Artificial Neural Network [ANN] modeling for tetracycline adsorption on rice husk using continuous system
Husham AbdMunaf Atta
Abstract
In this work, Tetracycline (TC) was dynamically adsorbed from aqueous solution using Raw Rice Husk (RRH). The removal of TC from various aqueous solutions was done continuously using a fixed bed column that was filled with RRH. It was investigated how various flowrates, bed heights, and TC concentrations impact the performance of the adsorption breakthrough. The dynamic TC adsorption process was modeled using an artificial neural network (ANN). Various error analysis techniques, such as the correlation coefficient, mean square error (MSE), and error histogram (R2) for the training, testing, and validation data, were employed to compare the model's predicted data with experimental data. The Artificial Neural Network (ANN) model that was utilized to predict TC adsorption using raw rice husk demonstrated a robust correlation coefficient of 0.999, This shows how well the trained model performed in predicting the TC adsorption process.