The research on the product distribution of the co-pyrolysis of coal and biomass and the use of the Coats–Redfern integral method and the Achar differential method to study the kinetic parameters and mechanism of the pyrolysis process.
Metal matrix composites have become a research hotspot due to their unique properties. In this paper, Ni-W-Carbon fiber (CF) composite coating was prepared by electrodeposition method, and the effects of CF content on the microstructure, composition, and corrosion resistance of the coating were studied by means of Scanning Electron Microscope (SEM), Energy Dispersive X-ray Spectrometer (EDS) and electrochemical testing by changing the concentration of CF in the plating solution. The results show that when 0.4 g/L nano-CF is added, the CF content in the composite coating is the largest and the distribution is uniform; the surface roughness Ra of the composite coating reaches a minimum of 14 nm. The electrochemical results show that the composite coating electrodeposited in the electrolyte containing 0.4 g/L nano-CF has the highest corrosion resistance. The Guglielmi model can be used to describe the co-deposition behavior. This study provides useful enlightenment for the further application of Ni-W-CF coating in harsh corrosive environments.
Three-dimensional computational fluid dynamics according to the actual size of carbonization square furnace was performed, the standard k-ε model, the finite-rate/eddy-dissipation model and the P1 radiation model were adopted to simulate the flow of gases, the combustion of gas mixture and the heat transfer process, a mathematical model for square furnace was established when the porous medium model was selected for approximate replacement of coal seams in the furnace. The temperature and pressure fields with eight particle sizes of coal were also investigated and compared to five groups of conditions in the actual industrial production. The results showed that furnace witnessed dropped temperature, increased pressure, and gradually accelerated decline in the volume percentage of the carbonization zone with decreasing particle size of coal. However, the trends of flow field distribution were similar under different particle sizes, the maximum temperature and pressure presented an approximately linear decline and an approximately exponential growth, respectively. The temperature difference at the same height gradually narrowed with the gradual increase in the coal seam height under the same particle size. Moreover, the comparison between the numerical simulation results and industrial practice revealed a temperature and pressure error both lower than 6.25% and 12.8%, respectively.
In the low-temperature dry distillation of low-rank coal, the important liquid product of coal tar is produced, but its quality and utilization rate are degraded by entrained dust. e movement of coal tar and dust in the furnace is a key factor in causing particles such as dust to mix with coal tar. erefore, the Euler-Lagrangian method is used to simulate the two-phase motion process of gas, tar, and dust in a furnace. By considering the effects of tar particle size, dust particle size, gas velocity, tar density, and dust density, the motion process mechanism is revealed, enabling the dust content in coal tar to be reduced and the quality improved. e results indicate that tar particles with sizes less than 0.20 mm can be removed from the furnace by gas, and the smaller the particle size is, the shorter the time required for removal. Dust particles greater than 0.18 mm in size cannot be completely removed from the furnace. As the gas velocity increases, the time required for complete removal of the tar mist and dust gradually decreases. When the speed is 0.70 m/s, all tar mist is removed, although some particles remain. Tar mist with a density of more than 900 kg/m 3 can be extensively removed, but dust with a density of more than 1400 kg/m 3 is difficult to remove and remains in the furnace. Finally, particle size distribution experiments in the product were conducted to verify the accuracy of the numerical simulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.