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

Michael A.E. Hansen ; Ananda Subramani Kannan (Institutionen för tillämpad mekanik, Strömningslära) ; Jacob Lund ; Peter Thorn ; Srdjan Sasic (Institutionen för tillämpad mekanik, Strömningslära) ; Jens M. Carstensen
P. Sharma and F.M. Bianchi (Eds.): SCIA 2017, Part II, LNCS 10270 (2017)
[Konferensbidrag, refereegranskat]

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).

Nyckelord: CDA, Gravity tables, Multispectral imaging and state optimization

Denna post skapades 2017-06-21. Senast ändrad 2017-06-28.
CPL Pubid: 250025


Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Strömningslära (2005-2017)


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