Optimization of photolysis of diclofenac using a response surface methodology

2013 ◽  
Vol 67 (4) ◽  
pp. 907-914 ◽  
Author(s):  
Jong-Kwon Im ◽  
Moon-Kyung Kim ◽  
Kyung-Duk Zoh
Keyword(s):  
Response Surface Methodology ◽  
Humic Acid ◽  
Response Surface ◽  
Goodness Of Fit ◽  
Environmental Parameters ◽  
Test Results ◽  
Optimal Conditions ◽  
Negative Contribution ◽  
Pareto Analysis ◽  
Determination Coefficient

This study investigates the effects of environmental parameters such as UV intensity (X1, 2.1 ∼ 6.3 mW/cm2), Fe(III) (X2, 0 ∼ 0.94 mg/L), NO3− (X3, 0 ∼ 20 mg/L) and humic acid (X4, 0 ∼ 30 mg/L) on the removal efficiency of diclofenac (DCF, Y), and optimization using a response surface methodology (RSM) based on Box–Behnken design (BBD). According to analysis of variance and t-test results (p < 0.001), the proposed quadratic BBD model based on a total of 29 experimental runs fitted well to the experimental data. Moreover, the determination coefficient (R2 = 0.990) and adjusted determination coefficient (Ra2 = 0.981) indicated that this model is adequate with a high goodness-of-fit. Variables of X1, X2 and X3 had significant positive contributions (p < 0.001), while X4 had significant negative contribution to the DCF removal (p < 0.001). A Pareto analysis showed that X4 was the most important factor (57.18%) in DCF photolytic removal. The predicted and observed DCF removal were 94.98 and 94.2% under optimal conditions (X1 = 6.29 mW/cm2, X2 = 0.75 mg/L, X3 = 15.65 mg/L and X4 = zero), respectively. The RSM not only gives valuable information on the interactions between these photoreactive species (UV intensity, Fe(III), NO3−, and humic acid) that influence DCF removal, but also identifies the optimal conditions for effective DCF removal in water.

Optimisation of the Oil Extraction from Nigella sativa Seeds Using Response Surface Methodology

2017 ◽  
Vol 68 (2) ◽  
pp. 331-336
Author(s):  
Gabriela Isopencu ◽  
Mirela Marfa ◽  
Iuliana Jipa ◽  
Marta Stroescu ◽  
Anicuta Stoica Guzun ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Nigella Sativa ◽  
Polynomial Equation ◽  
Herbaceous Plant ◽  
Box Behnken Design ◽  
Pareto Analysis ◽  
Black Cumin ◽  
Mediterranean Countries ◽  
Experimental Values

Nigella sativa, also known as black cumin, an annual herbaceous plant growing especially in Mediterranean countries, has recently gained considerable interest not only for its use as spice and condiment but also for its healthy properties of the fixed and essential oil and its potential as a biofuel. Nigella sativa seeds fixed oil, due to its high content in linoleic acid followed by oleic and palmitic acid, could be beneficial to human health. The objective of this study is to determine the optimum conditions for the solvent extraction of Nigella sativa seeds fixed oil using a three-level, three-factor Box-Behnken design (BBD) under response surface methodology (RSM). The obtained experimental data, fitted by a second-order polynomial equation were analysed by Pareto analysis of variance (ANOVA). From a total of 10 coefficients of the statistical model only 5 are important. The obtained experimental values agreed with the predicted ones.

Optimization of the Ultrasonic Extraction of Gypenoside III from Gynostemma pentaphyllum by Response Surface Methodology

2012 ◽  
Vol 550-553 ◽  
pp. 1866-1870
Author(s):  
Xiao Dan Tang ◽  
Hai Yang Hang ◽  
Shao Yan Wang ◽  
Jing Xiang Cong
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Extraction Yield ◽  
Raw Material ◽  
Extraction Temperature ◽  
Optimal Conditions ◽  
Gynostemma Pentaphyllum ◽  
Bioactive Component ◽  
Yield Value ◽  
Ultrasonic Time

Gypenosides III is a major bioactive component which is rich in Gynostemma pentaphyllum. For better utilization of the native resource, response surface methodology was used to optimize the extraction conditions of gypenosides III from G. pentaphyllum. The effects of three independent variables on the extraction yield of gypenosides III were investigated and the optimal conditions were evaluated by means of Box-Behnken design. The optimal conditions are as follows: ratio of ethanol to raw material 25, extraction temperature 58°C and ultrasonic time 25min. Under these conditions, the yield of gypenoside III is 1.216±0.05%, which is agreed closely with the predicted yield value.

scholarly journals Optimization of Supercritical Carbon Dioxide Extraction of Quercus infectoria Oil

2015 ◽  
Vol 74 (7) ◽  
Author(s):  
Liza Md Salleh ◽  
Stashia ELeaness Rosland Abel ◽  
Gholamreza Zahedi ◽  
Russly Abd Rahman ◽  
Hasmida Mohd Nasir ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Goodness Of Fit ◽  
Maximum Yield ◽  
Extraction Yield ◽  
Extraction Time ◽  
Extraction Temperature ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Quercus Infectoria

This current study focuses on the modelling and optimization of supercritical fluid extraction of Quercus infectoria galls oil. In this case, response surface methodology (RSM) and artificial neural network (ANN) were applied for the modelling and prediction of extraction yield of galls oil. A 17-run Box-Behnken Design (BBD) was employed to statistically optimize the process parameters of SC-CO2 extraction of Quercus infectoria galls at a condition as follows: pressure (5000, 6000, 7000 Psi), temperature (40, 50, 60°C) and extraction time (30, 45, 60 min). The maximum yield of the extracted oil is1.12 % and the optimum conditions are at an extraction pressure of 5574 Psi; extraction temperature of 75°C and extraction time of 54 min. Under the optimal conditions, the experimental results agree with the predicted values obtained through analysis of variance (ANOVA). This indicates a successful response surface methodology and highly satisfactory goodness of fit of the model used. The analysis of experimental design for process optimization results demonstrates that temperature and extraction time are the main parameters that influence the oil extraction of Quercus infectoria.

scholarly journals Response surface methodology-artificial neural network based optimization and strain improvement of cellulase production by Streptomyces sp.

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Ega Soujanya Lakshmi ◽  
Manda Rama Narasinga Rao ◽  
Muddada Sudhamani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Wild Strain ◽  
Cellulase Activity ◽  
Cellulase Production ◽  
Optimal Conditions ◽  
Zone Formation ◽  
Artificial Neural

ABSTRACT Thirty seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, grampositive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on CMC agar plate after Gram’s iodine staining.  Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG) and Ethyl methane sulphonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86 fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Effect of pH, temperature, duration of incubation, , and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxy Methyl Cellulase as a substrate are 55 oC of temperature, pH of 5.0 and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimised to 2 fold of the activity of the wild type by RSM and ANN.  

scholarly journals Applying Response Surface Methodology to optimize the decarbonization process of Timahdit oil shale

2019 ◽  
Vol 8 (5) ◽  
pp. 372-379 ◽  
Author(s):  
Laila Makouki ◽  
Meriem Tarbaoui ◽  
Samia Glissi ◽  
Said Mansouri ◽  
Hassan Hannache ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Acetic Acid ◽  
Response Surface ◽  
Oil Shale ◽  
Processing Time ◽  
Texture Evolution ◽  
Optimal Conditions ◽  
X Ray ◽  
Decarbonization Process ◽  
Scanning Electron

The present article aims to optimize the decarbonization of Timahdit oil shale layer Y by removing carbonates from the raw rock using acetic acid. The response surface methodology “central composite design (CCD)” has been used as a method of optimization to study the 3 factors of the process. The factors studied are the concentration of the acid, the processing time, and the ratio (liquid/solid). The optimal conditions with 68.17% of residue rate are obtained with 2 mol/l as concentration, 120 min as a time of treatment and 10.02 for the ratio.The raw (Y) and optimized materials (YO) were characterized by Scanning Electron Microscopy (SEM), X-ray fluorescence (XRF) and X-ray diffraction (XRD). The results showed that the acetic acid used to remove carbonates affects the chemical composition and the texture evolution of the residues.

Optimization of Conditions for Collagen Extraction from the Swim Bladders of Grass Carp (Ctenopharyngodon idella) by Response Surface Methodology

2010 ◽  
Vol 6 (3) ◽  
Author(s):  
Bao Zhang ◽  
Yunzhong Chen ◽  
Xuefei Wei ◽  
Mingqi Li ◽  
Mengjin Wang
Keyword(s):  
Response Surface Methodology ◽  
Acetic Acid ◽  
Statistical Analysis ◽  
Response Surface ◽  
Acid Concentration ◽  
Grass Carp ◽  
Ctenopharyngodon Idella ◽  
Acetic Acid Concentration ◽  
Optimal Conditions ◽  
Solid Ratio

The effects of liquid-solid ratio, acetic acid concentration and extraction time on the yield of acid-soluble collagen(ASC) from the swim bladders of grass carp were optimized by statistical analysis using response surface methodology. The response surface methodology (RSM) was used to optimize the yield of ASC by implementing the Box-Wilson design. Statistical analysis of the results showed that the linear and quadric terms of these three variables had significant effects. However, no interactions between the three variables were found to contribute to the response at a significant level. The optimal conditions for higher yield of ASC were a liquid-solid ratio of 17.85, an acetic-acid concentration of 0.54 M and a time of 34 h. Under these conditions, the model predicted an ASC yield of 8.39%. Verification of the optimization showed that an ASC yield of 8.21±0.15% was observed under the optimal conditions. The experimental values agreed with the predicted values, using analysis of variance, indicating an excellent fit of the model used and the success of response surface methodology for modeling extraction of ASC from the swim bladders of grass carp.

Supercritical CO2 Extraction of Plumula nelumbinis Oil Rich in γ-Sitosterol Using Response Surface Methodology

2013 ◽  
Vol 864-867 ◽  
pp. 213-216
Author(s):  
Dong Dong Jia ◽  
Rong Lan ◽  
Yong Yue Sun
Keyword(s):  
Response Surface Methodology ◽  
Factorial Design ◽  
Response Surface ◽  
Surface Analysis ◽  
Full Factorial Design ◽  
Response Surface Analysis ◽  
Optimal Conditions ◽  
Full Factorial

Supercritical CO2extraction ofPlumula nelumbinisoil rich in γ-sitosterol was investigated with a 42full factorial design and response surface analysis. At optimal conditions (P=35 MPa,T=55 C,dp=0.22 mm,Q=2.0 L/min), the yield of the extracted oil was up to 12.2%, in which the concentration of γ-sitosterol was 7.38%, indicating that the γ-sitosterol contents inPlumula nelumbinisand its oil were much higher than that in other vegetables.

Sign in / Sign up

Export Citation Format

Share Document