Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal – Chemometric and CFD approach

Abstract

In this study, sunflower meal is ground by a hammer mill after which air zigzag gravitational air classifier is used for separating sunflower hulls from the kernels in order to obtain protein rich fractions. Three hammer mill sieves with sieve openings diameter of 3, 2 and 1mm were used, while three air flows (5, 8.7 and 12.5m3/h) and three feed rates (30%, 60% an 90% of bowl feeder oscillation maximum rate) were varied during air classification process. For describing the effects of the test variables on the observed responses Principal Component Analysis, Standard Score analysis and Response Surface Methodology were used. Beside experimental investigations, CFD model was used for numerical optimization of sunflower meal air classification process.Air classification of hammer milled sunflower meal resulted in coarse fractions enriched in protein content. The decrease in sieve openings diameter of the hammer mill sieve increased protein content in coarse fractions of sunflower meal obtained at same air flow, and at the same time decreased matching fraction yield. Increase in air flow lead to the increase in protein content along the same hammer mill sieve. Standard score analysis showed that optimum values for protein content and ratio of coarse and fine fractions have been obtained by using a sieve with 1mm opening diameter, air flow of 12.5m3/h and 60% of the maximum feeder rate. Fraction ratio and protein content were mostly affected by the linear term of air flow and the sieve openings diameter of the hammer mill sieve in the Second Order Polynomial model. The main focus of CFD analysis was on the particle simulation and the evaluation of the separation efficiency of the zigzag classifier.