power plants. Taking a successful deep peak regulation renovation project of a lean coal-fired boiler as the research basis, this paper conducts a comprehensive study on the adjustable recirculation zone staged ignition combustion technology. It systematically elaborates the core content of the technology from three key dimensions: technical working principle, computational numerical simulation, and practical engineering application effects. This research aims to validate the technical feasibility of applying the proposed technology to realize full oil-free stable combustion for subcritical lean coal-fired boilers under ultra-low load conditions, and to provide reliable technical support and practical guidance for the promotion and implementation of similar boiler renovation projects. 

[Methods] In this study, a high-precision refined Computational Fluid Dynamics (CFD) model was established to carry out full-furnace combustion numerical simulation under the typical ultra-low load condition of 30% Economic Continuous Rating (30% ECR). Based on the simulation calculation data, the dynamic variation characteristics of furnace internal flow field distribution, combustion temperature field uniformity, and water wall heat load were comprehensively analyzed. In addition, the structural composition and stable combustion mechanism of the adjustable recirculation zone staged ignition burner were expounded in detail, and the field operation data and test results of the engineering renovation were summarized and analyzed to verify the practical performance of the technology. 

[Results] The numerical simulation results demonstrate that excellent combustion performance is achieved at the 30% ECR ultra-low load working condition. Specifically, the pulverized coal burnout rate reaches 99.86%, and the flue gas carbon monoxide (CO) concentration is as low as 4.38 mg·m-3, indicating thorough fuel combustion. The field steady combustion tests conducted at 30% ECR load after technical renovation further verify the remarkable practical effect. When the boiler operates with lean coal, the minimum oil-free stable combustion electric load is successfully reduced to 90 MW. During the whole ultra-low load operation period, the furnace negative pressure fluctuates steadily within the normal safe range. The denitrification system can maintain continuous and stable operation, and all flue gas emission indicators fully meet the national ultra-low emission standards. 

[Conclusion] The adjustable recirculation zone staged ignition burner adopts a slotted bluff body structure to construct an active staged ignition mechanism, combined with a flexible adjustable perimeter air design. The innovative structural design effectively solves two key operational problems of lean coal-fired boilers, realizing stable combustion reinforcement under low load conditions and preventing furnace slagging under high load conditions. After the renovation, the corrected thermal efficiency of the boiler reaches 91.47%, which realizes high-efficiency and stable operation of the boiler in a wide load range. All operational indicators of the renovated boiler fully meet the design expectations. This study provides a valuable and practical technical reference for the deep peak regulation transformation of the same type of subcritical lean coal-fired boilers, and has good engineering application and promotion value.

[Objective]Under the increasing penetration of renewable energy, the coordinated operation of interconnected multi-microgrids faces the challenge of simultaneously improving economic efficiency, reducing carbon-emission-related costs, and mitigating load fluctuations. Existing dispatch methods generally treat electricity-price and carbon-cost signals as predetermined parameters, and the interaction among internal settlement prices, carbon-cost signals, and inter-microgrid power transactions is insufficiently considered. To address these issues, a coordinated low-carbon dispatch method for multi-microgrids under electricity-carbon market coupling is proposed. 

[Methods]An interconnected multi-microgrid system consisting of three heterogeneous microgrids is established, incorporating wind power, photovoltaic generation, conventional generating units, energy storage systems, demand response, main-grid power exchange, and inter-microgrid power exchange. The 24-hour internal settlement electricity price and carbon-emission cost guidance coefficient are jointly optimized as decision variables. A discount-based pricing mechanism is introduced for inter-microgrid electricity transactions based on the internal settlement electricity price, thereby establishing a price linkage between internal power exchange and electricity trading with the main grid. On this basis, a coordinated low-carbon dispatch model is formulated by considering conventional-unit output, energy-storage charging and discharging, demand response, main-grid power exchange, inter-microgrid power exchange, and the corresponding operational constraints. An improved water flow optimization (IWFO) algorithm is developed to search for the electricity-carbon signal combinations. The algorithm incorporates opposition-based learning initialization, Lévy-flight-enhanced turbulence, a gravitational sinking-flow mechanism, and a Cauchy-Gaussian hybrid stagnation-escape strategy. For each candidate electricity-carbon signal combination, the corresponding multi-microgrid dispatch scheme is solved, and the comprehensive operating performance of the system is used to update the search process, thereby realizing the coordinated optimization of electricity-carbon signals and dispatch strategies. 

[Results]The case-study results show that the dynamic electricity-carbon signals can promote the coordinated participation of energy storage, demand response, and inter-microgrid power exchange. Compared with the fixed electricity-carbon signal case, the total operating cost decreases from CNY 3,904.3 to CNY 3,808.7, representing a reduction of approximately 2.4%. The carbon-emission-related cost decreases by 10.7%, while the peak-to-valley load difference decreases by 18.2%. The results further show that joint optimization of the internal settlement electricity price and carbon-emission cost guidance coefficient provides more effective comprehensive regulation than optimization of either signal alone. Benchmark-function tests and ablation experiments also demonstrate the improved optimization performance of IWFO. 

[Conclusion]The proposed method establishes a coordinated interaction among internal electricity-price signals, carbon-cost guidance, and multi-microgrid operating decisions. By coordinating conventional generation, energy storage, demand response, and inter-microgrid power exchange, the proposed approach promotes temporal and spatial complementarity among heterogeneous microgrids and improves the overall coordination among economic operation, carbon-emission-related cost control, and load regulation.

[Objective] Data-driven models for coal mill outlet temperature prediction often have difficulty maintaining high prediction accuracy while also satisfying the physical requirement of energy balance. This study proposes an outlet temperature prediction method that incorporates physical priors of the coal mill, with the aim of improving both the numerical accuracy and the physical consistency of the prediction results. 

[Methods] Operating data from an MPS medium-speed coal mill in a 350 MW coal-fired generating unit were used to construct a Physics-Guided Long Short-Term Memory-Physics-Informed Neural Network (PG-LSTM-PINN). The method introduces the air-coal ratio and hot-air ratio to represent the matching relationship between primary air and coal feed and the mixing state of hot and cold air. Differences between operating conditions are then used to correct the temporal attention mechanism so that historical information is weighted according to its relevance to the current condition. A thermal-inertia decay factor is embedded in the forget gate of the Long Short-Term Memory (LSTM) network to represent the dynamic influence of the mill thermal process on state updating. In addition, a physical loss is constructed from the energy-balance relationship and incorporated into model training. Model comparison, physical-consistency evaluation, ablation experiments, reduced-training-data experiments, and physical-loss-weight sensitivity experiments were conducted to evaluate the contribution and effectiveness of the proposed method. Together, these experiments evaluate prediction accuracy, energy-balance physical consistency, and performance with limited training data. 

[Results] PG-LSTM-PINN achieved a coefficient of determination (R²) of 0.981 1, a root mean square error (δRMSE) of 0.078 2 °C, and a mean absolute error (δMAE) of 0.061 1 °C. Compared with LSTM, its δRMSE and δMAE were reduced by 50.69% and 52.41%, respectively. The physical-residual root mean square error (δRMSE,p) and physical-residual mean absolute error (δMAE,p) were 21.35 kJ/s and 16.23 kJ/s, respectively. In the ablation experiments, removing any physical-guidance module increased both the prediction error and the physical residual, indicating that each module contributed to the overall performance of the complete model. In the reduced-training-data experiments, PG-LSTM-PINN maintained relatively low prediction errors at all tested training proportions. The sensitivity analysis of the physical-loss weight showed that the lowest δRMSE was obtained when η was 1.301 3×10⁻⁹. 

[Conclusion] The proposed PG-LSTM-PINN integrates operating-condition representation, temporal information weighting, thermal-inertia modeling, and energy-balance constraints within a unified prediction framework. The results show that it can improve the accuracy of coal mill outlet temperature prediction, reduce the energy-balance physical residual, and provide better prediction performance when the amount of training data is limited.

Compressed Carbon Dioxide Energy Storage (CCES) is a new type of energy storage technology that adapts to large-scale integration of new energy, ensuring the safe and stable operation of new power systems. With advantages such as being environmentally friendly, highly efficient, strong adaptability to operating conditions, and good energy storage stability, it has broad engineering application prospects in power peak regulation, frequency regulation, and long-duration energy storage, and is a current hotspot in energy storage technology research. 

[Objective] To further optimize the actual operating performance of low-pressure compressed carbon dioxide energy storage systems and improve the system operation and control framework, this paper conducts a dedicated study on the operating characteristics of the low-pressure energy release phase in low-pressure CCES systems, investigating the influence of key operating parameters on the overall energy release performance of the system, providing reliable experimental support and theoretical basis for the engineering implementation, parameter optimization, and stable operation of low-pressure CCES systems. 

[Methods] This study focuses on a 0-1 MPa low-pressure compressed carbon dioxide energy storage system, concentrating on the core control elements during the energy release process. Through controlled variable experiments, the influence of two major parameters—valve opening and heating temperature—on key performance indicators, such as energy release duration, output power, and operational stability, is systematically investigated. 

[Results] The results indicate that within the experimental conditions of this study, valve opening is the core parameter affecting the energy release performance of low-pressure CCES systems, significantly regulating the continuity of energy release. Compared with the fully open (100%) condition, a 40% valve opening condition effectively extends the energy release duration by 28%, greatly enhancing the system's long-term continuous energy release capability. Meanwhile, heating temperature can effectively increase the power output of the expander, with the 70°C heating condition showing a more pronounced power improvement than the 60°C condition, particularly under valve openings less than 50%. 

[Conclusion] Valve opening and heating temperature are key parameters for regulating the energy release characteristics of low-pressure CCES systems. Considering the comparison of operational stability, power generation, and energy release duration, within the experimental range of this study, the 50% valve opening combined with 70°C heating condition performs better in meeting long-duration energy release demands while balancing energy release continuity and operational stability. Under this condition, the power fluctuation amplitude is only one-third of that under the fully open condition, making it suitable for long-term stable energy release in power systems and providing an important reference for the practical operation and control of low-pressure CCES systems.

[Objective] Indirect cooling towers in coal-fired power plants are characterized by strong thermal inertia and stochastic environmental disturbances. These features make it difficult for conventional numerical simulation and physics-based modeling approaches to accurately predict future operating states. As a result, winter anti-freezing operation often relies on conservative control strategies, making it difficult to simultaneously ensure system safety and operating economy. To address this problem, this study proposes a modular machine-learning model selection strategy for future-state prediction of indirect cooling towers. 

[Methods] Considering the differentiated characteristics of the sector, total return-water temperature, and condenser modules, four machine-learning algorithms were comparatively evaluated for the corresponding prediction tasks. According to the prediction performance of different modules, long short-term memory (LSTM), artificial neural network (ANN), and random forest (RF) were selected as the optimal prediction algorithms for the respective modules. This modular configuration avoids the limitation of using a single model to represent the nonlinear and time-dependent characteristics of the entire indirect cooling system. On this basis, a machine-learning-based self-evolution prediction framework with closed-loop iteration of circulating-water temperature was developed. Different from the conventional static-input prediction mode, the proposed framework follows the actual circulating-water flow path and establishes a dynamic recursive closed loop among key water-temperature nodes. The predicted water temperature at the current step is used in the subsequent prediction step, allowing the future operating state of the system to evolve continuously along the prediction horizon. In this way, the proposed framework can continuously propagate the predicted operating state through the sector, total return-water, and condenser modules and thereby realize recursive future-state prediction. 

[Results] The proposed framework was validated using actual winter operating data from an indirect cooling tower of a 2×1 000 MW coal-fired power unit. The results demonstrate that the framework can recursively predict the operating state of the system for the following 1 h. During 95% of the prediction period, the prediction errors of the key operating parameters, including sector return-water temperature, total return-water temperature, unit back pressure, and total inlet-water temperature, were maintained within ±0.65 °C, ±0.60 °C, ±0.57 kPa, and ±0.30 °C, respectively. Based on the developed self-evolution prediction framework, virtual regulation and scenario-based prediction can also be performed for key operating conditions. By changing candidate operating strategies or external operating conditions within the prediction framework, the future response of the indirect cooling system can be quantitatively evaluated before actual regulation is implemented. Therefore, the framework is not limited to reproducing the future trajectory under the current operating condition, but can also be used to analyze how different operating strategies and external-condition variations may affect the subsequent system state. 

[Conclusion] This method eliminates the need for complex physical analytical expressions and simulation modeling, effectively solving prediction challenges caused by significant operational lag and strong coupling of operational parameters in indirect cooling towers. It provides a novel technical solution for safe, economical operation and intelligent control of indirect cooling towers in power plants during winter.

[Objective] Compressed carbon dioxide energy storage (CCES) combines a high-density working fluid, closed-cycle operation, flexible siting, and the potential for long-duration energy storage, making it a promising physical storage option for power systems with high renewable-energy penetration. However, the reported performance of CCES varies substantially because liquid, transcritical, and supercritical routes differ in storage state, pressure level, heat-transfer process, low-pressure-side storage method, and evaluation boundary. This review aims to clarify the operating characteristics, performance ranges, equipment requirements, and engineering applicability of the main CCES routes, and to identify the technical barriers to large-scale deployment. 

[Methods] The literature was reviewed from the perspectives of thermophysical properties, system configuration, storage mode, key equipment, cycle optimization, multi-energy coupling, dynamic behavior, and demonstration projects. Liquid, transcritical, and supercritical CCES systems were compared in terms of operating boundaries, round-trip efficiency, energy storage density, storage-vessel requirements, heat and cold recovery, and component matching. Representative studies on underground storage, flexible gas holders, adsorption storage, hydraulic compensation, waste-heat recovery, heat-pump integration, cold-electricity cogeneration, and external heat or cold sources were summarized. Engineering projects in Europe and China were also examined to assess the transition from conceptual studies to demonstration-scale applications. 

[Results] CCES performance is primarily governed by the CO₂ storage state, low-pressure-side storage mode, pressure ratio, heat-exchanger matching, turbomachinery efficiency, and system boundary. Liquid or gas-liquid phase-change systems generally provide relatively high energy storage density, but require liquefaction, vaporization, cold storage, and phase-change heat transfer. Transcritical systems offer flexible ground-based deployment and can balance efficiency with siting adaptability, but are sensitive to thermophysical-property variations near the critical region and may require large low-pressure storage volumes. Supercritical systems are comparatively compact and avoid phase-change control, but impose stricter requirements on high-pressure vessels, sealing, materials, and turbomachinery manufacture. Typical transcritical and supercritical systems generally report round-trip efficiencies of 50%~70%. Nevertheless, direct comparison is difficult when external heat, cold-energy products, design values, or different storage boundaries are included. Waste-heat recovery, heat-pump coupling, cascaded thermal storage, and cold-electricity cogeneration can improve overall energy utilization, but often increase equipment count, control complexity, and capital cost. Existing megawatt-scale demonstrations and larger projects under construction indicate that CCES is progressing from thermodynamic verification toward engineering demonstration. 

[Conclusions] CCES has potential for long-duration energy storage and reduced dependence on large underground gas caverns, but different routes should not be evaluated using a single efficiency indicator. Future research should establish consistent performance boundaries and focus on transient control near the critical region, off-design turbomachinery performance, heat-exchanger network optimization, reliable sealing and material selection, low-cost storage devices, cascaded heat and cold utilization, and experimentally validated dynamic models. Long-term operating data, safety standards, economic assessment methods, and market mechanisms are also required to support commercial deployment.

[Objective] Ash deposition on the heating surfaces of waste incineration boilers increases thermal resistance, weakens heat transfer, raises flue gas temperature, and reduces boiler operating efficiency. Powerful wave ash cleaning can generate high-intensity pressure waves and gas disturbance through rapid combustion of a combustible gas-air mixture, showing potential advantages over conventional acoustic soot blowing, steam soot blowing, and mechanical rapping. However, the ash fragmentation process under powerful wave action and the influence of outlet working pressure on cleaning performance remain insufficiently understood. This study investigates the dynamic ash removal process and pressure-dependent cleaning effect of a powerful wave ash cleaner in a waste incineration boiler, aiming to provide a basis for engineering application and parameter optimization. 

[Methods] A numerical model of the powerful wave ash cleaning process was established by combining the cohesive zone model with the coupled Eulerian-Lagrangian method. The gas domain was described using the Eulerian formulation, while the water-wall tube and ash deposit layer were described using the Lagrangian formulation, so as to simulate the transient fluid-solid interaction between the cleaning gas and the ash layer. Cohesive elements were embedded in the ash deposit, and the damage ratio of cohesive elements was used to characterize ash fragmentation. Four outlet working pressures, 25, 30, 35, and 40 MPa, were selected to compare the evolution of ash damage. Field tests were also conducted on a waste incineration boiler, in which the flue gas temperature at the inlet of the three-pass superheater and the main steam flow rate were collected. A normalized heat-transfer recovery index was introduced to reduce the influence of load fluctuation. 

[Results] The simulation results show that the damage degree of the ash deposit layer increases with increasing outlet working pressure, whereas the initial time of ash damage is only slightly affected by pressure. The field test results further verified this trend. When the outlet working pressure increased from 30 MPa to 35 MPa, corresponding to a pressure increase of 16.67%, the flue gas temperature drop at the left inlet of the three-pass superheater increased from 24.20℃ to 26.85℃, improving by about 11%. Meanwhile, the normalized heat-transfer recovery index increased from 0.53℃·h/t to 0.55℃·h/t, with a relative increase of about 3.8%. 

[Conclusions] The proposed model can reasonably describe the damage evolution of ash deposits during powerful wave ash cleaning. Both simulation and field test results indicate that the powerful wave ash cleaner has favorable cleaning performance, and its ash removal effect is positively correlated with outlet working pressure.