SVR-Based Predictive Model for Purity of the Mg-Al-Hydrotalcite

Abstract

In this study, a prediction model based on support vector regression (SVR) to describing the complex nonlinear relationship between the purity of the resultant (Mg-Al-hydrotalcite) and the raw material amount of reactant (NaOH, Mg2+ and Al3+) was developed. And the partarticle swarm optimization (PSO) algorithm was employ for optimizing the parameters of SVR model.The predicton results from SVR compared with that of BP (back- propagation) neuarl network prediction model by applying identical training and test samples stongly support that, SVR-based predictive model for Mg-Al-hydrotalcite purity is feasible and effective.This study suggest that SVR may be an efficient and novel methodology for conduct the productive process to produce synthetic hydrotacite in high purity and in high yield.