Cr(III) removal from simulated solution using hydrous magnesium oxide coated fly ash: Optimization by response surface methodology (RSM)

Hydrous magnesium oxide coated fly ash (MFA) has environmental remediation potential by providing a substrate for the adsorption of aqueous Cr(Ⅲ). Aqueous Cr(Ⅲ) adsorption onto MFA was examined as a function of MFA dosage, pH and initial Cr(Ⅲ) concentration with the Box-Behnken approach used for experimental design and optimization using response surface methodology (RSM). pH and dosage (dosage and concentration) have significant interactive effects on Cr(Ⅲ) adsorption efficiency. Analysis of variance shows that the response surface quadratic model is highly significant and can effectively predict the experimental outcomes. Cr(Ⅲ) removal efficiency of 98% was obtained using optimized conditions of MFA dosage, pH and initial Cr(Ⅲ) concentration of 1.57 g·L^-1, 4.11 and 126 mg·L^-1, respectively. Cr(Ⅲ) adsorption onto MFA is mainly attributed to the interaction between Cr(Ⅲ) and the functional group -OH of the hydrous magnesium oxide, in all probability caused by chemisorptions. The results of this study can conduce to reveal the interactions between Cr(Ⅲ) pollutant and MFA characteristics, posing important implications for the cost-effective alternative adsorption technology in the treatment of heavy metal containing wastewater.     

Photocatalytic oxidation of treated municipal wastewaters for the removal of phenolic compounds: optimization and modeling using response surface methodology (RSM) and artificial neural networks (ANNs)

BACKGROUND: TiO₂ heterogeneous photocatalysis should be optimized before application for the removal of pollutants in treated wastewaters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to model and optimize the photocatalytic degradation of total phenolic (TPh) compounds in real secondary and tertiary treated municipal wastewaters. RESULTS: RSM was developed by considering a central composite design (CCD) with three input variables, i.e. TiO₂ mass, initial concentration of TPh and irradiation intensity. At the same time a feed‐forward multilayered perceptron ANN trained using back propagation algorithms was used and compared with RSM. Under the optimum conditions established in experiments ([TPh]₀ = 3 mg L^?1; [TiO₂] = 300 mg L^?1; I = 600 W m^?2) the degradation for both TPh and total organic carbon (TOC) followed pseudo‐first‐order kinetic model. Complete degradation of TPh took place in 180 min and reduction of TOC reached 80%. A significant abatement of the overall toxicity was accomplished as revealed by Microtox bioassay. CONCLUSIONS: It was found that the variables considered have important effects on TPh removal efficiency. The results demonstrated that the use of experimental design strategy is indispensable for successful investigation and adequate modeling of the process and that ANNs gave better modelling capability than RSM. Copyright © 2012 Society of Chemical Industry     

Experiment research on mix design and early mechanical performance of alkali-activated slag using response surface methodology (RSM)

To enhance the quality of alkali-activated slag (AAS) materials, scientific and efficient mix design method is preferred. This paper presents an optimization of AAS materials using Response Surface Methodology (RSM). Three factors related to early strength such as modulus (n), concentration of alkali activator (CAA) and liquid–solid ratio (LSR) were investigated. Specimens with different mix ratios were prepared based on RSM design. The early mechanical performance was assessed, after 2 or 3 h of curing. Then response surface models were established and the effect law of each factor was systemically analyzed. The result shows that both n and CAA have a significant effect on the early strength, while LSR affects slightly. By adjusting the mix design parameters, the early performance of AAS can be effectively improved. This study verifies that RSM is efficient in the preparation of AAS and it can control the early strength of AAS accurately.