Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

A multiple linear regression (MLR) model and least square support vector regression (LS-SVM) model with principal component analysis (PCA) was used for preprocessing to predict the efficiency of methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) based on experimental data set achieved in batch study. The PCA-LSSVM model indicated higher predictive capability than linear method with coefficient of determination (R2) of 0.97 and 0.92 for the training and testing data set, respectively. Firstly, the novel nanoparticles including copper oxide as low cost, non-toxic, safe and reusable adsorbent was synthesized in our laboratory with a simple and routine procedure. Subsequently, this new material properties such as surface functional group, homogeneity and pore size distribution was identified by FT-IR, SEM and BET analysis. The methylene blue (MB) removal and adsorption onto the CuO-NP-AC was investigated and the influence of variables such as initial pH and MB concentration, contact time, amount of adsorbent and pH, and temperature was investigated. The results of examination of the time on experimental adsorption data and fitting the data to conventional kinetic model show the suitability of pseudo-second order and intraparticle diffusion model. Evaluation of the experimental equilibrium data by Langmuir, Tempkin, Freundlich and Dubinin Radushkevich (D-R) isotherm explore that Langmuir is superior to other model for fitting the experimental data in term of higher correlation coefficient and lower error analysis.