Predicting the capability of carboxymethyl cellulose-stabilized iron nanoparticles for the remediation of arsenite from water using the response surface methodology (RSM) model: Modeling and optimization

2017 ◽  
Vol 203 ◽  
pp. 85-92 ◽  
Author(s):  
Amir Mohammadi ◽  
Sepideh Nemati ◽  
Mohammad Mosaferi ◽  
Ali Abdollahnejhad ◽  
Mohammad Almasian ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Carboxymethyl Cellulose ◽  
Iron Nanoparticles ◽  
Modeling And Optimization ◽  
Rsm Model

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Efficient Removal of Levofloxacin by Activated Persulfate with Magnetic CuFe2O4/MMT-k10 Nanocomposite: Characterization, Response Surface Methodology, and Degradation Mechanism

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3583
Author(s):  
Junying Yang ◽  
Minye Huang ◽  
Shengsen Wang ◽  
Xiaoyun Mao ◽  
Yueming Hu ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Photoelectron Spectroscopy ◽  
Degradation Mechanism ◽  
Sol Gel ◽  
Combustion Method ◽  
Copper Ferrite ◽  
X Ray ◽  
Rsm Model

In this study, a magnetic copper ferrite/montmorillonite-k10 nanocomposite (CuFe2O4/MMT-k10) was successfully fabricated by a simple sol-gel combustion method and was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), the Brunner–Emmett–Teller (BET) method, vibrating sample magnetometer (VSM), and X-ray photoelectron spectroscopy (XPS). For levofloxacin (LVF) degradation, CuFe2O4/MMT-k10 was utilized to activate persulfate (PS). Due to the relative high adsorption capacity of CuFe2O4/MMT-k10, the adsorption feature was considered an enhancement of LVF degradation. In addition, the response surface methodology (RSM) model was established with the parameters of pH, temperature, PS dosage, and CuFe2O4/MMT-k10 dosage as the independent variables to obtain the optimal response for LVF degradation. In cycle experiments, we identified the good stability and reusability of CuFe2O4/MMT-k10. We proposed a potential mechanism of CuFe2O4/MMT-k10 activating PS through free radical quenching tests and XPS analysis. These results reveal that CuFe2O4/MMT-k10 nanocomposite could activate the persulfate, which is an efficient technique for LVF degradation in water.

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