scholarly journals Optimization of boron removal from water by electrodialysis using response surface methodology

2020 ◽  
Vol 81 (2) ◽  
pp. 293-300 ◽  
Author(s):  
Fatma Guesmi ◽  
Islem Louati ◽  
Chiraz Hannachi ◽  
Béchir Hamrouni
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Desirability Function ◽  
Feed Solution ◽  
Design Parameters ◽  
Anion Exchange Membranes ◽  
Boron Removal ◽  
Standard Design ◽  
Initial Ph

Abstract Boron removal from water containing 5 mg L−1 of boron using electrodialysis (ED) was studied as a function of several parameters such as flow rates, initial pH, coexisting anions and ED time. An ED cell, equipped with three cation exchange membranes (fumasep FKB) and two anion exchange membranes (fumasep FAB), was applied. The central composite design, which is the standard design of response surface methodology, was used to evaluate the effects and interactions of studied factors on boron removal efficiency. The effectiveness of the considered design parameters was well examined to find the optimum condition. The experimental data obtained were analyzed by analysis of variance for the polynomial model with 95% confidence level. Boron removal by ED showed to be independent of the electrodialysis time, whereas flow rate as well as the pH of the feed solution and also the coexisting anions on the feed solution play a significant role on the deboronation efficiency. According to the desirability function, the maximum response of 43.5% was predicted for boron removal at a pH equal to 10, a flow rate of 10 L h−1, a ratio between sulfates and that of boron equal to 2 and a reaction time of 25 minutes.

Optimization of photocatalytic degradation of real textile dye house wastewater by response surface methodology

2016 ◽  
Vol 74 (9) ◽  
pp. 1999-2009 ◽  
Author(s):  
Sayed Mohammad Bagher Hosseini ◽  
Narges Fallah ◽  
Sayed Javid Royaee
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Advanced Oxidation Process ◽  
Influential Factor ◽  
P Value ◽  
Textile Dye ◽  
Optimum Conditions ◽  
Effective Parameters ◽  
Initial Ph

This study evaluates the advanced oxidation process for decolorization of real textile dyeing wastewater containing azo and disperse dye by TiO2 and UV radiation. Among effective parameters on the photocatalytic process, effects of three operational parameters (TiO2 concentration, initial pH and aeration flow rate) were examined with response surface methodology. The F-value (136.75) and p-value <0.0001 imply that the model is significant. The ‘Pred R-Squared’ of 0.95 is in reasonable agreement with the ‘Adj R-Squared’ of 0.98, which confirms the adaptability of this model. From the quadratic models developed for degradation and subsequent analysis of variance (ANOVA) test using Design Expert software, the concentration of catalyst was found to be the most influential factor, while all the other factors were also significant. To achieve maximum dye removal, optimum conditions were found at TiO2 concentration of 3 g L−1, initial pH of 7 and aeration flow rate of 1.50 L min−1. Under the conditions stated, the percentages of dye and chemical oxygen demand removal were 98.50% and 91.50%, respectively. Furthermore, the mineralization test showed that total organic compounds removal was 91.50% during optimum conditions.

scholarly journals Optimization of the Electrospun Niobium–Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1644
Author(s):  
Babajide Oluwagbenga Fatile ◽  
Martin Pugh ◽  
Mamoun Medraj
Keyword(s):  
Response Surface Methodology ◽  
Tungsten Oxide ◽  
Response Surface ◽  
Flow Rate ◽  
Desirability Function ◽  
Polymer Concentration ◽  
Electrospun Nanofibers ◽  
Second Order ◽  
Working Parameters ◽  
Factor Interactions

The present research aimed to investigate the effect of working parameters on the electrospinning of niobium–tungsten oxide nanofibers and optimize the process using central composite design (CCD) based on the response surface methodology (RSM). An experiment was designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Meanwhile, a second-order prediction model of nanofibers diameter was fitted and verified using analysis of variance (ANOVA). The results show that the diameter of the nanofibers was significantly influenced by all the variables except the flow rate. Some second-order and cross factor interactions such as VD, DP, PF, PN, and P2 also have significant effects on the diameter of the nanofibers. The results of the ANOVA yielded R2 and adjusted R2 values of 0.96 and 0.93 respectively, this affirmed that the predictive model fitted well with the experimental data. Furthermore, the process parameters were optimized using the CCD method and a maximum desirability function of 226 nm was achieved for the diameter of the nanofibers. This is very close to the 233 nm diameter obtained from a confirmatory experiment using the optimum conditions. Therefore, the model is representative of the process, and it could be used for future studies for the reduction of the diameter of electrospun nanofibers.

scholarly journals Chemometric Access for RP-HPLC Simultaneous Estimation of Tadalafil and Dapoxetine Applying Response Surface Methodology

2021 ◽  
pp. 278-288
Author(s):  
S. Jayaseelan ◽  
N. Kannappan ◽  
V. Ganesan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Phosphate Buffer ◽  
Desirability Function ◽  
Hplc Method ◽  
Simultaneous Estimation ◽  
Statistical Experimental Design ◽  
Good Resolution ◽  
Rp Hplc

Aims: A RP-HPLC method was developed and validated for simultaneous estimation of Tadalafil and Dapoxetine applying statistical experimental design. Methodology: Multivariate optimization of the experimental conditions of RP-HPLC method was using Design of experiments. Independent three factors like phosphate buffer pH, mobile phase composition and flow rate were applied to design mathematical models. To study the response surface methodology by using Central composite design (CCD). In depth the effects of these independent factors was studied using CCD. Simultaneously optimize the retention time and resolution of the analytes was applying Desirability function. Results: The predicted and optimized data from contour picture containing phosphate buffer (pH 3.4) and acetonitrile in the ratio of 40:60%v/v respectively. Flow rate was found to be 0.8 ml/min. Baseline separation of both analytes with run time of less than 10.0 min and good resolution were achieved using these optimum conditions. Conclusion: Method was validated according to ICH guidelines by using optimized assay conditions. Therefore, the reports distinctly indicated that Quality by design access could be satisfactorily used to optimize RP-HPLC method for simultaneous estimation of Tadalafil and Dapoxetine.

Application of response surface methodology and artificial neural network: modeling and optimization of Cr(VI) adsorption process using Dowex 1X8 anion exchange resin

2016 ◽  
Vol 73 (10) ◽  
pp. 2402-2412 ◽  
Author(s):  
Soumaya Harbi ◽  
Fatma Guesmi ◽  
Dorra Tabassi ◽  
Chiraz Hannachi ◽  
Bechir Hamrouni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Desirability Function ◽  
Xrd Analysis ◽  
Neural Network Modeling ◽  
Initial Ph ◽  
Artificial Neural ◽  
The Fourier Transform

We report the adsorption efficiency of Cr(VI) on a strong anionic resin Dowex 1X8. The Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis of this adsorbent were investigated. Response surface methodology was applied to evaluate the main effects and interactions among initial pH, initial Cr(VI) concentration, adsorbent dose and temperature. Analysis of variance depicted that resin dose and initial pH were the most significant factors. Desirability function (DF) showed that the maximum Cr(VI) removal of 95.96% was obtained at initial pH 5, initial Cr(VI) concentration of 100 mg/L, resin dose of 2 g and temperature of 283 K. Additionally, a simulated industrial wastewater containing 14.95 mg/L of Cr(VI) was treated successfully by Dowex 1X8 at optimum conditions. Same experimental design was employed to develop the artificial neural network. Both models gave a high correlation coefficient (RRSM2 = 0.932, RANN2 = 0.996).

scholarly journals RESPONSE SURFACE METHODOLOGY AND DESIRABILITY FUNCTION AS STATISTICAL TOOLS IN THE OPTIMIZATION OF ELECTROCOAGULATION PROCESS IN SURFACE WATER

2020 ◽  
Vol 6 (2) ◽  
pp. 0152-0163
Author(s):  
Efraim Lázaro Reis ◽  
Maria Paulina Mendonza Combatt ◽  
Karina Esther Vasquez Sanjuan ◽  
Antônio Augusto Neves ◽  
Regina Célia Santos Mendonça
Keyword(s):  
Response Surface Methodology ◽  
Surface Water ◽  
Response Surface ◽  
Desirability Function ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Safe Work ◽  
Apparent Color ◽  
Initial Ph ◽  
Electrocoagulation Process

The electrocoagulation for water clarification for purification have been studied as alternative to the processes of the water treatment. This study aimed to model and to optimize this process for types of water with different turbidity conditions; considering the current intensity, electrolysis time and initial pH on apparent color removal, chemical oxygen demand and surface water turbidity. Electrocoagulation tests were make aluminum electrodes. The optimal operating conditions and models based on the response surface methodology were obtained with central composite design. In order to comply with the esthetic / organoleptic standard stipulated for this stage of the process, the characterization of the three types of water studied must have color < 15 uH, COD < 18 mg L-1 O2 and turbidity < 5 NTU). The correlation between the analyzed answers allows finding specific conditions of the parameters, assisting in the determination of safe work points in the operation of clarification.

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 Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

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