The Use of Artificial Neural Network for Modeling Adsorption of Congo Red Onto Activated Hazelnut Shell

Abstract

Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo Red (CR) dye from aqueous solutions, and a modeling study was conducted using Artificial Neural Networks (ANNs). For the adsorption study, pH (3-9), initial CR concentration (5-400 mg/L), contact time (2-240 min.), and adsorbent quantity (0.5-10 g/L) parameters were investigated. Conducted in a batch system, the adsorption experiments resulted in a notable removal efficiency of 87% under optimal conditions. The kinetic data for hazelnut shell activated carbon (HSAC) removal of CR were most accurately represented by the pseudo-second-order kinetic model. Furthermore, the equilibrium data demonstrated a strong agreement with the Freundlich model. The maximum adsorption capacity of HSAC for CR was determined to be 34.8 mg/g. Considering the various experimental parameters influencing CR adsorption, an Artificial Neural Network (ANN) model was constructed. The analysis of the ANN model revealed a correlation of 98%, indicating that the output parameter could be reliably predicted. Thus, it was concluded that ANN could be employed for the removal of CR from water using HSAC.