Axial dispersion in liquid flow through packed beds

Miller, Steven Frank; King, Charly

doi:10.1002/aic.690120425

Cited by 72 publications

(10 citation statements)

References 14 publications

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“…Schmidt numbers were estimated from the molecular diffusion coefficient of the dye in water (Ferrel et al, 1955) by assuming the diffusion coefficient in solution is inversely proportional to the solution viscosity (Bird et al, 1960). Miller and King (1966) and Gunn (1969Gunn ( , 1971) have discussed liquid dispersion in the absence of molecular diffusion, infinite Schmidt number, and both have shown that Peclet num- bers should be at a constant, minimum value for this situation in laminar flow. The magnitude of the glycerol Peclet numbers found here is consistent with their discussions, and the relative insensitivity of the Peclet numbers to changing velocities suggests that it is safe to assume the Schmidt number for the glycerol solution is effectively infinite over the range of flow rates studied.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Axial dispersion of non‐Newtonian fluids in porous media

Payne

Parker

1973

AIChE Journal

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Section: Discussion Of Resultsmentioning

confidence: 99%

Axial dispersion of non‐Newtonian fluids in porous media

Payne

Parker

1973

AIChE Journal

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“…Examples are Carberry and Bretton (1958), Ebach and White (1958), Liles and Geankoplis (1960), Saffman (1960), Perkins and Johnston (1963), Pfannkuch (1963), Poreh (1965), Miller and King (1966), Chung and Wen (1968), Fried and Combarnous (1971), Klotz (1973), Miyauchi and Kikuchi (1975), Sahimi et al (1986), Hu and Brusseau (1994), Matsubayashi et al (1997), and Delgado (2006). Examples are Carberry and Bretton (1958), Ebach and White (1958), Liles and Geankoplis (1960), Saffman (1960), Perkins and Johnston (1963), Pfannkuch (1963), Poreh (1965), Miller and King (1966), Chung and Wen (1968), Fried and Combarnous (1971), Klotz (1973), Miyauchi and Kikuchi (1975), Sahimi et al (1986), Hu and Brusseau (1994), Matsubayashi et al (1997), and Delgado (2006).…”

mentioning

confidence: 99%

Estimating Solute Dispersion Coefficients in Porous Media at Low Pore Water Velocities

Pugliese

Poulsen

2014

Soil Science

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The relationship between solute dispersion coefficient (D mech ) and pore water velocity during chloride transport in porous media with different particle characteristics at low pore water velocities (u < 1 cm min −1 ), relevant for ground water flow conditions, was investigated. Data for 29 porous media with very different particle shapes (ranging from almost spherical to very angular) and particle sizes (0.088-12 mm) at different velocities (340 breakthrough curves) were used in the analyses. The analysis showed that D mech at low u is significantly different from that at higher u. Whereas the u -D mech relationship generally can be assumed linear at higher u (based on observations in earlier studies), the data presented in this study clearly show that this is not the case at low u. The results further indicated that D mech at low u is strongly related to porous medium particle shape and to some degree also to medium particle size range and mean particle diameter. As the current knowledge about dispersion at low u and its dependency on porous medium properties is very limited, the results obtained in this study represent a significant addition to this understanding. A set of expressions for predicting D mech at low u from medium properties was developed. These expressions yield good accuracy across all 29 media, thus providing a means for predicting dispersion at low u across a very wide range of media.

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“…The only change was the correlation used to calculate the axial dispersion coefficient

D_{L, i}

. In this article, an empirical correlation found by Athalye based on data of Miller and King was used as done in Ref. .…”

Section: Methodsmentioning

confidence: 99%

Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

Pirrung

Wielen

Beckhoven

et al. 2017

Biotechnology Progress

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Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:696-707, 2017.

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Axial dispersion in liquid flow through packed beds

Cited by 72 publications

References 14 publications

Axial dispersion of non‐Newtonian fluids in porous media

Axial dispersion of non‐Newtonian fluids in porous media

Estimating Solute Dispersion Coefficients in Porous Media at Low Pore Water Velocities

Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

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