Simulation of the Ultrasonic Precipitation Process of Nano-hydroxyapatite by an Artificial Neural Network

The ultrasonic precipitation technique for preparing hydroxyapatite nanoparticles is a complex process that was strongly influenced by temperature, reaction time and ultrasonic power. The use of a modified artificial neural network （ANN） was proposed to model the non-linear relationship between ultrasonic precipitation parameters and the hydroxyapatite content. The improved model for processing dataset and selecting its topology was developed using the Levenberg-Marquardt training algorithm and was trained with comprehensive dataset of hydroxyapatite nanoparticles collected from experimental data. A basic repository on the domain knowledge of ultrasonic precipitation process for the preparation of hydroxyapatite is established via sufficient data mining by the network. With the help of the repository stored in the trained network, the influence of preparation temperature, preparation time and ultrasonic sonicating power on the hydroxyapatite content can be analyzed and predicted. The results show that the ANN system is effective and successful in analyzing the influence of ultrasonic precipitation parameters on the preparation of hydroxyapatite nanoparticles.