Challenges such as adapting to renewable energy power fluctuations and coping with start-stop mechanisms exist during the operation of electrolyzers. Capacity configuration models based on long time scales struggle to accurately capture these dynamic characteristics, which reduces the accuracy of capacity planning for hydrogen production systems.

Taking a hybrid hydrogen production system composed of alkaline (ALK) electrolyzers and proton exchange membrane (PEM) electrolyzers as the research object, this paper proposes a minute-level time-scale capacity planning method for hybrid hydrogen production. First, a minute-level electrolyzer start-stop control model is designed to accurately describe the operating states of the two types of electrolyzers. Second, considering ALK electrolyzers’ poor adaptability to power fluctuations and long start-stop time, we develop a power allocation strategy that prioritizes the stable operation of ALK electrolyzers. Finally, we conduct multi-objective optimization for the capacity planning problem of the hybrid hydrogen production system, and the Pareto solution set of the model is obtained via the augmented ε-constraint method.

Simulation results show that under the condition of 20 MW installed wind power capacity and 20 MW installed photovoltaic capacity, with a total system investment cost of 25 million yuan, the proposed 1-minute time-scale model increases the average daily hydrogen production by 14.7%, reduces the unit hydrogen production cost by 7.5%, and decreases the renewable energy curtailment rate by 63.6% compared with the traditional 15-minute time-scale model. In addition, with 1-minute scheduling accuracy, the ALK/PEM electrolyzer capacity ratio is gradually optimized as investment increases: when the total investment is below 25 million yuan, the proportion of ALK electrolyzers exceeds 90%; when the total investment exceeds 30 million yuan, the investment proportion of PEM electrolyzers rises to 19.4%. In contrast, for the 15-minute time-scale model, the ALK/PEM capacity ratio reaches 4:1 even when the investment is only 20 million yuan. This prematurely increased proportion of PEM electrolyzers not only deviates from practical engineering conditions but also degrades overall system performance, indicating that coarse time-scale scheduling may lead to capacity mismatch.

Hybrid tower structures have emerged as crucial supporting structures for wind turbine generators in low-wind-speed regions. However, the hybrid towers have a complicated construction process, leading to frequent occurrences of defects such as tower step misalignment and structural adhesive deficiency. These defects pose a significant threat to the structural integrity of the tower, thereby impacting the overall reliability and economic efficiency of wind power projects. There is an urgent need for safe construction and efficient operation and maintenance guidance.

Taking the damage of a 4.2 MW concrete-steel hybrid tower as a case, ABAQUS was used to establish finite element models of the tower structure under various defect scenarios, including joint misalignment, insufficient adhesive at joint interfaces, and excessive pad height, to investigate the influence mechanism on structural performance and derive engineering recommendations.

Excessive pad height, insufficient adhesive application, inadequate concrete strength, joint misalignment, and insufficient pretensioning of steel strands collectively contribute to the degradation of the tower structure's safety. Based on the aforementioned research findings, this paper proposes targeted engineering optimization recommendations.

Under the rigorous guidance of China’s national strategic goals of “dual carbon” (carbon peaking and carbon neutrality), the transformation of energy structures in heavy industries has become a critical priority. In particular, establishing an innovative energy supply system for the alumina digestion process that is predominantly powered by green electricity is essential. This transition is pivotal for promoting a comprehensive zero-carbon transformation, significantly enhancing the utilization efficiency of renewable energy resources, and effectively reducing the operational costs of the system.

To address these challenges, this study constructs a novel green electricity-molten salt synergistic hybrid system, supported by advanced wind and solar forecasting techniques, to supply reliable power for the alumina digestion process. Regarding the methodological framework, a sophisticated hybrid prediction model combining the autoregressive integrated moving average model (ARIMA) and a long short-term memory (LSTM) network is developed to achieve precise meteorological data forecasting. Furthermore, the non-dominated sorting genetic algorithm III (NSGA-III), integrated with a fuzzy satisfaction function, is utilized to conduct a rigorous multi-objective optimization configuration study. This comprehensive simulation covers a continuous period of 1 week (168 h) and evaluates performance across 12 typical operating scenarios to ensure robustness.

The comprehensive empirical results indicate that the prediction accuracy of the proposed model is exceptionally high, with the average coefficient of determination (R²) value for meteorological data exceeding 97.5%, thereby providing reliable data input for system control. The optimized full-equipment configuration demonstrates significant advantages in maintaining a cross-seasonal stable energy supply, achieving an optimal balance among economic viability, environmental impact, and overall energy efficiency. Specifically, the minimum weekly operating cost is recorded at 6.322 9 million yuan, the lowest carbon emission is reduced to 44.97 t, and the maximum effective green electricity rate reaches an impressive 99.67%. Additionally, the configuration of molten salt thermal energy storage equipment effectively smooths the inherent fluctuations of green electricity and drastically reduces reliance on external grid power purchases, resulting in an average green electricity supply proportion of 98.23%. The strong synergistic effect between wind turbine equipment and photovoltaic equipment successfully compensates for the temporal and intensity limitations of single energy sources, significantly improving both the effective green electricity rate and the stability of the system’s energy supply.

The proposed green electricity-molten salt synergistic hybrid system successfully realizes a stable green power supply, providing robust theoretical support for the optimization of low-carbon, low-cost alumina digestion processes powered directly by green electricity.

Laser-induced breakdown spectroscopy (LIBS) holds significant potential for application in the field of coal property analysis, due to its advantages of eliminating complex sample pretreatment, enabling multi-parameter synchronous detection, and offering rapid analysis. However, discrepancies in spectral responses exist among different instruments. These discrepancies cause severe accuracy degradation when a quantitative model trained on spectra acquired by a master instrument is applied to slave instruments. Therefore, this study constructed cross-instrument LIBS quantitative analysis models of coal property by integrating TrAdaBoost transfer learning with various machine-learning algorithms.

Two LIBS-based coal analyzers were designated as the master and slave instruments respectively, and LIBS spectra were collected from different numbers of coal samples on both devices. Random forest (RF), support-vector regression (SVR), and their TrAdaBoost-enhanced counterparts (TrA-RF and TrA-SVR) were employed to build quantitative analysis models. Model performance was evaluated by predicting the coal properties of unknown coal samples on the slave instrument.

The results indicated that both TrA-RF and TrA-SVR models significantly outperformed their non-transfer counterparts. The TrA-RF model achieved the highest accuracy for calorific value, ash content, and carbon content. Compared with RF model, the mean absolute errors decreased from 1.390 MJ/kg, 4.774 %, and 3.826 % to 0.654 MJ/kg, 2.338%, and 1.927%, respectively. TrA-SVR model yielded the highest accuracy for volatile matter prediction. Compared with the SVR model, the mean absolute error decreased from 2.722% (SVR) to 2.524%.

These findings demonstrate that coupling transfer learning with an appropriate base learner markedly enhances the adaptability of LIBS-based coal property models across different instruments.

Under the background of China’s “dual-carbon” strategy, the efficient and stable operation of coal-fired power station boilers is crucial for peak shaving of the power grid, and temperature field monitoring is one of the keys to ensuring the safe and efficient operation of boilers. Addressing challenges such as decreased combustion stability, severe load fluctuations, and temperature field reconstruction under deep peak shaving conditions, this study focuses on the precise detection of cross-sectional temperature fields in opposed-fired boilers. A dual-band furnace temperature field reconstruction system that integrates the inverse Monte Carlo method with the Tikhonov regularization algorithm is proposed.

This system innovatively incorporates wireless detectors, breaking through the limitations of conventional wired devices that are difficult to route in complex boiler spaces, and providing hardware support for real-time monitoring under deep peak shaving conditions. On-site furnace tests conducted under multi-load conditions (25%, 33%, and 66% load) of a 630 MW opposed-fired boiler revealed that there were significant differences in temperature fields between deep peak shaving and conventional operation.

At low loads in the main combustion zone, the high-temperature center deviates from the geometric center (towards the left and front walls), while at high loads, it tends to be evenly distributed. The high-temperature zone in the burnout zone is concentrated near the walls of the front and rear sections. At low loads, there is a significant difference in the area of the high-temperature zones on the front and rear walls, indicating poor combustion uniformity. Meanwhile, extinction coefficient analysis further indicates that the burnout zone (0.91~0.94) is significantly higher than the main combustion zone (0.26~0.51), verifying the differences in flame radiation characteristics between deep peak shaving and conventional operation.

Through collaborative analysis of algorithm optimization, hardware innovation, and the structural characteristics of opposed-fired boilers, a high-precision temperature field monitoring system has been constructed, providing key data support and engineering pathways for combustion state diagnosis, operation optimization, and safety regulation under deep peak shaving conditions. This has important practical value for the deep peak shaving operation of coal-fired boilers.

Against the problem that the conventional timed and quantitative soot blowing mode is prone to cause local over-blowing and under-blowing of the waterwall, studies are carried out by relying on effective monitoring methods. As slag deposition on waterwall is a key factor affecting the safe and economic operation of thermal power boilers, long-term unresolved local over-blowing or under-blowing will not only accelerate the corrosion and wear of the waterwall, but also increase energy consumption and operational costs of power plants. Therefore, the core goal of this research is to establish a precise soot blowing algorithm to replace the conventional timed and quantitative soot blowing mode and realize adaptive and efficient soot blowing control.

A new type of waterwall slagging monitoring sensor was used to monitor the in-furnace waterwall surface temperature, which can collect real-time, continuous and high-precision temperature data to lay a reliable foundation for subsequent model construction. Three machine learning methods, including eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and random forest regression (RFR), were compared to construct theoretical in-furnace waterwall surface temperature models under clean waterwall conditions, and a calculation method for waterwall slagging factor was proposed. On this basis, a precise soot blowing algorithm was established. To verify the optimization effect of this algorithm, a 3-month practical application test was carried out in a 1 030 MW thermal power unit, and the operation data was compared with the original timed and quantitative soot blowing mode.

The research shows that the theoretical in-furnace waterwall surface temperature model established by the random forest regression method performed the best, with an R² of 0.92, δ_MSE of 73.77, and δ_MAPE of 1.16%. The implementation of the precise soot blowing algorithm is significantly better than the original quantitative soot blowing mode, with a significant reduction in soot blowing frequency and no deterioration of the waterwall slagging state, and the local maximum temperature of the waterwall is controlled within the safe range, avoiding the risk of tube explosion caused by overheating.

This algorithm reduces the consumption of soot blowing steam while ensuring the safety of boiler operation, which directly reduces the daily operation cost of the power plant. Moreover, the reduction of soot blowing frequency also reduces the influence of high-temperature steam on the waterwall, effectively extending the service life of the waterwall and reducing the maintenance cost of the boiler. It can be popularized and applied in thermal power plants of different capacities, and has extremely high application value.