Deniz Bingöl

Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin.

In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of heavy metal biosorption process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of process variables which are pH, biosorbent mass, and temperature, on the sorbed amount of lead were investigated through two-levels, three-factors central composite design (CCD). Same design was also utilized to obtain a training set for ANN. The results of two methodologies were compared for their predictive capabilities in terms of the coefficient of determination-R(2) and root mean square error-RMSE based on the validation data set. The results showed that the ANN model is much more accurate in prediction as compared to CCD.

Geochemical and spectroscopic investigations of Cd and Pb sorption mechanisms on contrasting biochars: engineering implications.

Biochars prepared from nut shells, plum stones, wheat straws, grape stalks and grape husks were tested as potential sorbents for Cd and Pb. Mechanisms responsible for metal retention were investigated and optimal sorption conditions were evaluated using the RSM approach. Results indicated that all tested biochars can effectively remove Cd and Pb from aqueous solution (efficiency varied between 43.8% and 100%). The removal rate of both metals is the least affected by the biochar morphology and specific surface but this removal efficiency is strongly pH-dependent. Results of variable metal removal combined with different optimized conditions explain the different metal sorption mechanisms, where the predominant mechanism is ion exchange. In addition, this mechanism showed very strong binding of sorbed metals as confirmed by the post-desorption of the fully metal-loaded biochars. Finally, these biochars could thus also be applicable for metal contaminated soils to reduce mobility and bioavailability of Cd and Pb.

Chemometric evaluation of the heavy metals distribution in waters from the Dilovası region in Kocaeli, Turkey

The main objective of this study was to test water samples collected from 10 locations in the Dilovası area (a town in the Kocaeli region of Turkey) for heavy metal contamination and to classify the heavy metal (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) contents in water samples using chemometric methods. The heavy metals in the water samples were identified using inductively coupled plasma-mass spectrometry (ICP-MS). To ascertain the relationship among the water samples and their possible sources, the correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were used as classification techniques. About 10 water samples were classified into five groups using PCA. A very similar grouping was obtained using CA.

Removal of Lead (II) from Aqueous Solution on Multiwalled Carbon Nanotube by Using Response Surface Methodology

ABSTRACT Heavy metals released into the environment continue to receive attention over with advances of technology, but with variations in concentration. Heavy metals released into the environment in various concentrations continue to receive attention, fostered by advances in technology. Lead, one of the best-known heavy metals, can be found at high levels in industrial wastewater, is nonbiodegradable, and is a neurotoxic metal. To remove Pb(II) ions from aqueous solutions, the adsorption process of Pb(II) ions was optimized by using response surface methodology (RSM). The effects of initial pH, initial concentration of lead, and temperature on the adsorption efficiency (E, %) were investigated. pH was the most significant factor that affected the removal of lead. Central composite design (CCD) can be successfully applied to the removal of Pb(II) ions from aqueous solutions.

Trace elements and nutrients adsorption onto nano-maghemite in a contaminated-soil solution: A geochemical/statistical approach

Two experiments were carried out to study the competition for adsorption between trace elements (TEs) and nutrients following the application of nano-maghemite (NM) (iron nano-oxide; Fe2O3) to a soil solution (the 0.01molL(-1) CaCl2 extract of a TEs-contaminated soil). In the first, the nutrients K, N, and P were added to create a set of combinations: potential availability of TEs during their interaction with NM and nutrients were studied. In the second, response surface methodology was used to develop predictive models by central composite design (CCD) for competition between TEs and the nutrients K and N for adsorption onto NM. The addition of NM to the soil solution reduced specifically the concentrations of available As and Cd, but the TE-adsorption capacity of NM decreased as the P concentration increased. The CCD provided more concise and valuable information, appropriate to estimate the behavior of NM sequestering TEs: according to the suggested models, K(+) and NH4(+) were important factors for Ca, Fe, Mg, Mn, Na, and Zn adsorption (Radj(2)=95%, except for Zn with Radj(2)=87%). The obtained information and models can be used to predict the effectiveness of NM for the stabilization of TEs, crucial during the phytoremediation of contaminated soils.

Process modeling and thermodynamics and kinetics evaluation of Basic Yellow 28 adsorption onto sepiolite

AbstractThe dye sorption of Basic Yellow 28 (BY 28) in an adsorption process was optimized by varying three independent factors (initial pH, temperature, and ionic strength) using a central composite design, which is an experimental design that is useful in response surface methodology. The quantitative relationship was measured between the amount of the adsorbed dye (q, mg g−1) and the economical adsorbent, sepiolite, and the effect of initial pH, temperature, and ionic strength on the adsorption process was evaluated using the quadratic model. The model adequacy was tested by the analysis of variance, and the model was shown to be highly significant. The model showed that each of the independent factors was significant at the 5% significance level, revealing that the dye adsorption in the aqueous solution was affected by all three factors that were studied. The predicted maximum amount of adsorbed dye (31.54 mg g−1) was found using a solution pH of 6, a temperature of 25°C, and an ionic strength of 0.00...

Optimization of the Experimental Variables Influencing the Corrosion Rate of Aluminum Using Response Surface Methodology

The effects of temperature and sodium dodecyl benzene sulfonate (SDBS) concentration in hydrochloric acid (HCl) solution on aluminum corrosion rate were investigated using the response surface methodology (RSM) with central composite design (CCD). Optimization studies are an essential tool used in the corrosion systems. RSM is a statistical technique frequently used in most studies involving optimization. RSM was used to optimize the corrosion current density (icorr) of aluminum determined using the potentiodynamic polarization method and to evaluate the effects and the interactions of temperature and SDBS concentration. The lowest icorr was found as SDBS concentration at 130 mg/L and temperature at 24°C. This methodology was first applied successfully in optimizing the conditions that are needed to attain the minimum icorr of aluminum under tested conditions in this study.

New Inorganic–Organic Hybrid Materials and Their Oxides for Removal of Heavy Metal Ions: Response Surface Methodology Approach

In this paper, Cu(II), Fe(III), Pb(II), and Zn(II) heavy metal ions were removed from their aqueous solutions by using novel inorganic–organic hybrid materials, Al-GPTS-H and Al-GPTS-NaOSiMe3-H (hybrid material-1 and 2, respectively), and their oxides (calcined-1 and 2) as adsorbents. These ions removal by adsorption was optimized by using response surface methodology (RSM). Central composite design (CCD) method was used in order to investigate the effects of initial pH, initial metal concentration of solutions and adsorbent quantity on the adsorption efficiency (R, %). As a result of the experiments under optimum conditions, the maximum % R values were obtained by hybrid material-1 for Fe(III) (99.89%) and by calcined material-1 for Pb(II) (97.14%), respectively. These quite high adsorption efficiency values have shown that these hybrid materials and their oxides are suitable to use for heavy metal ions removal from aqueous solutions.

Response surface methodology approach to leaching of nickel laterite and evaluation of different analytical techniques used for the analysis of leached solutions

The objective of the study was to find the amount of nickel leached or extracted from a low-grade lateritic ore by H2SO4 leaching. In this study, the recovery of nickel and the dissolution of iron were attempted using response surface methodology, namely, central composite design, to find the optimum conditions of leaching for nickel lateritic ore. In the leaching experiments, effects on amount of iron in solution, as well as nickel recovery of various parameters such as concentration of H2SO4, leaching temperature and initial liquid to solid ratio on nickel recovery were examined. The significance of each factor and their interactive effects on measured responses were evaluated using statistically-designed experiments. Two second-order polynomial models of high significance were used to describe the relationships between the responses and factors. The estimated results from the equations showed good agreement with the experimental results. Determination coefficients (adj-R2) of nickel and iron were 99.66% and 99.92%, respectively. Nickel and iron contents (%) of the leached solutions were analyzed by flame atomic absorption spectrometry. The analysis of nickel in the leached solutions was carried out with calibration curves and standard addition techniques. Two analytical techniques were compared statistically by the “t-test”. In addition, to determine whether the effect of aliquot volume taken from the leached solution and the added standard amount from the measurement results, the effect of dilution on the precision of the calibration curve and the accuracy of the standard addition technique were investigated. These techniques were first applied to real samples using the RSM approach and received good results and were validated. The kinetic models of leaching were evaluated based on the “Shrinking core model” at different temperatures and particle sizes. The dissolution kinetics of nickel and iron were determined as “diffusion controlled”.

Investigation of the effect of physical conditions of a coating bath on the corrosion behavior of zinc coating using response surface methodology

In this study, to minimize the corrosion rate zinc coatings conditions were optimized using a Box-Behnken design combined with response surface methodology. The different electro zinc coatings were obtained by changing three independent variables; the stirring rate (200–600 rpm), temperature of the bath (30–70°C) and the coating time (30–120 min) in 3.5% NaCl solution. The corrosion rate of the obtained zinc coatings was determined using the Tafel polarization method. A quadratic regression equation was derived to predict the responses. The correspondence between the experimental and predicted values (R(adj)2 = 98.55%) demonstrates that the quadratic model was highly significant with 95% confidence levels (α = 0.05). The optimum zinc coating conditions based on the minimization of corrosion rate were; stirring rate 600 rpm, temperature of bath 55°C and a coating time of 50 min for icorr 16.35 μA/cm2.