Soft computing procedure to optimize the electrospinning parameters of polyacrylonitrile nanofibrous air filter

Abstract

Following the previous work, this research aims to highlight the applicability of a new strategy for engineering morphological properties of nanofibrous filters using a model consisting of artificial neural analytical model, and genetic algorithm. The main idea is to engineer the morphological properties of nanofibrous filters before mass production. To collect data, a central composite design of experiment considering parametersch as polymer concentration (10, 12, and 14 ), applied voltage (14, 18, and 22 ), distance (10, 15, and 20 ), and time (15, 30, and 45 ) is employed. Results showed that reduced the cost value from 0.1477 to 0.1183 incororating a polymer concentration of 10.08  an applied voltage of 16.09  a distance of 20  and a time of 27.17  The optimized filter possessed a filtration efficiency of 95% and a pressure drop of 169.2530  for a particle size of 300 nm.