Predictive model based on artificial neural net for purity of the artificial synthetic hydrotalcite

摘要

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on artificial neural net was developed. And the non-linear relationship between the hydrotalcite purity and the raw material amount of NaOH, Mg~(2+), Al~(3+) was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net from the testing data. The learning algorithm for neural net is BP (back-propagation) algorithm with 3-2-1 structure. The results show that, for multi-factor synthesis prediction, the prediction model based on BP learning algorithm for hydrotalcite purity of the prio-synthesis hydrotalcite is feasible and effective. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts.