Computational modelling of industrial pulp stock chests

Abstract

AbstractAgitated pulp stock chests are the most widely used mixers in pulp and paper manufacture. Stock chests are used for a number of purposes, including attenuation of high‐frequency disturbances in pulp properties (such as mixture composition, fibre mass concentration, and suspension freeness) and are designed using semi‐empirical rules based largely on previous experience. Tests made on both laboratory and industrial‐scale pulp chests indicate that they are subject to non‐ideal flows, including channelling and creation of dead zones.In the present work, a commercial computational fluid dynamic (CFD) software (Fluent) is used to model two industrial pulp stock chests. The first chest is rectangular, agitated using a single side‐entering impeller, and feeds a mixture of chemical pulps at 3.5% mass concentration (Cm) to a papermachine. The second chest has rectangular geometry, with a mid‐feather wall used to direct suspension flow through a U‐shaped trajectory past four side‐entering impellers. This chest is used to remove latency from a Cm = 3.5% thermomechanical pulp suspension ahead of stock screening.For CFD computations, pulp rheology was described using a modified Hershel–Buckley model. Steady‐state simulations were made corresponding to process conditions during mill tests. The calculated steady‐state flows were then used to determine the dynamic response of the virtual chests and then compared with experimental measurements and found to agree reasonably well. The computed flow fields provided insight into mixing processes occurring within the chests, showing cavern formation around the impellers (which reduced the agitated volume available for mixing). Mass‐less particle tracking, using the steady‐state flow field, gave insight into the stagnant regions and bypassing zones created in the vessels. This paper discusses difficulties encountered in characterising the mixing (both experimentally and computationally) and the limitations of the industrial data.