The development of empirical correlations to understand the frictional behavior of aqueous biomass slurry flows in vertical pipes

JOURNAL OF THE ASABE(2023)

引用 0|浏览5
暂无评分
摘要
Large-scale biofuel production at levels equivalent to conventional oil refineries using long-distance pipeline hydro-transport of biomass can be a cleaner alternative to fossil fuels when it comes to economics and traffic congestion associated with the overland transportation of biomass. The transport of aqueous slurries of several saturated mass concentrations (5%-40%) and four particle sizes (from <3.2-19.2 mm) of two types of agricultural residue biomass (ARB) feedstock (corn stover and wheat straw) was studied through a vertical test section of a 29 m long, 50 mm diameter closed circuit pipeline facility, and frictional pressure drops were recorded at different flow rates (0.5-4.3 m s(-1)). A framework was developed in RStudio (4.0.5) to analyze the experimentally obtained frictional pressure drops of biomass slurries through a multiple linear regression approach using a backward elimination method and Akaike information criterion. An empirical model was proposed to predict slurry frictional pressure drop in terms of slurry velocity, slurry solid mass concentration, particle aspect ratio, and feedstock type. The model satisfactorily predicted the frictional pressure drops of both feedstocks of biomass-water slurry flows through pipes within a 95% confidence interval. The correlations introduced for onset velocities of drag reduction in terms of slurry solid mass concentrations seemed helpful to interpret the transition points of the corresponding slurries in vertical upward flows through pipes. The empirical correlation developed in this research could help select biomass slurry pumps and pipe dimensions when designing a typical long distance pipeline network for biofuel production at the commercial level.
更多
查看译文
关键词
Agricultural biomass wastes,Frictional loss prediction,Numerical model,Onset velocity correlation,Regression coefficients,Upward pipe flow
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要