Neural Network Modeling and Simulation of the Solid/Liquid Activated Carbon Adsorption Process

Abstract

A three-layer feed-forward neural network was constructed and tested to analyze the kinetic dye uptake of a batch activated carbon adsorption process. The operating variables studied are the contact time, initial dye concentration, agitation speed, temperature, initial solution pH, activated carbon mass, and volume of the dye solution treated. The studied operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output or the target. The constructed network was found to be precise in modeling the rate of dye uptake for the operating conditions studied. The constructed neural network was found to be highly precise in predicting the dye uptake rate for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the reaction rate for any operating conditions.