Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5-15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction

Modeling the adsorption of benzeneacetic acid on CaO2 nanoparticles using artificial neural network

The present work reported a method for removal of benzeneacetic acid from water solution using CaO2 nanoparticle as adsorbent and modeling the adsorption process using artificial neural network (ANN). CaO2 nanoparticles were synthesized by a chemical precipitation technique. The characterization and confirmation of nanoparticles have been done by using different techniques such as X-ray powder diffraction (XRD), high resolution field emission scanning electron microscope (HR-FESEM),transmittance electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis. ANN model was developed by using elite-ANN software. The network was trained using experimental data at optimum temperature and time with different CaO2 nanoparticle dosage (0.002-0.05 g) and initial benzeneacetic acid concentration (0.03-0.099 mol/L). Root mean square error (RMS) of 3.432, average percentage error (APE) of 5.813 and coefficient of determination (R2 ) of 0.989 were found for prediction and modeling of benzeneacetic acid removal. The trained artificial neural network is employed to predict the output of the given set of input parameters. The single-stage batch adsorber design of the adsorption of benzeneacetic acid onto CaO2 nanoparticles has been studied with well fitted Langmuir isotherm equation which is homogeneous and has monolayer sorption capacity

Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction