Universal counter-current chromatography modelling based on counter-current distribution

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Abstract

There is clearly a need for a model which is versatile enough to take into account the numerous operating modes and pump out procedures that can be used with counter-current chromatography (CCC). This paper will describe a universal model for counter-current chromatography based on counter-current distribution. The model is validated with real separations from the literature and against established CCC partition theory. This universal model is proven to give good results for isocratic flow modes, as well as for co-current CCC and dual flow CCC, and will likely also give good results for other modes such as intermittent CCC.

Introduction

Counter-current chromatography (CCC), based on counter-current distribution (CCD) proposed in 1944 [1], is a discrete-step liquid–liquid chromatography process. CCD is usually performed using a large number of test tubes, each partly filled with a lower (stationary) phase and an upper (mobile) phase. The sample mixture is introduced in the first test tube. After mixing phases by shaking up the test tubes and leaving the phases to settle out, the components in the sample mixture will be distributed over the phases according to their partition coefficient. After each distribution step, the upper (mobile) phase of every test tube is transferred to the next test tube. The process of redistribution and phase movement continues until the desired component(s) are eluted from the test tubes [2], [3].

The simple step nature of the CCD process makes it an ideal basis for modelling CCC [4]. CCC is a liquid–liquid chromatography technique first introduced by Ito et al. [5]. Like CCD, the process is based on two immiscible phases; traditionally a stationary phase and a mobile phase. The phases flow freely in continuous tubing. One of the phases is maintained stationary in the tubing due to a combination of hydrodynamic and hydrostatic forces. Mixing and settling to promote distribution of sample components between the phases, is stimulated by the varying g-force. The tubing is wound on a bobbin, which is spun in planetary motion.

Mathematical modelling for an entire chromatography process dates back to 1941 [6]. Though purely mathematical, their solution still includes an iterative computation and is limited to the conventional CCC (or CCD) operation mode. More recent work has been done on the same subject, trying to simplify the mathematical solution and taking the shape of chromatography peaks into account [7].

The initial mathematical solution proposed in 1941 [6] was based on binomial expansion. Due to its nature however, it only applies to isocratic flow modes. To improve the run time and separation efficiency, different operation modes have been introduced for CCC. Dual flow CCC, where both phases are pumped simultaneously in opposite directions was first suggested for CCD in 1963 [8] and first applied to CCC by Ito [9]. Different models on dual flow have been developed recently [10], [11], [12]. Another mode where the phases both move in the same direction, normally at different speeds, is referred to as co-current CCC [13]. Recently, modelling on this has been proposed by Berthod and Hassoun [14].

There is clearly a need for a model which is versatile enough to take into account the numerous operating modes and pump out procedures that can be used with CCC. This paper will describe a universal model for counter-current chromatography. It has a number of operational input parameters, such as the coil volume, the retention values and flow rates of the phases and the operational mode with the output being a traditional chromatogram. The model is validated with real separations from the literature and against established CCC partition theory.

Section snippets

Conventional mode

The distribution coefficient [15] defines the affinity of a component to the phase system:D=CSCMorD=CUCLwhere CS, CM, CU and CL are the concentrations in the stationary, mobile, upper and lower phase, respectively. In CCC traditionally there is a stationary phase and mobile phase. In CCD this means that the mobile phase of each tube is transferred to the next tube. This is followed by mixing of the entire content of each tube, and a settling into upper and lower phase again, in order to promote

Cell movement

The way the model works is very similar to the CCD process. The model simulates a number of imaginary test tubes, also called cells. The cells in the model consist of arrays of numbers representing the mass concentrations of sample components. The programming language used for the model is C++, and for the arrays a dynamic storage type is used called a vector. This way the arrays can be made any length dynamically, allowing for a greater flexibility and optimisation of available computer

Conventional flow mode

The model was compared with experimental values from actual CCC experiments [18] (Table 1).

The CCC experiments are from a SFCC 2000 (Societe Francaise Chromato Colonnes, Paris, France) hydrodynamic type J coil planet centrifuge CCC machine, with a spiral coil using 15 m of 2.7 mm I.D. PTFE tubing with a total volume of 156 ml.

The machine was running at a rotational speed of 600 rpm with mobile phase flow rates of 1, 2, 3 and 4 ml/min. A heptane–methanol–water system with sample components toluene (D =

Conclusion

The model is very flexible in the way that it can simulate many different CCC scenarios. For conventional flow mode the model appears to give the same results as actual experiments. For co-current flow the model gives very similar results as actual experiments as well. For dual flow, there seems to be only a small error in the model values. One of the model values shows a greater deviation from experimental data, but this may be due to the sensitivity of the flow balance. Each of the peaks

References (19)

  • L.C. Craig

    J. Biol. Chem.

    (1944)
  • A.E. Kostanian

    J. Chromatogr. A

    (2002)
  • A.E. Kostanian et al.

    J. Chromatogr. A

    (2007)
  • A. Berthod et al.

    J. Chromatogr. A

    (2006)
  • B. Williamson et al.

    J. Biol. Chem.

    (1947)
  • A. Berthod et al.

    J. Chromatogr. A

    (2000)
  • L.C. Craig et al.

    Anal. Chem.

    (1949)
  • I.A. Sutherland et al.

    J. Liq. Chromatogr. Relat. Technol.

    (2003)
There are more references available in the full text version of this article.

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