Prediction of the Characteristics of a New Sonochemical Reactor Using an Expert Model

Abstract

Artificial neural network (ANN) models were developed to estimate the characteristics of a novel high-frequency sonoreactor. Mean residence time and ultrasound dissipated power were considered to evaluate the performance of this sonoreactor. These parameters were calculated from predicted tracer concentration and temperature variation by ANN models. The best network configurations were determined as two layers network including 10 and 12 neurons in hidden layer for the concentration and temperature prediction, respectively. For both networks Levenberg–Marquardt was the best training algorithm with average absolute relative error (AARE) below than 2%. The estimated characteristics were in good agreements with experimental data. Also, sensitivity analyses were done to find the relative importance of each input variable in networks training.