Xinjiang Zhundong coal has abundant reserves and contains a relatively high content of alkaline earth metal elements. The high-calcium fly ash generated from its combustion serves as an excellent raw material for CO₂ sequestration. By adopting the atmospheric pressure direct wet carbonation process, research and optimization analysis were carried out on the carbonation of high-calcium fly ash, focusing on key parameters such as flue gas flow rate, temperature, and solid-liquid ratio. A kinetic model was constructed to determine the key factors and rate-controlling steps. Meanwhile, the performance of this process in chlorine removal and heavy metal removal was evaluated. It was found that, increasing the flue gas flow rate and reducing the solid-liquid ratio can effectively enhance the degree of carbonation per unit mass of fly ash. During the rapid carbonation stage (0~20 min), low temperature is beneficial for increasing the degree of carbonation, but the effect is not significant. In the rapid carbonation zone, the reaction of fly ash is mainly controlled by solid-film diffusion, with a correlation coefficient of 0.917 37 and an activation energy of 10.36 kJ/mol. After optimization by the response surface method, the optimal operating condition parameters are as follows: temperature of 57.1 ℃, flue gas flow rate of 2.86 L/min, and solid-liquid ratio of 200.0 g/L. Under these conditions, the average actual degree of carbonation reaches 30.2%. The chlorine content of the fly ash processed according to these parameters meets the requirements for reinforced products in the JC/T 409—2016 standard. For typical heavy metals such as arsenic and copper, the removal rates reach 88.4% and 55.6% respectively, indicating that this process has a certain detoxification ability. Therefore, the atmospheric wet carbonation of high calcium fly ash in Zhundong has great potential for application.

Direct air carbon capture (DAC) technology has been booming in the past decade, and now it has gradually developed from laboratory toward commercial device. Because the adsorption DAC is more promising than absorption DAC, some companies have launched DAC demonstration projects based on adsorption. However, there is relatively little introduction to these companies and projects based on adsorption DAC in current research, and a comprehensive study has not yet been formed. In view of the above reasons, some representative companies owning adsorption DAC technologies and their projects are investigated through existing literatures and their corporate websites, and the key contents are focused. In addition, the device types of these enterprises are divided into centralized devices and integrated devices according to the arrangement of equipment, and the characteristics of these two types of devices are introduced. By summarizing the characteristics of DAC enterprises and technologies, it is found that most enterprises are committed to reducing operation and investment costs, so the possible cost reduction methods in the future industrialization process are put forward and the effects are analyzed.

Post-combustion carbon capture is the underpinning technology and necessary choice for low-carbon power generation, yet its integration into natural gas combined cycle (NGCC) power plants will significantly reduce the plants’ power generation efficiency. In order to reduce the efficiency penalty of the power plants integrated with decarbonization system and improve the energy utilization efficiency of the integrated system, a novel post-combustion carbon capture process that comprehensively recovers the waste heat and liquefied natural gas cold energy is innovatively proposed. Firstly, the key operating parameters of the conventional carbon capture process, including stripper pressure and lean solvent loading, are optimized with sensitivity analysis. On this basis, design and evaluation of novel process is performed. In the novel process, a back-pressure turbine is utilized to recover the pressure energy of the extracted low-pressure steam and assist the lean vapor compression as well as recover the inter-cooling heat of CO₂ compression to heat the reflux condensate of the stripper, which reduces the minimum regeneration energy consumption by 17.3% (to 3.35 GJ) at the flash pressure drop of 90 kPa. Furthermore, the extracted low-pressure steam is reduced from 68.40 kg/s to 48.95 kg/s by recovering the superheat of steam extraction. Aiming at solving the problems of high energy consumption of the conventional CO₂ compression process and the waste of cold energy in the liquefied natural gas regasification process, a novel CO₂ two-stage compression and intermediate liquefication process is proposed, reducing compression work by 34.5%, and the cooling load and the number of equipment were significantly decreased. Exergy analysis results show that the exergy efficiencies of the novel carbon capture process and CO₂ compression process increase from 23.12% and 62.19% to 29.48% and 65.96%, respectively. The simulation results show that, the net power output of the plant integrated with the novel carbon capture process increases from 341.93 MW to 358.75 MW, resulting in a significant energy saving by increasing the net power output efficiency from 48.85% to 51.25% and decreasing the efficiency penalty from 13.77% to 9.53%.

In the oxygen combustion CO₂ cycle, heat integration of the air separation unit (ASU) is commonly used to improve the matching of the heat recovery process. However, the ASU heat integration increases the heat recovery load, and the relatively low load ramp rate of the ASU affects the overall performance of the system. To eliminate the need for ASU heat integration and further enhance cycle efficiency, a method involving split adiabatic compression is proposed to balance the thermal capacities of the hot and cold streams. A power generation system model based on the gasification oxygen combustion CO₂ cycle is developed in Aspen, and the thermodynamic performance of the system, as well as the effect of ASU heat integration, are analyzed. A recompression system is also introduced for comparison. The results show that, the conventional system with integrated ASU heat has a net efficiency of 43.39%. Compared with a system without heat integration, the power consumption of the ASU increases by 19.9 MW, while 180.8 MW of heat integration is provided, resulting in a 1.64 percentage points increase in net efficiency. Considering limitations in heat recovery, the optimal split mass flow rate for the recompression system is 258.2 kg/s. Compared with the ASU heat integration, the recompression system reduces the heat recovery load by 59.8 MW, and the average heat exchanger temperature difference is further reduced by 3.1 ℃, improving the net efficiency to 43.52%. The study reveals the mechanism by which heat integration affects the efficiency of the oxygen combustion CO₂ cycle and proposes an optimization to decouple the power cycle from the ASU heat integration through the recompression process, providing theoretical guidance for the parameter design of the recompression system.

The TiO₂ surface is functionalized with different concentrations of K₂CO₃ and polyethyleneimine (PEI), and in-depth research on CO₂ adsorption performance and mechanism is conducted. CO₂ low-temperature adsorbent was successfully prepared by ultrasonic impregnation method using K₂CO₃ and PEI as functionalized materials and commercial selective catalytic reduction (SCR) catalyst white embryo (porous TiO₂) as carrier. The physicochemical properties of the modified adsorbents were characterized using X-ray diffraction (XRD), differential thermogravimetry (DTG), Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The results indicate that, K₂CO₃ and PEI activate the porous structure of TiO₂, enhancing the density of surface alkaline active sites. This enhancement facilitates the accommodation of PEI and K₂CO₃, exposes adsorption active sites, and promotes CO₂ diffusion and CO₂ adsorption. 50%PEI@TiO₂ introduces numerous active functional groups and alkaline amine sites, achieving a CO₂ adsorption capacity of 2.11 mmol/g. By measuring the CO₂ adsorption by 50%PEI@TiO₂ adsorbent and fitting to Langmuir and Freundlich adsorption isotherm models, it finds that CO₂ is mainly adsorbed physically, and van der Waals force plays a major role during adsorption. The optimal adsorption and desorption temperatures for CO₂ are 50 ℃ and 110 ℃, respectively. The cyclic experiment showed that, compared with PEI, K₂CO₃-loaded adsorbents exhibit greater stability, with a decrease in adsorption capacity of less than 10% after 30 cycles. These findings suggest that functionalized materials based on commercial SCR catalyst TiO₂ pellets hold promise for low-temperature CO₂ capture in industry flue gases.

To solve the problem of severe ash accumulation and slagging on heating surface of boilers caused by a large proportion of blended economic coal, based on the close relationship between the ash fouling layer and the flue gas flow field parameters, the concept of cross-sectional “ash fouling characteristic field” is proposed, and a new intelligent soot blowing control system for boilers is developed, which includes functions such as characteristic field detection and generation, and benchmark field prediction. By comparing the difference in “drop value” and “concentration” between the benchmark feature field and the current feature field, the system can timely and accurately determine the appropriate blowing time, achieving “intelligent perception and on-demand blowing”. The new system solves the problem of lack of measurement points and low accuracy in existing model calculation methods, overcomes the disadvantage of high equipment cost in furnace observation methods, and uses on-site full section data collectors combined with intelligent prediction models for ash pollution characteristic fields to achieve low-cost and high-precision detection of ash and slag accumulation, effectively solving the problems of over blowing and under blowing. The actual application effect of the power plant shows that, after the new system was put into use for 3 months, the monthly blowing frequency decreased by 19.6%, and the monthly blowing steam consumption decreased by 229.0 tons, which is equivalent to a direct economic benefit of 284 000 yuan per year. In addition, the system also brings multiple indirect benefits, such as avoiding sudden coking that causes the unit to stop, extending the service life of the heating surface, and avoiding delayed soot blowing that leads to a decrease in boiler efficiency. The relevant control optimization experience can be used as a reference for similar units in the future.

In order to study the effect of molten salt thermal storage schemes on peak shaving capacity and economy of double reheat condensing units, by taking a 660 MW double reheat condensing unit as an example, seven bypass thermal storage schemes are designed by combing thermal storage with bypass system, considering different thermal storage sources. Through simulation, the changes in indicators of different schemes, such as the minimum power generation load rate, thermal storage load reduction number, compensation for increased peak shaving capacity and coal consumption costs, are studied in the heat storage initial range from 30%THA to 50%THA. The results show that, the minimum power generation load rate of the schemes with multiple parallel heat storage sources are lower than that of the schemes using a single heat source. Scheme VII with three parallel heat storage sources can reduce the minimum power generation load rate to below 18% under different initial heat storage conditions. However, in the Scheme I with superheated steam heat storage, the load reduction number of heat storage exceeds 2.00, and the load reduction capacity per unit of heat storage power is the largest. As the load rate of the initial heat storage condition decreases, there is a maximum compensation for the annual increase in peak shaving capacity, and the compensation for multiple thermal storage heat source schemes is greater than that for a single heat source scheme. The annual increase in coal consumption cost of the scheme including low-pressure bypass heat storage is much higher than other schemes, but it will decrease with the initial working condition of heat storage.

Micro-nano particle doping is an important method for the modification of molten salt thermal storage materials. By taking a binary carbonate molten salt mixture of 40Li₂CO₃-60Na₂CO₃ (mass fraction) as the base molten salt, CuO and CuCl₂ as the dopants, three composite molten salt phase change thermal storage materials, namely CuO-Li₂CO₃-Na₂CO₃, CuCl₂-Li₂CO₃-Na₂CO₃, and CuO-CuCl₂-Li₂CO₃-Na₂CO₃, were re prepared separately using a high-temperature melting method. Moreover, the thermal properties of these compounds were tested, and the effects of additives on the modification of binary carbonate molten salts and composite molten salt phase change thermal storage materials were investigated. The results show that, the melting point of the Li₂CO₃-Na₂CO₃ molten salt with 0.24% CuO addition decreased by 5.2 ℃, the latent heat of phase change decreased by 98.1 J/g, the average specific heat capacity of the solid phase decreased by 0.39 J/(g·℃), and the average specific heat capacity of the liquid phase decreased by 0.77 J/(g·℃). The upper limit of the operating temperature increased by 4 ℃. For the Li₂CO₃-Na₂CO₃-CuCl₂ molten salt with 0.06% CuO addition, the melting point increased by 9.6 ℃, the latent heat of phase change decreased by 15 J/g, the average specific heat capacity of the solid phase increased by 0.07 J/(g·℃), and the average specific heat capacity of the liquid phase increased by 0.12 J/(g·℃). The upper limit of the operating temperature increased by 17 ℃. Both molten salts exhibited improved thermal conductivity performance after the addition of CuO.

The porous media methodology is used to describe the filter bags and their surface ash layer seepage flow. On this basis, the discrete particle model (DPM), as implemented in Fluent software, is employed to simulate the dynamics of dust particles movement and deposition in a baghouse filter. The effects of dust particle size on evolving morphology of the cake on the filter bags are investigated, and the influence of additional resistance of the filter cake on subsequent ash particle deposition is explored. The results show that, when the duration of dust removal is sufficiently long, the cake thickness and pressure loss in the baghouse filter increase in a linear fashion over time. For a constant dust mass flow rate, the pressure loss becomes higher with smaller dust particles. The smaller ash particles are transported more effectively by the upward airflow to the top of the vertical filter bag, whereas the larger particles, due to their weight, tend to settle in the middle area. This results in significant differences in the distribution of cake thickness. In contrast to conventional algorithms that neglect cake resistance, the proposed model, which incorporates this resistance through advanced development in commercial software, predicts a more uniform distribution of dust thickness, which is more consistent with the actual situations. This conclusion can provide references for improving the operational efficiency of baghouse filters.

The early faults of sliding bearings are highly concealed. To accurately predict their vibration amplitude, a deep learning model incorporating a YOLOv8-optimized CBAM attention mechanism is proposed. The CBAM module is embedded between the Backbone and Neck to enhance the model’s focus on critical vibration features. Additionally, an improved complete intersection over union loss function is employed to enhance object detection accuracy. Considering the nonlinear and non-stationary characteristics of vibration data, the empirical mode decomposition (EMD) method is integrated into the model to improve the accuracy of vibration state prediction. The experimental results show that, on the 600 MW steam turbine operation dataset, this method improves the detection accuracy by 2.85 percentage points and 8.50 percentage points compared with that of the conventional YOLOv8 and YOLOv7, respectively. Moreover, the root mean square error (RMSE) is reduces, and the mean absolute error (MAE) decreases. Furthermore, in high-noise environments, the model’s error fluctuation reduces by 30% compared with that of the conventional methods, demonstrating stronger generalization ability and stability.