Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-β

Abstract

Symbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the synthesis process of the oxides Bi2MoO6 and V2O5 in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them.