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.

[1]  K. Yetilmezsoy,et al.  Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. , 2008, Journal of hazardous materials.

[2]  V. Gupta,et al.  Biosorption of lead from aqueous solutions by green algae Spirogyra species: kinetics and equilibrium studies. , 2008, Journal of hazardous materials.

[3]  M. Alamri,et al.  Adsorptive removal of arsenite as (III) and arsenate as (V) heavy metals from waste water using Nigella sativa L. , 2009 .

[4]  S. Ferreira,et al.  Box-Behnken design: an alternative for the optimization of analytical methods. , 2007, Analytica chimica acta.

[5]  M B Kasiri,et al.  Modeling and optimization of heterogeneous photo-Fenton process with response surface methodology and artificial neural networks. , 2008, Environmental science & technology.

[6]  Xiangke Wang,et al.  Removal of Pb(II) from aqueous solution by oxidized multiwalled carbon nanotubes. , 2008, Journal of hazardous materials.

[7]  G. Blázquez,et al.  Kinetic Modeling of the Biosorption of Lead(II) from Aqueous Solutions by Solid Waste Resulting from the Olive Oil Production , 2011 .

[8]  Hang-Sik Shin,et al.  Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network , 2011, Bioprocess and biosystems engineering.

[9]  Mohamed Khayet,et al.  Artificial neural network modeling and response surface methodology of desalination by reverse osmosis , 2011 .

[10]  T. Albanis,et al.  Developments on chemometric approaches to optimize and evaluate microextraction. , 2009, Journal of chromatography. A.

[11]  S. H. Hasan,et al.  Biosorption of Pb(II) from water using biomass of Aeromonas hydrophila: central composite design for optimization of process variables. , 2009, Journal of hazardous materials.

[12]  Sermin Elevli,et al.  Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN , 2012 .

[13]  A. Çelekli,et al.  Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria. , 2011, Bioresource technology.

[14]  Rekha S. Singhal,et al.  Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan , 2008 .

[15]  Indrawati,et al.  Removal of Pb(II), Cd(II) and Co(II) from aqueous solution using Garcinia mangostana L. fruit shell. , 2010, Journal of hazardous materials.

[16]  S. Ferreira,et al.  Application of Multivariate Techniques in Optimization of Spectroanalytical Methods , 2007 .

[17]  Lihua Chen,et al.  Removal of lead (II) from aqueous solution by a new biosorption material by immobilizing Cyanex272 in cornstalks , 2011 .

[18]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[19]  R. Jacques,et al.  Ponkan peel: A potential biosorbent for removal of Pb(II) ions from aqueous solution , 2008 .

[20]  Hüseyin Bozkurt,et al.  Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model. , 2012, Bioresource technology.

[21]  S. H. Hasan,et al.  Bioadsorption of Arsenic: An Artificial Neural Networks and Response Surface Methodological Approach , 2011 .

[22]  Cristina Maria Quintella,et al.  Statistical designs and response surface techniques for the optimization of chromatographic systems. , 2007, Journal of chromatography. A.

[23]  M. Bezerra,et al.  Response surface methodology (RSM) as a tool for optimization in analytical chemistry. , 2008, Talanta.

[24]  A. Ahmad,et al.  Oryza sativa L. husk as heavy metal adsorbent: optimization with lead as model solution. , 2006, Bioresource technology.

[25]  V. Gupta,et al.  Removal of lead and chromium from wastewater using bagasse fly ash--a sugar industry waste. , 2004, Journal of colloid and interface science.

[26]  P. Negi,et al.  Antibacterial Activity of Nigella sativa L. Seed Extracts. , 2010 .

[27]  Yanhu Li,et al.  Biosorption of copper and lead ions by waste beer yeast. , 2006, Journal of hazardous materials.

[28]  Ashutosh Kumar Singh,et al.  Experimental design and response surface modeling for optimization of Rhodamine B removal from water by magnetic nanocomposite. , 2010 .

[29]  Huiping Zhang,et al.  A hybrid genetic--neural algorithm for modeling the biodegradation process of DnBP in AAO system. , 2011, Bioresource technology.

[30]  Mansour Ghaffari Moghaddam,et al.  Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles , 2011 .

[31]  W. Ngah,et al.  Removal of heavy metal ions from wastewater by chemically modified plant wastes as adsorbents: a review. , 2008, Bioresource technology.