Investigation on the synthesis conditions at the interpore distance of nanoporous anodic aluminum oxide: A comparison of experimental study, artificial neural network, and multiple linear regression

Abstract

Using nanoporous anodic aluminum oxide thin layer becomes more popular in recent years due to its capability to be a membrane in some engineering applications. The main purpose of this paper is to investigate the synthesis conditions at the interpore distance of nanoporous anodic aluminum oxide through an experimental study, an artificial neural network (ANN), and a multiple linear regression (MLR) model. A total of 33 experimental data used to establish both models. The models have three inputs including the concentration of electrolyte, temperature, and applied voltage. The interpore distance of nanoporous anodic aluminum oxide is considered as output in the models. The results of the models are compared with the results of experimental study and an empirical formula proposed by Nielsch. The results reveal that the proposed models have good prediction capability with acceptable errors. However, in this research, the proposed ANN model is accurate than the MLR analysis and both of them are better than empirical formula. The proposed models can also predict the results of experimental study successfully.