Search

The deposition of ammonium bisulfate ash (ABS) is a common occurrence in the flue of a boiler in a power plant. This can result in a reduction in the efficiency of the selective catalytic reduction (SCR) process and the air preheater. To reduce the formation of ABS deposits in flue gas systems, extend the service life of SCR catalysts, and improve the efficiency of heat exchangers, online monitoring technology was employed to observe the formation of ABS deposits in a vertical one-dimensional furnace. The objective of this study is to investigate the formation of ABS deposits on heat transfer surfaces at varying temperatures and their impact on heat transfer characteristics. It was observed that the growth rate of ABS deposition decreased with the increase in wall temperature. At temperatures of 250°C, 300°C, and 350°C, the stable thickness of ABS deposition decreased to 4.09mm, 3.97mm, and 3.84mm, respectively. Moreover, the relative heat flux (RHF) also increases with the increase of wall temperature, from 0.68 at 250°C to 0.78 at 350°C. Consequently, an increase in the surface temperature of the heat transfer medium can effectively reduce the formation of ABS deposits and enhance the efficiency of heat transfer.

Floating offshore wind turbine is the key and fundamental equipment for obtaining wind energy on deep sea. Because of its large floating base motions, floating offshore wind turbine is loaded by aerodynamic, hydrodynamic and their coupled actions with the structures, and is very complex in dynamic characteristics. Therefore, study on the dynamics plays a very important role in the design, development, operation and maintenance of floating offshore wind turbine. This paper introduced briefly the recent research progress of the dynamic simulation analysis of floating offshore wind turbine from the aspects of aerodynamics of wind turbine body, hydrodynamics of floating base and wind-wave-current-structure coupled dynamics. The results and characteristics of the current research were summarized, and the research trend in the future was shown, providing a reference for further investigation in this field.

Waste tires can be used as a substitute for coal due to the high calorific value. In this study, the co-pyrolysis characteristics of the waste tires (truck tires, liners and nylon tires), pulverized coal and their blends are studied using thermogravimetric analyzer. The pyrolysis of truck tires, liners and coal is characterized by a three stages reaction while the pyrolysis of nylon tires and their blends are four stages. The pyrolysis characteristics of the blends can be expressed by the superposition of the pyrolysis characteristics of the one-component material, indicating the slight interaction of the co-pyrolysis between the waste tire and the coal. The co-pyrolysis kinetics of waste tires, coal and their blends are also investigated. For the blends of coal with truck tires and liners, the increased content of coal reduces the activation energy in the 2nd stage and leads to an increase and then a decrease in the 3rd stage. Different from the former, the activation energy increases with the increase of tire powder in both the 2nd and 3rd stages in the blends of coal and nylon tires. This is attributed to the fact that the nylon tires contain more synthetic rubber than truck tires and liners.

Diffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over 35%$35\%$ compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff. [ABSTRACT FROM AUTHOR]