Membrane fractionation of milk: state of the art and challenges

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Abstract

Separation of milk into well-defined fractions will lead to a more optimal use of milk components (milk fat, casein, serum proteins) and their functional properties. In principle, membrane separation technology is well capable of large-scale fractionation of milk. Membrane processes for the isolation of serum proteins from whey, and the reduction of bacteria and spores in skimmed milk have already been adopted by the dairy industry. Other separation steps, such as the separation and/or fractionation of cream and the concentration of casein micelles, could become feasible in the future. In this paper, we give an overview of the current use of membranes in the fractionation of milk and discuss recent developments in membrane technology. Besides these different separation steps, we focus on the problem of fouling, which is considered the limiting factor in milk filtration. Different strategies to avoid and to decrease fouling are discussed, as well as their suitability in a fractionation process for milk. Further, new developments on computer modeling are discussed. Both analytical models and computational fluid dynamics (CFD) provide insight in the mechanisms of fouling and can be used to evaluate different methods to control fouling and to optimize process parameters. For the rational design of a milk fractionation process, it is essential that research in various fields, such as new membranes, module design, fouling control and modeling be carried out as part of an integrated approach toward a radical new process. (C) 2004 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)263-272
JournalJournal of Membrane Science
Volume243
Issue number1-2
DOIs
Publication statusPublished - 2004

Keywords

  • cross-flow microfiltration
  • discretized boltzmann-equation
  • hydrodynamic diffusion-model
  • spacer-filled channels
  • beta-lactoglobulin
  • skim milk
  • alpha-lactalbumin
  • dairy-industry
  • particulate suspensions
  • large-scale

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