Ananda Subramani Kannan

Studies of grain segregation patterns on a Destoner using a CFD-DEM approach

Removal of contaminants from ‘food grade’ quality grains is of great importance in food and grain processing operations. A thorough understanding of the inherent granular segregation profiles on this processing equipment is a pivotal step in the design and development of more efficient processes. One such grain cleaning operation is the ‘density-based separation’ using a destoner. This process removes stones and other heavy material from lighter food grains using a vibrating deck and fluidizing air. In this paper we formulate a CFD-DEM framework (set up and implemented in the OpenFOAM® environment) to study granular segregation patterns on a destoner. The scheme is first validated by comparing simulations with experimental data using a gas-solid fluidized-bed test case. A good agreement between the experiments and the simulations is noted. This proposed framework is then used to characterize the combined effects of deck inclination and fluidization velocities on the separation profiles generated from a virtual destoner. We have found these profiles to be highly sensitive to changes in fluidization conditions, with the gradual development of segregation zones at velocities close to the minimum fluidization velocity of the heavier component. A deck inclination of 5 degrees and a fluidization velocity of 2.0 m/s is considered optimal while steeper slopes (inclinations of 15 degrees) and lower air velocities (0 and 1.5 m/s) are deemed unsuitable for segregation.

State Estimation of the Performance of Gravity Tables Using Multispectral Image Analysis

Gravity tables are important machinery that separate dense (healthy) grains from lighter (low yielding varieties) aiding in improving the overall quality of seed and grain processing. This paper aims at evaluating the operating states of such tables, which is a critical criterion required for the design and automation of the next generation of gravity separators. We present a method capable of detecting differences in grain densities, that as an elementary step forms the basis for a related optimization of gravity tables. The method is based on a multispectral imaging technology, capable of capturing differences in the surface chemistry of the kernels. The relevant micro-properties of the grains are estimated using a Canonical Discriminant Analysis (CDA) that segments the captured grains into individual kernels and we show that for wheat, our method correlates well with control measurements (\(R^2 = 0.93\)).