Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach

Abstract

Copper sulfide nanoparticle-loaded activated carbon (CuS-NP-AC) was prepared and used as an adsorbent for the accelerated removal of chrysoidine G (CG) assisted by ultrasound. This nanomaterial was characterized by FE-SEM, BET and XRD. The effects of variables such as initial CG concentration (mg L⁻¹), adsorbent amount (g) and sonication time (s) on CG removal were investigated and optimized by using central composite design (CCD) under response surface methodology (RSM). The Langmuir isotherm was applied to describe well the experimental equilibrium data with high figures of merit. The mass transfer mechanism of time varying adsorption was shown to be described by the second-order equation model. The random forest (RF) model applied to the experimental data was shown to be highly applicable to predict CG adsorption onto CuS-NP-AC. The optimal tuning parameters for the RF model were obtained based on 100 and 2 for n_tree and m_try, respectively. For the training dataset, the values of MSE and the coefficient of determination (R²) were found to be 0.0021 and 0.9657, respectively, while they were determined as 0.0069 and 0.8976 for the testing dataset. It was found that a small adsorbent amount (0.03 g) is applicable for efficient removal of CG (RE > 94%) in a short time (360 s) with reasonably high adsorption capacity (89.3 mg g⁻¹).