Analysis of gas-solid flow and shaft-injected gas distribution in an oxygen blast furnace using a discrete element method and computational fluid dynamics coupled model

lronmaking using an oxygen blast furnace is an attractive approach for reducing energy consumption in the iron and steel industry. This paper presents a numerical study of gas-solid flow in an oxygen blast fur- nace by coupling the discrete element method with computational fluid dynamics. The model reliability was verified by previous experimental results. The influences of particle diameter, shaft tuyere size, and specific ratio （X） of shaft-injected gas （51G） flowrate to total gas flowrate on the SIC penetration behavior and pressure field in the furnace were investigated. The results showed that gas penetration capacity in the furnace gradually decreased as the particle diameter decreased from 100 to 40 mm. Decreasing particle diameter and increasing shaft tuyere size both slightly increased the SIG concentration near the furnace wall but decreased it at the furnace center. The value of X has a significant impact on the SIG distribution. According to the pressure fields obtained under different conditions, the key factor affecting SIG penetration depth is the pressure difference between the upper and lower levels of the shaft tuyere. If the pressure difference is small, the SIG can easily penetrate to the furnace center.

Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle

Boron-dipyrromethene(BODIPY)is one promising class of sensitizers for dye-sensitized solar cells(DSSCs)due to unique merits of high absorption coefficient and versatile structural modification capability.However,such derivatives usually suffer from limited power conversion efficiencies(PCEs)because of narrow light absorption band and low electron injection.To aid the discovery of BODIPY sensitizers,we employ an inverse design method to design efficient sensitizers by integrating data mining and firstprinciple techniques.We establish robust data-mining models using genetic algorithm and multiple linear regression,where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse designs.Based on the features’understanding,we design candidates NH1-6 and predict their PCEs,demonstrating remarkable enhancements(58%maximum)compared to previous works.Furthermore,their optoelectronic properties including maximum absorption wavelengths,oscillator strengths,bandgaps,transferred charges,charge transferred distances,TiO_(2) conduction band shifts,short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements(93 nm,122.41%,23.70%,36.36%,471.17%,63.64%,28.55%,107.86%maximum),which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’shortcomings.The as-designed BODIPY sensitizers can be promising candidates for DSSCs,and such method could help accelerate the discovery of other energy materials.