In Process Measurement of Flocculation For Oil Sands Separations

Separation of water from fine tailings in the minerals or petroleum industries has been a long standing and well documented challenge.

Mnet 121911 Oilsands

Benjamin SmithSeparation of water from fine tailings in the minerals or petroleum industries has been a long standing and well documented challenge. To speed water recycle, the density of dispersed particles is increased to form sediments. Current treatments involve highly engineered types of polymers and chemicals to promote flocculation and modify rheology for the specific slurry being processed. By inducing flocculation, scientists and engineers adjust the particle size distribution to maximize the solid-liquid separation rate. In all cases, the dewatering efficiency is directly proportional to the incoming particle size distribution, particle population, shear, polymer type, polymer dosage, and the flocculated particle strength, porosity, and aggregate size. Measurement of flocculation kinetics as well as the size and strength of the flocs formed is critical to be able to understand, optimize and control complex dewatering processes.

Since flocculation is a dynamic process with changing particle size and count, representative sampling and offline analysis are difficult. Offline samples are typically manipulated through dilution or dispersion preparing them for measurement. This can alter or destroy fragile particle structures and offline measurements often cannot be applied to make real-time process optimization and control decisions. With established in situ particle characterization technology, scientists and engineers can quickly measure the particle and floc behavior in process without taking samples1, 2.

Des O'GradyIn process particle size characterization enables the lab researcher and field operator to track the entire dynamic particle system in oil sands mature fine tailings (MFT) streams at standard operating concentrations, temperatures and pressures – without sampling or sample dilution. As the source of the incoming stream varies in concentration and mineral components, real time particle size, shape and count measurements provide predictive measurements to enable a controlled response. Scientists and engineers can understand how the particle system responds to changing process parameters and optimize the type of polymer additive, the dosage, and the shear to control solid-liquid separation and downstream throughput.

In-Process Particle Size and Count Measurement to Optimize the Separation Process
By implementing in-process particle measurement technology, real time flocculation performance is measured at full process concentrations allowing optimization of MFT dewatering at the bench or in the field. Figure 1 depicts how in-process measurements allow users to measure changes to the particle size and count with increasing polymer dosage. The distribution progressively shifts to lower counts and larger size as the dosage increases3.

When shear is introduced to improve the polymer dispersion the initial aggregate size distribution often increases. However, depending on the strength of the flocs and intensity of shear, it may result in dispersion and solids breaking up over time, resulting in a distribution that shifts to smaller particle counts (Figure 2). These fine dispersed particles have a negative impact on dewatering, and optimizing the changes in the process and maximizing the degree of flocculation improves downstream dewatering. By monitoring flocculation performance in real time without having to take samples, it is possible to adjust process parameters and dosage protocols in order to optimize dewatering.

Figure 2. In process particle size and count statistics trended over time combined with real time microscopy images for process understanding. A Incoming steady state MFT dispersion and poor water release; B Polymer dosage followed by flocculation; C Floc breakdown and maximum dewatering D. Floc shear and dispersion reverting to the MFT and less water release

Figure 1: Chord length distributions tracking changes in particle count and size as flocculent dosage increases3

In process particle size and count measurements can also be used to optimize and even predict flocculation efficiency and breakage under varying shear conditions. Researchers have used in process particle size and count measurements to follow flocculation in turbulent pipe flow to quantify the kinetics of aggregate growth and breakage. They have shown that varying the slurry flow rate for a fixed polymer dosage (Figure 4) greatly affects the extent of aggregation and the degree of breakage at longer times. By measuring the real time particle dimension over a range of conditions, a population balance model for flocculation can be applied using the outputs in hydrodynamic modeling to optimize operating conditions in pipes, channels or thickener feed wells4.

Figure 4. The effect of slurry flow rate on the flocculation mean size (chord length, μm) over time at different shear rate4

While aggregate dimensions are taken as indicative of likely settling properties, the corresponding settling rates are actually found to diminish at longer reaction times (Figure 5). This is attributed to flocculent deactivation which prevents additional growth at longer times, reducing dewatering performance4.

Figure 5. In process particle size and count measurements applied to predict conditions for the optimum settling rates and reaction time after flocculent addition4

Conclusion
By using in-process particle characterization tools, flocculation kinetics are measured in real time. This provides scientists and engineers with information to optimize the polymer type, dosage, and shear based on the variation in the incoming particle size, concentration and flow rates. This enables scientists to:

  • Understand how the particle system responds to changing process parameters
  • Optimize particle flocculation to improve downstream separations performance
  • Control particle distribution to achieve consistency, process throughput, and process stability

 

1. Owen, A.T. et al (2008) International Journal of Mineral Processing 87, pp. 90–99.
2. Blanco, A. et al. (2002). The Canadian Journal of Chemical Engineering, 80, 1-7.
3. Fawell, P. D. et al. Parker Center (CSIRO) Australia, (2002) International Process                                 
4. Fawell, P. D. et al Parker Centre (CSIRO) Australia, (2009) Paste.

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