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

Abstract

AbstractFollowing 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 wt%), applied voltage (14, 18, and 22 kV), distance (10, 15, and 20 cm), and time (15, 30, and 45 min) is employed. Results showed that reduced the cost value from 0.1477 to 0.1183 incororating a polymer concentration of 10.08 wt%, an applied voltage of 16.09 kV, a distance of 20 cm, and a time of 27.17 min. The optimized filter possessed a filtration efficiency of 95% and a pressure drop of 169.2530 Pa for a particle size of 300 nm.Keywords: Electrospun nanofibrous filterfeed-forward back-propagationcentral composite design of experimentgenetic algorithmlinear programming Author contributionsNegar Hosseinzadeh Kouchehbaghi and Majid Sohrabi carried out all experimentation, Milad Razbin conceptualized, analyzed, modeled, and optimized data, Ahmad Ahmadi Daryakenari, Marjan Abbasi, and Seyed Hajir Bahrami supervised, funded, and administered the study. In the writing and reviewing step, all authors contributed.Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Data availability statementAll the data used to support the findings of this study are available from the corresponding author upon request.