ABSTRACT
Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic functional groups of the material were examined by the FTIR method. The BET surface area of the synthesized material, named HSAC, was 812 m2/g. 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 (R2 = 0.998). 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. The optimum adsorption parameters were determined to be pH 6, contact time of 60 min, 10 g/L of HSAC, and a concentration of 400 mg/L for CR. 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.
Subject(s)
Congo Red , Corylus , Neural Networks, Computer , Water Pollutants, Chemical , Adsorption , Corylus/chemistry , Water Pollutants, Chemical/chemistry , Congo Red/chemistry , Kinetics , Charcoal/chemistry , Models, Chemical , Waste Disposal, Fluid/methods , Hydrogen-Ion ConcentrationABSTRACT
In this investigation, the modeling of the Aksaray industrial wastewater treatment plant was performed using artificial neural networks with various architectures in the MATLAB software. The dataset utilized in this study was collected from the Aksaray wastewater treatment plant over a 9-month period through daily records. The treatment efficiency of the plants was assessed based on the output values of chemical oxygen demand (COD) output. Principal component analysis (PCA) was applied to furnish input for the Feedforward Backpropagation Artificial Neural Networks (FFBANN). The model's performance was evaluated using the Mean Squared Error (MSE), the Mean Absolute Error (MAE) and correlation coefficient (R2) parameters. The optimal architecture for the neural network model was determined through several trial and error iterations. According to the modeling results, the ANN exhibited a high predictive capability for plant performance, with an R2 reaching up to 0.9997 when comparing the observed and predicted output variables.
