Operating parameter optimization of centrifuge based on APSO-RBF

To achieve centrifugal liquid COD concentration of oily sludge is less than 2000mg/L, optimizer mathematical model was proposed based on adaptive particle swarm-radial basis function neural network algorithms (APSO-RBF). Implementation on the APSO-RBF optimizer was described. The centrifuge parameters optimize controller was created based on APSO-RBF neural network algorithms. The intelligent centrifuge was established based on the APSO-RBF optimization processor and the traditional centrifuge. So centrifuge parameters achieve optimization. By the comparison test of three kinds of oily sludge samples, the results indicate that the mathematical model of optimization controller based on APSO-RBF neural network algorithms is correct and reasonable. The embedded smart centrifuge with APSO-RBF neural network optimization controller is superior to the additional centrifuge without the intelligent controller. It can improve the efficiency of centrifuge, and achieve target that centrifugal liquid COD concentration of oily sludge is less than 2000mg/L. So it is useful.