Artificial Neural Network Approach for Modeling of Effect of Ultrasound on the Dissolution of Magnesia in Aqueous Carbon Dioxide

This article is about dissolving magnesia in aqueous carbon dioxide by applying ultra sound. Particle size, reaction temperature, and solid/liquid ratio were chosen as the experimental parameters. As a result of the experimental study, the ultrasound energy conversion fraction (USECF) was obtained. Using experimental data, a model has been created for artificial neural networks and USECF. Created and modeled, the particle size, time, reaction temperature, solid/liquid ratio, and amplitude rate were determined as input variables. USECF was determined as the output variable of the model. In this study, six different ANN models were created by using two different learning algorithms and three different transfer functions. The results of these models were compared with the experimental results. It has been determined that the model established with the Levenberg Marquart learning algorithm and the TANSIG transfer function gives the best result of the ANN model compared to the other models. The ANN model established with the Gradient Descent learning algorithm and the LOGSIG transfer function were determined to be the second model that gave the best results. The regression R value for the model performance indicator training data was determined as 0.99 after validation, and the regression R value for the test data was determined as 0.99.