Neural network modeling of Pb2+ removal from wastewater using electrodialysis

Artificial neural network (ANN) was applied to predict separation percent (SP) of lead ions from wastewater using electrodialysis (ED). The aim was to predict SP of Pb2+ as a function of concentration, temperature, flow rate and voltage. Optimum numbers of hidden layers and nodes in each layer were determined. The selected structure (4:6:2:1) was used for prediction of SP of lead ions as well as current efficiency (CE) of ED cell for different inputs in the domain of training data. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values.

[1]  Akbar Shahsavand,et al.  Neural networks modeling of hollow fiber membrane processes , 2007 .

[2]  Abhay B. Bulsari,et al.  SIMULATION OF MEMBRANE SEPARATION BY NEURAL NETWORKS , 1995 .

[3]  Lei Wang,et al.  Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases , 2006 .

[4]  S. Chellam,et al.  Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks , 2003 .

[5]  M. Sadrzadeh,et al.  Separation of copper ions by electrodialysis using Taguchi experimental design , 2004 .

[6]  Toraj Mohammadi,et al.  Water shortage and seawater desalination by electrodialysis , 2003 .

[7]  Abderrahim Abbas,et al.  Modeling of an RO water desalination unit using neural networks , 2005 .

[8]  M. Bustard,et al.  Biosorption of heavy metals by distillery-derived biomass , 1998 .

[9]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[10]  Toraj Mohammadi,et al.  Modeling of metal ion removal from wastewater by electrodialysis , 2005 .

[11]  Octavian Pastravanu,et al.  Neural network models for ultrafiltration and backwashing , 2000 .

[12]  Coskun Aydiner,et al.  Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal , 2005 .

[13]  Mark C. Porter,et al.  Handbook of Industrial Membrane Technology , 2007 .

[14]  Sunando DasGupta,et al.  Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network , 2005 .

[15]  Chiranjib Bhattacharjee,et al.  Studies on the applicability of artificial neural network (ANN) in continuous stirred ultrafiltration , 2002 .

[16]  Stefano Curcio,et al.  REDUCTION AND CONTROL OF FLUX DECLINE IN CROSS-FLOW MEMBRANE PROCESSES MODELED BY ARTIFICIAL NEURAL NETWORKS , 2006 .

[17]  M. El-Maazawi,et al.  Polarographic and Voltammetric Determination of Some Toxic Heavy Metal Ions in the Treated Wastewater at Abu-Dhabi, U.A.E. , 1999 .

[18]  Ali Zilouchian,et al.  Adaptive receptive fields for radial basis functions , 2001 .

[19]  Y. A. Liu,et al.  Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process Simulation , 2002 .

[20]  J. Calvo,et al.  Dynamic crossflow ultrafiltration of colloids: a deposition probability cake filtration approach , 2001 .

[21]  Michel Cabassud,et al.  Neural networks for long term prediction of fouling and backwash efficiency in ultrafiltration for drinking water production , 2000 .

[22]  M. Loizidou,et al.  Equilibrium and kinetic ion exchange studies of Pb2+, Cr3+, Fe3+ and Cu2+ on natural clinoptilolite. , 2002, Water research.

[23]  Seyed Mohammad Ali Razavi,et al.  Dynamic prediction of milk ultrafiltration performance: A neural network approach , 2003 .

[24]  Shankararaman Chellam,et al.  Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions , 2005 .

[25]  Cumali Kinaci,et al.  Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network. , 2006, Journal of biotechnology.

[26]  Toraj Mohammadi,et al.  Effect of operating parameters on Pb2+ separation from wastewater using electrodialysis* , 2004 .

[27]  Michel Cabassud,et al.  Dynamic modelling of crossflow microfiltration of bentonite suspension using recurrent neural networks , 1999 .

[28]  Ali Mortazavi,et al.  Dynamic modelling of milk ultrafiltration by artificial neural network , 2003 .

[29]  G. B. Sahoo,et al.  Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms , 2006 .

[30]  K. L. Edwards,et al.  The use of artificial neural networks in materials science based research , 2007 .

[31]  P. Alvarez,et al.  Heavy metal removal with Mexican clinoptilolite: multi-component ionic exchange. , 2001, Water research.

[32]  A. Ayame,et al.  Solvent extraction of Pb(II) from acid medium with zinc hexamamethylenedithiocarbamate followed by back-extraction and subsequent determination by FAAS , 1999 .

[33]  S. Chellam,et al.  Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks , 2003 .

[34]  J. Sandeaux,et al.  Electroextraction of Pb2+ ions from diluted solutions by a process combining ion-exchange textiles and membranes , 1998 .

[35]  M. Merroun,et al.  Removal of lead from aqueous solutions by Myxococcus xanthus , 1996 .

[36]  Won-Yong Lee,et al.  Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks , 2004 .

[37]  Ian Flood,et al.  Neural Networks in Civil Engineering. I: Principles and Understanding , 1994 .

[38]  P. Zhou,et al.  Heavy metal removal from wastewater in fluidized bed reactor , 1999 .

[39]  Luke E. K. Achenie,et al.  A hybrid neural network model for PEM fuel cells , 2005 .

[40]  Farouq S Mjalli,et al.  Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. , 2007, Journal of environmental management.

[41]  Benjamin W. Wah,et al.  Artificial Neural Networks: Concepts and Theory , 1990 .

[42]  J. M. Brady,et al.  Binding of hard and soft metal ions to Rhizopus arrhizus biomass , 1995 .

[43]  G. Trystram,et al.  Dynamic modeling of crossflow microfiltration using neural networks , 1995 .

[44]  R. Juang,et al.  Metal recovery and EDTA recycling from simulated washing effluents of metal-contaminated soils , 2000 .

[45]  Thomas Melin,et al.  Modelling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment , 2005 .

[46]  S. Chellam,et al.  Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks , 2005 .

[47]  P. Cañizares,et al.  Recovery of heavy metals by means of ultrafiltration with water-soluble polymers: calculation of design parameters , 2002 .

[48]  A. Kim,et al.  Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach , 2006 .

[49]  A. Levine,et al.  Microbial biosorption of copper and lead from aqueous systems. , 1995, The Science of the total environment.

[50]  Stefano Curcio,et al.  Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach , 2005 .

[51]  Goutam Banerjee,et al.  The role of salvinia rotundifolia in scavenging aquatic Pb (II) pollution: a case study , 1997 .

[52]  Stefano Curcio,et al.  Monitoring and control of TMP and feed flow rate pulsatile operations during ultrafiltration in a membrane module , 2002 .

[53]  W. Richard Bowen,et al.  Prediction of the rate of crossflow membrane ultrafiltration of colloids: A neural network approach , 1998 .

[54]  Michel Cabassud,et al.  Modelling of ultrafiltration fouling by neural network , 1998 .

[55]  Eric Latrille,et al.  Application of artificial neural networks for crossflow microfiltration modelling: “black-box” and semi-physical approaches , 1997 .

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

[57]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[58]  W. Richard Bowen,et al.  DYNAMIC ULTRAFILTRATION OF PROTEINS-A NEURAL NETWORK APPROACH , 1998 .

[59]  V. Andreoni,et al.  Heavy metal biosorption by bacterial cells , 1998 .

[60]  Khawla Abdulmohsen Al-Shayhi.  Modeling, simulation and optimization of large-scale commercial desalination plants , 1998 .

[61]  J. Aguilar,et al.  Solvent extraction of Ga(III), Cd(II), Fe(III), Zn(II), Cu(II), and Pb(II) with ADOGEN 364 dissolved in kerosene from 1–4 mol dm−3 HCl media , 2000 .

[62]  G. C. April,et al.  Selective lead ion recovery from multiple cation waste streams using the membrane-electrode process , 1998 .

[63]  Nidal Hilal,et al.  Rejection and modelling of sulphate and potassium salts by nanofiltration membranes: neural network and Spiegler–Kedem model , 2007 .

[64]  W. Richard Bowen,et al.  Predicting salt rejections at nanofiltration membranes using artificial neural networks , 2000 .

[65]  J. Laîné,et al.  Neural networks for prediction of ultrafiltration transmembrane pressure – application to drinking water production , 1998 .

[66]  Ronald W. Rousseau,et al.  Handbook Of Separation Process Technology , 2008 .

[67]  Michel Cabassud,et al.  Neural networks: a tool to improve UF plant productivity , 2002 .