Using an artificial intelligence technique to optimize calcium phosphates synthesis conditions

The most widely used calcium phosphate-based bioceramic is biphasic calcium phosphate (BCP) consist of hydroxyapatite and tricalciumphosophate. Bioactivity and biodegradation of BCP is controlled by varying HA/TCP ratio. In this study, a biphasic calcium phosphate composite (HA/b-TCP) was synthesized through a wet chemical method. The structure and the composition of the resulting powders were characterized by different analytical techniques. To estimate and predict the synthesis conditions, a back-propagation neural network (BPNN) which has 2 inputs and 1 output was designed. Some experimental samples have been prepared to train the BPNN to get it to estimate the output parameters. Then BPNN is tested using some samples that have not been used in the training stage. To prepare the training and testing data sets, some experiments were performed. The effects of the pH of reactants and the Ca:P ratios of reactants, as input parameters, have been investigated on the Ca:P ratio of products, as output parameters. The comparison of the predicted values and the experimental data indicates that the developed model has an acceptable performance to estimate the Ca:P ratio of products in HA/beta-TCP composite powder.