ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY APPROACHES FOR OPTIMIZATION OF PARAMETERS FOR AN AIR-JET SUNFLOWER SEEDS REMOVER MACHINE

Abstract

An impingement jet method was employed for extracting of sunflower seeds from sunflower heads (SHs). The method was based on holding SHs with a rotating plate and extracting the sunflower seeds with the help of pressurized air-jets. Artificial neural networks (ANNs) and response surface methodology (RSM) were used to model the effects of operational parameters of impingement air-jet on performance of preliminary model of the remover machine. The operational parameters were diameter of nozzle (ND), angle of impingement (AI), distance between nozzle outlet and sunflower head (DBNS), air pressure (AP) and rotational velocity of sunflower head (RV). The final ANN model, 3-5-1, successfully modeled the relationship between three operational parameters, ND, AI and RV with removing performance of machine (RPAJSSR) with R2of 0.98 and T value of 0.96. The RSM method was applied for three different locations of SHs at the optimum AP of 7 bar. The maximum value of RPAJSSR, (57%) was obtained for ND of 8 mm, AI of 30°, DBNS of 20 mm and RV of 10 rpm at side region of SH (SRSH). Also, the minimum value (4.49%) belonged to ND of 4 mm, AI of 30°, DBNS of 20 mm and RV of 15 rpm for central region of SH (CRSH).