Box-Behnken Surface Statistical Design Research Articles

Improved pollutant management by kinetic and Box-Behnken design analysis of HDTMA-modified bentonite's adsorption of indigo carmine dye

The optimal performance characteristics of hexadecyltrimethylammonium-modified bentonite (HDTMA-B) for the removal of indigo carmine (IC, vat dye) from aqueous solution require a process parameter study. Using batch mode experiments and kinetic models, the study evaluated the adsorption behavior and dye-adsorbent interaction at different temperatures, adsorbent dosages, and initial dye concentrations. The rating mechanism for adsorption in ICs was found to be quite similar to the pseudo-second-order model. All of these IC dye removal adsorption parameters were optimized using a Box-Behnken surface statistical design with a pH of 6.6 as the starting point (as a fixed input parameter). At the 95% confidence level, the main effects of starting dye concentration and adsorbent dosage were both statistically significant, as was the square impact of initial dye concentration (ANOVA). There was a negative interaction between the adsorbent dose and the initial dye concentration, and this led to the discovery of a substantial detrimental effect. The primary factor in IC elimination was the initial dye concentration component (PC = 48.36%). At 34.28 °C and a starting dye concentration of 53.3 mg L-1, 0.97 g L-1 of adsorbent was optimal for removing 96.40% of IC from aqueous solutions, yielding a desirability of 1.00. The removal effectiveness of IC dye by HDTMA-modified bentonite could be predicted and the experimental parameters optimized using the simulated polynomial function. The regeneration capacity of the HDTMA-B adsorbent proves its importance in industrial wastewater treatment.

Adsorption of Ibuprofen as a pharmaceutical pollutant from aqueous phase using zinc oxide nanoparticles: Green synthesis, batch adsorption and biological activities.

Recent interest has focused on the biosynthesis of metal nanoparticles, particularly from plants. The production of precipitate served as an early indicator of the presence in the present study's use of ZnO nanoparticles green synthesis of these particles, which was further validated by; Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD). Additionally, the BET was used to calculate the surface area, which came out to be 119.12 m2 /g. Since the true effects of new pollutants, including medicines, on the environment and human health are not well understood, their presence in aquatic systems poses a severe hazard. For this reason, the antibiotic Ibuprofen (IBP) was absorbable to ZnO-NPs in this search. As opposed to fitting to Langmuir isothermally, the adsorption process was discovered to be pseudo-second-order kinetically, and the reaction was determined to be a chemisorption process. The process was endothermic and spontaneous, according to thermodynamic studies. Maximizing IBP removal from aqueous solution required the use of a Box-Behnken surface statistical design with four components, four levels, and response surface modelling. Solution pH, IBP concentration, duration, and dose were the four parameters that were utilized. The regeneration process, which is employed for five cycles with excellent efficiency, is the best benefit of using ZnO-NPs. Examine the elimination of pollutants from actual samples as well. However, the adsorbent is quite effective at reducing biological activity. At high concentrations of ZnO-NPs demonstrated notable antioxidant activity and RBC hemocompatibility and no discernible hemolysis was seen. ZnO-NPs demonstrated a notable percent suppression of α-amylase up to 53.6 % at 400 μg/mL, and so displayed potential as an antidiabetic. Cyclooxygenase was suppressed by ZnO-NPs in an anti-inflammatory test (COX-1 & COX-2) up to 56.32 and 52.04 % at a concentration of 400 μg/mL, respectively. Significant anti-Alzheimer potential was demonstrated by ZnO-NPs at 400 μg/mL by inhibiting Acetyl cholinesterase (AChE) and Butyl cholinesterase (BChE) up to 68.98 ± 1.62% and 62.36% respectively. We came to the conclusion that guava extract is helpful for ZnO-NP reduction and capping. The bioengineered nanoparticles could prevent Alzheimer's, diabetes, and inflammation and were biocompatible. This article is protected by copyright. All rights reserved.

Effective Adsorption and Removal of Doxorubicin from Aqueous Solutions Using Mesostructured Silica Nanospheres: Box-Behnken Design Optimization and Adsorption Performance Evaluation.

The aim of this study is to evaluate the efficacy of mesoporous silica nanospheres as an adsorbent to remove doxorubicin (DOX) from aqueous solution. The surface and structural properties of mesoporous silica nanospheres were investigated using BET, SEM, XRD, TEM, ζ potential, and point of zero charge analysis. To optimize DOX removal from aqueous solution, a Box-Behnken surface statistical design (BBD) with four times factors, four levels, and response surface modeling (RSM) was used. A high amount of adsorptivity from DOX (804.84 mg/g) was successfully done under the following conditions: mesoporous silica nanospheres dose = 0.02 g/25 mL; pH = 6; shaking speed = 200 rpm; and adsorption time = 100 min. The study of isotherms demonstrated how well the Langmuir equation and the experimental data matched. According to thermodynamic characteristics, the adsorption of DOX on mesoporous silica nanospheres was endothermic and spontaneous. The increase in solution temperature also aided in the removal of DOX. The kinetic study showed that the model suited the pseudo-second-order. The suggested adsorption method could recycle mesoporous silica nanospheres five times, with a modest reduction in its ability for adsorption. The most important feature of our adsorbent is that it can be recycled five times without losing its efficiency.

Box–Behnken design for the optimization of methylene blue and methyl orange removal from aqueous solution by activated carbon

In this work, experimental design approach was adopted for the optimization of the removal of anionic and cationic model dyes (methylene blue and methyl orange) by activated carbon. A Box–Behnken surface statistical design with three factors and three-level combined with response surface modelling was employed to maximize dyes removal from aqueous solution. Three factors were used; solution pH, dyes concentration and activated carbon ratio to aqueous volume. Experimental results showed that solution pH has a positive effect on the adsorption of MB and a negative effect on the adsorption of MO. MO adsorption was more influenced by dye concentration in solution compared to MB adsorption. On the other side, MB adsorption was more influenced by the mass ratio of activated carbon compared to MO adsorption. The mass ration of activated carbon influence more MB adsorption at low dye concentration. In addition, at low solution pH, MO adsorption depends strongly on its concentration in solution

Adsorption properties of flouroquinolone type antibiotic ciprofloxacin into 2:1 dioctahedral clay structure: Box-Behnken experimental design

In this study, the removal of ciprofloxacin (CPx) from aqueous solutions by usingmontmorillonite (MMT) clay was studied. The effect of adsorbent amount, contact time, solution pH and temperature were experimentally performed in batch adsorption experiments to examine the adsorption capacity, isotherm, kinetic and thermodynamic parameters of the adsorption process. Under the experimental conditions the system reached equilibrium in 60 min and adsorption capacity was obtained 113.64 mg g−1. The maximum adsorption of CPx on the MMT clay was around 98% and the maximum adsorption capacity was found as 128 mg g−1 at pH 7. Adsorption mechanism was clarified with Langmuir, Freundlich, DubininRadushkevich and Temkin isotherm models. Adsorption experiments were performed at different temperatures to explain kinetic studies with pseudo first order, pseudo second order, intraparticle diffusion and Elovich model. The pseudo second-order kinetic model and Temkin isotherm model could better define the experimental data. Thermodynamic parameters showed that the CPx adsorption was endothermic and spontaneous. Also, the effect of such parameters as solid/liquid ratio (S/L), temperature (T) and pH on the adsorption of CPx was examined using response surface methodology based on Box–Behnken surface statistical design. According to Box-Benhken design, higher values of the correlation coefficients and p-values greater than 0.05 were in good adaptation with the optimum combination of process parameters, which demonstrated the fitness of the chosen model for evaluating the experimental data. In order to explain the interaction mechanism between MMT and CPx, FTIR, XRD, zeta potential and BET surface area analysis were performed before and after adsorption.

Extraction of Essential Oils from Vietnam’s Orange (Citrus sinensis) Peels by Hydrodistillation: Modeling and Process Optimization

In this study, extraction of essential oil from orange peels was investigated by hydrodistillation method and combine technology software to optimize the process. By using the response surface methodology (RSM) based on Box-Behnken surface statistical design, the effect of parameters such as water-to-material ratio (2-4 mL/g), extraction time (45-75 min), and temperature (120-140 ºC) on the extraction of essential oil process from orange (Citrus sinensis) peels was examined . All factors were significantly affected to the extraction yield of essential orange peel oil. Optimum conditions for extraction oil yield including the ratio of water to material, temperature, extraction time achieved 3.19 mL/g, 130.08 ºC, 74.31 min, respectively. Below the optimal extraction condition, the maximum yield of orange oil of 3.21 % was obtained. Using gas chromatography/mass spectrometry (GC/MS), the results revealed that the essential orange peels oil is extremely rich in limonene (98.343 %).

Fluoride removal from secondary effluent of the graphite industry using electrodialysis: Optimization with response surface methodology

Response surface methodology was utilized to model and optimize the operational variables for defluoridation using an electrodialysis process as the treatment of secondary effluent of the graphite industry. Experiments were conducted using a Box-Behnken surface statistical design in order to evaluate the effects and the interaction of the influential variables including the operational voltage, initial fluoride concentration and flow rate. The regression models for defluoridation and energy consumption responses were statistically validated using analysis of variance (ANOVA); high coefficient of determination values (R2 = 0.9772 and R2 = 0.9814; respectively) were obtained. The quadratic model exhibited high reproducibility and a good fit of the experimental data. The optimum values of the initial fluoride concentration, voltage and flow rate were found to be 13.9 mg/L, 13.4 V, 102.5 L/h, respectively. A fluoride removal efficiency of 99.69% was observed under optimum conditions for the treatment of the secondary effluent of the graphite industry.

Decoloration of waste PET alcoholysis liquid by an electrochemical method.

Disperse Red 60 simulated polyester alcoholysis liquid decoloration by electro-Fenton with Fe3O4 catalyst was studied. The influences of the main operating parameters such as catalyst dosage (0.3-0.9 g/L), current density (60-120 mA/cm2) and pH (1-7) were optimized by response surface methodology (RSM) based on Box-Behnken surface statistical design (BBD). In optimal conditions, the initial concentration of 25 mg/L disperse red polyester alcoholysis liquid was catalyzed by 0.6 g/L Fe3O4, and the decoloration efficiency was 97.18% with the current density of 90 mA/cm2 and initial pH of 4.6. There was a relative error of 1.18% with the predicted model when the predictive value was 98.25% under the same conditions. In addition, ultraviolet-visible absorption spectra (UV-Vis), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) were used to study the degradation mechanism during decoloration. The intermediates were identified and the proposed degradation pathways were investigated by liquid chromatography-mass spectrometry (LC-MS) analysis.

Heavy metal removal with pure and biochar rice husks: modeling and optimization using Box-Behnken design

In this work, the most common heavy metals such as Cu (II), Zn (II), and Pb (II) removal from aqueous leachate of industrial waste were investigated using pure and biochar rice husks. The influence of such parameters as pH, adsorbent dose (g L–1), and contact time (min) on the biosorption of rice and biochar rice was examined by using response surface methodology (RSM) based upon Box-Behnken surface statistical design at a leaching solution, Ci = 32.69 g L–1 as a fixed input parameter. The results show that both linear and squared terms of process variables are found significant on the response variable. The optimum conditions depending on both combinations and individual of all responses for the removal of Zn (II), Cu (II), and Pb (II) ions were determined using the optimising algorithm. To sum up, the use of biochar rice husk is found better than the use of pure rice husk according to statistical analysis. This study strongly indicated that pure and biochar rice husks are an influential alternative adsorbent for the removal of heavy metal ions by sorption.

Heavy metal removal with pure and biochar rice husks: modelling and optimisation using Box-Behnken design

In this work, the most common heavy metals such as Cu (II), Zn (II), and Pb (II) removal from aqueous leachate of industrial waste were investigated using pure and biochar rice husks. The influence of such parameters as pH, adsorbent dose (g L–1), and contact time (min) on the biosorption of rice and biochar rice was examined by using response surface methodology (RSM) based upon Box-Behnken surface statistical design at a leaching solution, Ci = 32.69 g L–1 as a fixed input parameter. The results show that both linear and squared terms of process variables are found significant on the response variable. The optimum conditions depending on both combinations and individual of all responses for the removal of Zn (II), Cu (II), and Pb (II) ions were determined using the optimising algorithm. To sum up, the use of biochar rice husk is found better than the use of pure rice husk according to statistical analysis. This study strongly indicated that pure and biochar rice husks are an influential alternative adsorbent for the removal of heavy metal ions by sorption.

The use of response surface methodology (RSM) for modeling of lead (II) removal from industrial waste by pumice and vermiculite

The main goal of the present work was to search the feasibility of pumice and vermiculite as adsorbents for lead (II) removal from industrial waste by using Box-Behnken method. Batch adsorption method was performed to evaluate the feasibility. Box-Behnken surface statistical design approach with three inlet parameters namely pH (2, 5, 8), adsorbent concentration (10, 50, 100 mg L–1), and contact time (5, 30, 120 min) was conformed optimising experimental conditions of lead (II) adsorption process. Experiments were carried out regression analysis demonstrated good proper of the experimental data to the linear model with coefficient of determination (R2) values of 91.49% and 98.51% for vermiculite and pumice, respectively. Optimisation of pH (2-2), adsorbent concentration (57.81 mg L–1–75.00 mg L–1), and contact time (89.06 min, –78.50 min) gave a maximum of ≈ 99.99% lead (II) removal for vermiculite and pumice, respectively.

Degradation of 2,4-dichlorophenoxyacetate isopropyl amine (2,4-D IPA) by O3/AC/UV in an internally slurry airlift photo-reactor

ABSTRACTAn externally illuminated slurry airlift reactor (ALR) was used to decompose 2,4-dichlorophenoxyacetate isopropyl amine during catalytic ozonation with activated carbon. The effect of superficial gas velocity (0.05–0.15 cm/s), UVAB irradiation (0–60 W), treatment period (10–30 min) and amount of activated carbon (0–0.8 g/l) on removal efficiency was investigated using response surface methodology (RSM) based on the Box–Behnken surface statistical design. Well-defined circulation pattern in the ALR allowed all the fluid elements to be exposed to high light intensity zone and achieve sufficient contact between the solid catalyst and the pollutant. Treatment period appeared as the most influential variable followed by the amount of activated carbon, superficial gas velocity and UV irradiation. A kinetic study was also carried out to evaluate the degradation efficiency versus the O3, O3/AC, O3/UV and O3/AC/UV combinations in which the last one had the highest impact. Efficient suspensions of AC in the ALR resulted in the high efficiency of the O3/AC system. No significant difference was observed between the overall kinetic constants determined in O3/AC and O3/AC/UV systems due to the light transmission obstacle of solid suspension.

Removal of textile dye reactive green-19 using bacterial consortium: Process optimization using response surface methodology and kinetics study

In the present work, removal of a textile dye Reactive Green-19 from the aqueous medium was investigated using a developed bacterial consortium. Optimal combinations of three significant process parameters pH (5–10); incubation temperature (25–45°C); and Yeast extract concentration (0–1.5g/100mL) were determined using response surface methodology. Experiments were based on Box–Behnken surface statistical design, performed at initial dye concentration of 100mg/L as a fixed input parameter. Regression analysis displayed good fit of the experimental data to the second-order polynomial model with the coefficient of determination (R2)=0.9974 and Predicted (R2)=0.9677. The adequacy of the model was established with various descriptive statistics like lack of fit, coefficient of variation, predicted residual sum of squares, and adequate precision. The optimal decolorization conditions were as follows: temperature, 32.04°C; pH, 8.3 and Yeast Extract concentration, 1.16g/100mL. Under optimized conditions, more than 97% removal efficiency was achieved within 24h. First order kinetic fitted well with experimental data for different concentrations of dye Reactive green-19. Michaelis-Menten kinetics was used to define the apparent relationship between the decolorization rate and the dye concentration at optimized condition. The double reciprocal plot for kinetics yield maximum decolorization rate, Vm=120.48h−1 and half saturation constant, Km=1674mg/L. Biodegradation of dye was confirmed through UV–vis spectroscopy, Fourier transform infra-red spectroscopy, and High-performance liquid chromatography analysis. Reactive green-19 degradation products were identified through GC–MS analysis.

Adsorptive removal of crystal violet dye by a local clay and process optimization by response surface methodology

The current study relates to the removal of a dye [crystal violet (CV)] from aqueous solutions through batch adsorption experiment onto a local clay from Morocco. The clay was characterized by X-ray diffraction, IR spectroscopy, X-ray fluorescence, scanning electron microscope, Brunauer–Emmett–Teller analysis and Fraunhofer diffraction method. The influence of independent variables on the removal efficiency was determined and optimized by response surface methodology using the Box–Behnken surface statistical design. The model predicted maximum adsorption of 81.62% under the optimum conditions of operational parameters (125 mg L−1 initial dye concentration, 2.5 g L−1 adsorbent dose and time of 43 min). Practically, the removal ranges in 27.4–95.3%.

Parametric optimization of Cu (II) and Ni (II) adsorption onto coal dust and magnetized sawdust using box‐behnken design of experiments

The simultaneous removal of Cu (II) and Ni (II) ions from aqueous solution was investigated by using two different adsorbents (coal dust and magnetized sawdust). Experiments were made as a function of initial metal concentration (5–100 mg/L), adsorbent dose (2–20 g/L), and temperature (20–40°C) for the adsorption of metals by employing response surface methodology (RSM) based on Box–Behnken surface statistical design. The pH was kept constant at 4 and 5 for coal dust and magnetized sawdust, respectively. The magnetized sawdust showed better results for both Cu (II) and Ni (II) removal as compared with coal dust. The F statistics and ANOVA analysis confirmed that the model chosen to explain the relationship between the factors and the response is correct.For this work, three‐level three‐factorial experimental design has been applied to validate adsorption parameters affecting the combined removal of Cu (II) and Ni (II) onto coal dust and magnetized saw dust. Optimization of process parameters for Cu (II) and Ni (II) ions adsorption onto the adsorbents was done using RSM. The empirical relationship between the response (adsorption percentage of heavy metal) and the independent variables (initial heavy metal concentration, adsorbent dose, and temperature) has been expressed by the quadratic models. These models in turn help to understand and establish the optimized set of conditions without performing the unnecessary experiments at various combinations. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1597–1604, 2016

Optimization of Sorption Parameters for Color Removal of Textile Dye by Cross-linked Chitosan Beads Using Box-Behnken Design

Cross-linked chitosan-epichlorohydrin beads (CEB) was prepared and studied for the removal of Reactive Red 4 (RR4) from textile industry. The effect of parameters such as adsorbent dosage (A: 0.5-1.5 g/L), pH (B: 4-10) and time of contact (C: 30-80 min) on the color removal of RR4 was investigated using response surface methodology (RSM) based on Box–Behnken surface statistical design at an initial RR4 concentration, Co = 100 mg/L as a fixed input parameter. The results of RSM indicated that the proposed model predicted the responses adequately within the limits of input parameters being used. Regression analysis showed good fit of the experimental data to the second-order polynomial model with coefficient of determination (R 2 ) value of 0.9958 and Fisher F-value of 185.36. Optimization of adsorbent dose (1.0 g/L), pH (4.0), and contact time (80 min) gave a maximum of 70.53% RR4 removal by CEB.

PROCESS PARAMETRIC OPTIMIZATION OF A DIVIDED WALL DISTILLATION COLUMN

The optimal design of a divided wall distillation column (DWC) involves a number of variables and is a multivariable nonlinear problem. Various process parameters such as reflux rate, liquid split, and vapor split affect the product quality and energy efficiency of a DWC. Response surface methodology (RSM) based on Box-Behnken surface statistical design (BBD) was used in this article to study the process parameter optimization. A commercial process simulation software was used for rigorous simulation using several multicomponent mixtures. It was found that the number of simulation runs reduced significantly for the optimization of a DWC using BBD. The optimization results of RSM take care of interactions between process variables, and the predictions agreed well with the rigorous simulation results. [Supplementary materials are available for this article. Go to the publisher's online edition of Chemical Engineering Communications for the following free supplemental resources: 17 data tables.]

Development of RSM- and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength

Laser transformation hardening (LTH) is an innovative and advanced laser surface modification technique as compared to conventional transformation hardening processes and has been employed in aerospace, marine, chemical applications, heat exchangers, cryogenic vessels, components for chemical processing and desalination equipment, condenser tubing, airframe skin, and nonstructural components which introduces the advantageous residual stresses into the surface, improving the mechanical properties like wear, resistance to corrosion, tensile strength, and fatigue strength. In the present study, LTH of commercially pure titanium, nearer to ASTM grade 3 of chemical composition was investigated using continuous wave 2 kW, Nd: YAG laser. The effect of laser process variables such as laser power, scanning speed, and focused position was investigated using response surface methodology (RSM) and artificial neural network (ANN) keeping argon gas flow rate of 10 lpm as fixed input parameter. This paper describes the comparison of the heat input (HI) and ultimate tensile strength (σ) (simply called as tensile strength) predictive models based on ANN and RSM. The paper also presents the effect of laser process variables on the HI and ultimate σ. The research work also emphasizes on the effect of HI on σ. The experiments were conducted based on a three-factor, three-level Box–Behnken surface statistical design. Quadratic polynomial equations were developed for proper process parametric study for its optimal performance characteristics. The experimental results under optimum conditions were compared with the simulated values obtained from the RSM and ANN model. Adequacy of the developed models was tested by analysis of variance technique. A multilayer feed-forward neural network with a Levenberg–Marquardt back-propagation algorithm was adopted to develop the relationships between the laser hardening process parameters, HI, and ultimate σ. The performance of the developed ANN models were compared with the second-order RSM mathematical models of HI and σ. There was good agreement between the experimental and simulated values of RSM and ANN. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. It has been found that there is a trend of increased tensile strength with the decrease of hardening heat input and a trend of increased tensile strength with the increase of hardening cooling rate. As heat input decreases, there will be a faster cooling rate. Considering the effect of HI on ultimate σ, it was found that the lower the heat input, the faster cooling rate. The details of experimentation, model development, testing, validation of models, effect of laser process variables on heat input and ultimate σ, effect of HI on σ, and performance comparison of RSM and ANN models are presented in the paper. The results of Box–Behnken design of RSM and ANN models also indicate that the proposed models predict the responses adequately within the limits of input parameters being used. It is suggested that regression equations can be used to find optimum conditions for HI and σ of laser-hardened commercially pure titanium material.

Removal of fluoride from aqueous solution by adsorption on Apatitic tricalcium phosphate using Box–Behnken design and desirability function

The adsorption method was used for fluoride removal from aqueous solution by Apatitic tricalcium phosphate. In this study, response surface methodology was employed for the removal of fluoride. Experiments were carried out as per Box–Behnken surface statistical design with four input parameters namely adsorbent dose (0.1–0.3g), initial concentration (30–60mgL−1), temperature (20–40°C) and pH (4–11). Contact time (90min) was taken as a fixed input parameter. Regression analysis showed good fit of the experimental data to the second-order polynomial model with coefficient of determination (R2) value of 0.966 and Fisher F-value of 10.28. Applying the method of the desirability function, optimization of adsorbent dose (29g), initial concentration (60mgL−1), T (40°C) and pH (4) gave a maximum of 82.34% fluoride removal white desirability of 0.916 by Apatitic tricalcium phosphate. Dynamic adsorption data were applied to pseudo-first-order and pseudo-second-order rate equations. Pseudo-second-order kinetic model well expressed fluoride adsorption onto Apatitic tricalcium phosphate. According to the correlation coefficients, the adsorption of fluoride on the Apatitic tricalcium phosphate was correlated well with the Langmuir and Freundlich models.

Optimization of an azo dye batch adsorption parameters using Box–Behnken design

Dye removal using adsorption requires a proper process parametric study to determine its optimal performance characteristics. In this study, response surface methodology was employed for the removal of methyl orange (MO) from aqueous solution using activated carbon-commercial grade (ACC) as an adsorbent. Experiments were carried out as per Box–Behnken surface statistical design with four input parameters namely adsorbent dose ( w: 5–20 g/l), contact time ( t: 2–6 h), temperature ( T: 25–55 °C) and pH (pH: 2–8). Initial MO concentration ( C o = 100 mg/l) was taken as a fixed input parameter. Regression analysis showed good fit of the experimental data to the second-order polynomial model with coefficient of determination ( R 2) value of 0.9114 and Fisher F-value of 10.28. Optimization of w (15.75 g/l), t (4 h), T (40 °C) and pH (2) gave a maximum of 99.11% MO removal by ACC.