Comparison of modeling, optimization, and prediction of important parameters in the adsorption of cefixime onto sol-gel derived carbon aerogel and modified with nickel using ANN, RSM, GA, and SOLVER methods

Abstract

Today, the main goal of many researchers is the use of high-performance, economically and industrially justified materials, as well as recyclable materials in removing organic and dangerous pollutants. For this purpose, sol-gel derived carbon aerogel modified with nickel (SGCAN) was used to remove Cefixime from aqueous solutions. The influence of important parameters in the cefixime adsorption onto SGCAN was modeled and optimized using artificial neural network (ANN), response surface methodology (RSM), genetic algorithm (GA), and SOLVER methods. R software was applied for this purpose. The design range of the runs for a time was in the range of 5 min-70 min, concentration in the range of 5 mg L^-1 to 40 mg L^-1, amount of adsorbent in the range of 0.05 g L^-1 to 0.15 g L^-1, and pH in the range of 2.0-11. The results showed that the ANN model due to lower Mean Squared Error (MSE), Sum of Squared Errors (SSE), and Root Mean Squared Error (RMSE) values and also higher R² is a superior model than RSM. Also, due to the superiority of ANN over the RSM model, the optimum results were calculated based on GA. Based on GA, the highest Cefixime adsorption onto SGCAN was obtained in pH, 5.98; reaction time, 58.15 min; initial Cefixime concentration, 15.26 mg L^-1; and adsorbent dosage, 0.11 g L^-1. The maximum adsorption capacity of Cefixime onto SGCAN was determined to be 52 mg g^-1. It was found the pseudo-second-order model has a better fit with the presented data.

Keywords: Adsorption; Cefixime; Neural network; Response surface methodology; Sol-gel derived carbon aerogel.