An integrated model for predicting the size of silver nanoparticles in montmorillonite/chitosan bionanocomposites: A hybrid of data envelopment analysis and genetic programming approach

Unique chemical and physical properties of silver nanoparticles (AgNPs) contribute to the broader scope of their applications in different fields including medical utilities. Considering the high dependency of AgNPs properties on their size, this study employed Gene Expression Programming (GEP) to develop a quantitative model for estimating the size of AgNPs in montmorillonite/chitosan bionanocomposites prepared by the chemical approach. Generalization capabilities, fault tolerance, noise tolerance, high parallelism, nonlinearity, and significant information processing characteristics are the main advantages of GEP. Accordingly, the practical parameters including reaction temperature, AgNO3 concentration, weight of montmorillonite in aqueous AgNO3/chitosan solution (WMMT), and percentage of chitosan were the input parameters selected through GEP modeling. The accuracy of the proposed models was investigated based on statistical indicators including Mean Absolute Percentage Error (MAPE), Root Relative Squared Error (RRSE), Root Mean Square Error (RMSE), and correlation coefficient (R2). Finally, the best model was selected by R2 = 0:987, RMSE = 0:100, RRSE = 0:146, and MAPE = 0:221. The sensitivity analysis confirmed that the percentage of chitosan, concentration of AgNO3, WMMT, and reaction temperature were the most effective parameters for the size of AgNPs. © 2021 Sharif University of Technology. All rights reserved.