Biosorption of copper(II) ions by flax meal: Empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation

Abstract In the present study, application of waste flax meal was investigated for the removal of copper(II) ions from aqueous solution. The effect of operating parameters such as metal ions concentration (20–200 ppm), biosorbent dosage (1–10 g/L) and solution pH (2–5) was modeled by both response surface methodology (RSM) and artificial neural network (ANN). This study compares central composite design (CCD), Box–Behnken design (BBD) and full factorial design (FFD) utility for modeling and optimization by response surface methodology. The best statistical predictability and accuracy resulted from CCD ( R 2  = 0.997, MSE = 0.34). Maximum biosorption efficiency expressed as the sorption capacity, which was found to be 34.4 mg/g, at initial Cu 2+ concentration of 200 ppm, biosorbent dosage of 1 g/L and initial solution pH of 5. The precision of the equation obtained by RSM was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of biosorption efficiency. A feed-forward neural network with a topology optimized by response surface methodology was applied successfully for prediction of biosorption performance for the removal of Cu 2+ ions by waste flax meal. The number of hidden neurons, the number of epochs, the adaptive value and the training goal were chosen for optimization. The multilayer perceptron with three neurons in one input layer, twenty two neurons in one hidden layer and one neuron in one output layer were required to build the model. The neural network turned out to be more accurate than RSM model in the prediction of Cu 2+ biosorption by flax meal. The novelty of this paper is application of response surface methodology in order to optimize artificial neural network topology. The research on modeling biosorption by RSM and ANN has been highly developed and new waste material flax meal as potential biosorbent has been proposed.

[1]  Anna Witek-Krowiak,et al.  Removal of microelemental Cr(III) and Cu(II) by using soybean meal waste--unusual isotherms and insights of binding mechanism. , 2013, Bioresource technology.

[2]  Amanat Ali Bhatti,et al.  Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution , 2014 .

[3]  R. Silverstein,et al.  Spectrometric identification of organic compounds , 2013 .

[4]  H. A. Staab Spectrometric Identification of Organic Compounds. Von R. M. Silverstein und G. C. Bassler. Verlag John Wiley & Sons, Inc., New York-London 1963. 1. Aufl., 177 S., geb. £ 3.4.0. , 1965 .

[5]  Anna Witek-Krowiak,et al.  State of the Art for the Biosorption Process—a Review , 2013, Applied Biochemistry and Biotechnology.

[6]  Güleser Kalaycı Demir,et al.  Thomas and artificial neural network models for the fixed-bed adsorption of methylene blue by a beach waste Posidonia oceanica (L.) dead leaves , 2011 .

[7]  Steven G. Gilmour,et al.  Augmented Box-Behnken Designs for Fitting Third-Order Response Surfaces , 2012 .

[8]  N. Mondal,et al.  Modeling of the adsorptive removal of arsenic: A statistical approach , 2014 .

[9]  Adrian Bonilla-Petriciolet,et al.  Modeling of fixed-bed adsorption of fluoride on bone char using a hybrid neural network approach , 2013 .

[10]  Ensar Oguz,et al.  Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and Neural Network Modelling , 2010 .

[11]  Jahan B. Ghasemi,et al.  Comparative study of Box–Behnken, central composite, and Doehlert matrix for multivariate optimization of Pb (II) adsorption onto Robinia tree leaves , 2013 .

[12]  M. Sillanpää,et al.  Utilization of agro-industrial and municipal waste materials as potential adsorbents for water treatment—A review , 2010 .

[13]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[14]  Siuli Mukhopadhyay,et al.  Response surface methodology , 2010 .

[15]  Pichiah Saravanan,et al.  Optimization of operating parameters using response surface methodology for adsorption of crystal violet by activated carbon prepared from mango kernel , 2012 .

[16]  S. S. Mahapatra,et al.  Artificial neural network modelling of As(III) removal from water by novel hybrid material , 2015 .

[17]  R. Marković,et al.  Using the Low-Cost Waste Materials for Heavy Metals Removal from the Mine Wastewater , 2011 .

[18]  N. Das,et al.  Biosorption of Zn(II) onto Pleurotus platypus: 5-Level Box–Behnken design, equilibrium, kinetic and regeneration studies , 2014 .

[19]  Erdal Kiliç,et al.  Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. , 2012, Bioresource technology.

[20]  Anna Witek-Krowiak,et al.  Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. , 2014, Bioresource technology.

[21]  Yashar Falamarzi,et al.  Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs) , 2014 .

[22]  Farshad Rahimpour,et al.  A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA) , 2014 .

[23]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .