Direct Measurement of Floc Breakage in Flowing Suspensions

“…This technique is widely used in crystallization process (Barrett and Glennon, 1999), however the methodology for flocculation studies has to be developed. To acquire the best data it is necessary to optimize the following parameters: probe location, duration of the measure and data averaging (Peng and Williams, 1994;Dunham et al, 2000).…”

Section: Fundamentals Of Technique and Operationmentioning

confidence: 99%

Flocculation Monitoring: Focused Beam Reflectance Measurement as a Measurement Tool

et al. 2002

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1I n paper manufacturing flocculation can be defined as the formation of aggregates or flocs from the raw materials presented in a papermaking furnish. These interactions include fibres-fibres; fines-fibres; fillers-fibres; fillers-fines; and dissolved and colloidal fraction with all other furnish constituents. Fibre flocculation is mainly due to mechanical processes and it has an important influence on formation. However, fines and fillers interaction is dominated by the electrostatic interactions between particles and chemicals, and are the main influence on retention. Adhesion and occlusion of fines and fillers in the mechanical network mainly affect drainage. Dissolved and colloidal material flocculation is also due to electrostatic interactions with the added polyelectrolytes and it will mainly affect the efficiency of the chemicals and the runnability of the process and the quality of the final product when deposits are formed. Flocculation is extremely important in papermaking and it can be considered the intermediate state between the raw materials and the final product. For this reason, flocculation control has become an increasingly important issue in the paper industry over the years. The goal is to achieve the optimum flocculation that allows papermakers to obtain good retention and drainage at the same time as good formation. In the past, the optimum was considered the right equilibrium between these opposite effects; however, nowadays, the aim is to optimize these opposing effects in a sequential way. In other words, manipulating the chemicals and the turbulence in order to first flocculate the suspension, then break down the flocs, and then to re-flocculate the suspension in an optimum manner. This approach facilitates low mechanical flocculation and high chemical flocculation. Considering that the raw materials and the papermaking conditions are fixed for a given product and paper machine, there are only a few variables that can be controlled to improve flocculation, from the point of view of floc properties, size and strength to shear stress. Among these variables are consistency, type of chemical additive and addition point (Gess, 1998;Blanco, 1994).On the other hand, trends in papermaking such as a move towards closed-water systems, a higher use of recovered paper, faster machines, etc., also affect flocculation. For example, accumulation of anionic trash, fluctuating pH and temperature, increase of conductivity and turbulence, and so forth. These changes may affect pulp fractions, polymer characteristics, flocculation mechanism and floc properties. Therefore, the efficiency of the polymer can be dramatically affected. These effects most often result in a degradation of the additive efficiency and sometimes hinder the effectiveness of the various additives (Blanco et al., 1998;Vendries and Pfromm, 1998 One of the most important parameters that influences flocculation and, therefore, floc properties, is the flocculant characteristics. Flocculant additives operate via different flocculation mecha...

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“…Eq. (2) reflects the observation that larger aggregates are more likely to fragment than smaller ones because smaller aggregates tend to be more fragmentation resistant than larger ones (Harris & Maricq, 2002;Peng & Williams, 1994). The exponent 1=D f means that the fragmentation rate becomes proportional to the radius of gyration of the aggregate (Harris & Maricq, 2002;Tontrup et al, 2000).…”

Section: Oxidation-driven Soot Aggregate Fragmentation Modelsmentioning

confidence: 99%