메뉴 건너뛰기




Volumn 48, Issue 8, 2009, Pages 1371-1381

Neural network modeling of Pb2+ removal from wastewater using electrodialysis

Author keywords

Electrodialysis; Metal ions; Neural network; Wastewater treatment

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CURRENT EFFICIENCY; EXPERIMENTAL DATA; HIDDEN LAYERS; LEAD IONS; MODELING RESULTS; NEURAL NETWORK MODELING; OPTIMUM NUMBER; TRAINING DATA;

EID: 68549134912     PISSN: 02552701     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cep.2009.07.001     Document Type: Article
Times cited : (81)

References (72)
  • 1
    • 0037056753 scopus 로고    scopus 로고
    • Recovery of heavy metals by means of ultrafiltration with water-soluble polymers: calculation of design parameters
    • Canizares P., Perez A., and Camarillo R. Recovery of heavy metals by means of ultrafiltration with water-soluble polymers: calculation of design parameters. Desalination 144 (2002) 279-285
    • (2002) Desalination , vol.144 , pp. 279-285
    • Canizares, P.1    Perez, A.2    Camarillo, R.3
  • 3
    • 0033151345 scopus 로고    scopus 로고
    • Heavy metal removal from wastewater in fluidized bed reactor
    • Zhou P., Huang J.C., Li A.W.F., and Wei S. Heavy metal removal from wastewater in fluidized bed reactor. Water Res. 33 8 (1999) 1918-1924
    • (1999) Water Res. , vol.33 , Issue.8 , pp. 1918-1924
    • Zhou, P.1    Huang, J.C.2    Li, A.W.F.3    Wei, S.4
  • 4
    • 0032701592 scopus 로고    scopus 로고
    • Polarographic and voltammetric determination of some toxic heavy metal ions in the treated wastewater at Abu-Dhabi, UAE
    • E1-Hasani S.R., AI-Dhaheri S.M., EI-Maazawi M.S., and Kamal M.M. Polarographic and voltammetric determination of some toxic heavy metal ions in the treated wastewater at Abu-Dhabi, UAE. Water Sci. Technol. 40 7 (1999) 67-74
    • (1999) Water Sci. Technol. , vol.40 , Issue.7 , pp. 67-74
    • E1-Hasani, S.R.1    AI-Dhaheri, S.M.2    EI-Maazawi, M.S.3    Kamal, M.M.4
  • 5
    • 0342918681 scopus 로고    scopus 로고
    • 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 tool dmy3 HCI media
    • Rodryguez de San Miguel E., Aguilar J.C., Rodryguez M.T.J., and De Gyves J. 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 tool dmy3 HCI media. Hydrometallurgy 57 (2000) 151-165
    • (2000) Hydrometallurgy , vol.57 , pp. 151-165
    • Rodryguez de San Miguel, E.1    Aguilar, J.C.2    Rodryguez, M.T.J.3    De Gyves, J.4
  • 6
    • 0033038462 scopus 로고    scopus 로고
    • Solvent extraction of Pb(II) from acid medium with zinc hexamamethylenedithiocarbamate followed by backextraction and subsequent determination by FAAS
    • Dapaah A.R.K., Takano N., and Ayame A. Solvent extraction of Pb(II) from acid medium with zinc hexamamethylenedithiocarbamate followed by backextraction and subsequent determination by FAAS. Anal. Chim. Acta 386 (1999) 281-286
    • (1999) Anal. Chim. Acta , vol.386 , pp. 281-286
    • Dapaah, A.R.K.1    Takano, N.2    Ayame, A.3
  • 8
    • 0035255744 scopus 로고    scopus 로고
    • Heavy metal removal with Mexican clinoptilolite: multi-component ionic exchange
    • Mier M.V., Callejas R.L.P., Gehr R., Cisneros B.E.J., and Alvarez P.J.J. Heavy metal removal with Mexican clinoptilolite: multi-component ionic exchange. Water Res. 35 2 (2001) 373-378
    • (2001) Water Res. , vol.35 , Issue.2 , pp. 373-378
    • Mier, M.V.1    Callejas, R.L.P.2    Gehr, R.3    Cisneros, B.E.J.4    Alvarez, P.J.J.5
  • 9
    • 0001537725 scopus 로고    scopus 로고
    • Heavy metal biosorption by bacterial cells, Fresenius
    • Vecchio A., Finoli C., Di Simine D., and Andreoni V. Heavy metal biosorption by bacterial cells, Fresenius. J. Anal. Chem. 361 4 (1998) 338-342
    • (1998) J. Anal. Chem. , vol.361 , Issue.4 , pp. 338-342
    • Vecchio, A.1    Finoli, C.2    Di Simine, D.3    Andreoni, V.4
  • 11
    • 0031729767 scopus 로고    scopus 로고
    • Biosorption of heavy metals by distillery-derived biomass
    • Bustard M., and McHale A.P. Biosorption of heavy metals by distillery-derived biomass. Bioprocess Eng. 19 5 (1998) 351-353
    • (1998) Bioprocess Eng. , vol.19 , Issue.5 , pp. 351-353
    • Bustard, M.1    McHale, A.P.2
  • 12
    • 0028991711 scopus 로고
    • Microbial biosorption of copper and lead from aqueous systems
    • Pradhan A.A., and Levine A.D. Microbial biosorption of copper and lead from aqueous systems. Sci. Total Environ. 170 (1995) 209-220
    • (1995) Sci. Total Environ. , vol.170 , pp. 209-220
    • Pradhan, A.A.1    Levine, A.D.2
  • 13
    • 0029361093 scopus 로고
    • Binding of hard and soft metal ions to Rhizopus arrhizus biomass
    • Brady J.M., and Tobin J.M. Binding of hard and soft metal ions to Rhizopus arrhizus biomass. Enzyme Microb. Technol. 17 (1995) 791-796
    • (1995) Enzyme Microb. Technol. , vol.17 , pp. 791-796
    • Brady, J.M.1    Tobin, J.M.2
  • 14
    • 0344199880 scopus 로고    scopus 로고
    • Selective lead ion recovery from multiple cation waste streams using the membrane-electrode process
    • Gopal V., April G.C., and Schrodt V.N. Selective lead ion recovery from multiple cation waste streams using the membrane-electrode process. Sep. Purif. Technol. 14 (1998) 85-93
    • (1998) Sep. Purif. Technol. , vol.14 , pp. 85-93
    • Gopal, V.1    April, G.C.2    Schrodt, V.N.3
  • 16
    • 0034668324 scopus 로고    scopus 로고
    • Metal recovery and EDTA recycling from simulated washing effluents of metal contaminated soils
    • Juang R.S., and Wang S.W. Metal recovery and EDTA recycling from simulated washing effluents of metal contaminated soils. Water Res. 34 15 (2001) 3795-3803
    • (2001) Water Res. , vol.34 , Issue.15 , pp. 3795-3803
    • Juang, R.S.1    Wang, S.W.2
  • 17
    • 85047675660 scopus 로고    scopus 로고
    • The role of salvinia rotundifolia in scavenging aquatic Pb(II) pollution: a case study
    • Banerjee G., and Sarker S. The role of salvinia rotundifolia in scavenging aquatic Pb(II) pollution: a case study. Bioprocess Eng. 17 (1997) 295-300
    • (1997) Bioprocess Eng. , vol.17 , pp. 295-300
    • Banerjee, G.1    Sarker, S.2
  • 19
    • 0042265152 scopus 로고    scopus 로고
    • Water shortage and seawater desalination by electrodialysis
    • Mohammadi T., and Kaviani A. Water shortage and seawater desalination by electrodialysis. Desalination 158 (2003) 267-270
    • (2003) Desalination , vol.158 , pp. 267-270
    • Mohammadi, T.1    Kaviani, A.2
  • 20
    • 7444244032 scopus 로고    scopus 로고
    • Separation of copper ions by electrodialysis using Taguchi experimental design
    • Mohammadi T., Moheb A., Sadrzadeh M., and Razmi A. Separation of copper ions by electrodialysis using Taguchi experimental design. Desalination 169 (2004) 21-31
    • (2004) Desalination , vol.169 , pp. 21-31
    • Mohammadi, T.1    Moheb, A.2    Sadrzadeh, M.3    Razmi, A.4
  • 21
    • 34249723766 scopus 로고    scopus 로고
    • The use of artificial neural networks in materials science based research
    • Sha W., and Edwards K.L. The use of artificial neural networks in materials science based research. Mater. Des. 28 (2007) 1747-1752
    • (2007) Mater. Des. , vol.28 , pp. 1747-1752
    • Sha, W.1    Edwards, K.L.2
  • 22
    • 33846897687 scopus 로고    scopus 로고
    • Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance
    • Mjalli F.S., Al-Asheh S., and Alfadala H.E. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. J. Environ. Manage. 83 (2007) 329-338
    • (2007) J. Environ. Manage. , vol.83 , pp. 329-338
    • Mjalli, F.S.1    Al-Asheh, S.2    Alfadala, H.E.3
  • 23
    • 33747889741 scopus 로고    scopus 로고
    • Predicting flux decline in cross-flow membranes using artificial neural networks and genetic algorithms
    • Sahoo G.B., and Ray C. Predicting flux decline in cross-flow membranes using artificial neural networks and genetic algorithms. J. Membr. Sci. 283 (2006) 147-157
    • (2006) J. Membr. Sci. , vol.283 , pp. 147-157
    • Sahoo, G.B.1    Ray, C.2
  • 24
    • 33645931361 scopus 로고    scopus 로고
    • Prediction of permeate flux decline in cross-flow membrane filtration of colloidal suspension: a radial basis function neural network approach
    • Chen H., and Kim A.S. Prediction of permeate flux decline in cross-flow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination 192 (2006) 415-428
    • (2006) Desalination , vol.192 , pp. 415-428
    • Chen, H.1    Kim, A.S.2
  • 25
    • 0028987364 scopus 로고
    • Dynamic modeling of crossflow microfiltration using neural networks
    • Dornier M., Decloux M., Trystram G., and Lebert A. Dynamic modeling of crossflow microfiltration using neural networks. J. Membr. Sci. 98 (1995) 263-273
    • (1995) J. Membr. Sci. , vol.98 , pp. 263-273
    • Dornier, M.1    Decloux, M.2    Trystram, G.3    Lebert, A.4
  • 26
    • 0031172546 scopus 로고    scopus 로고
    • Application of artificial neural networks for crossflow microfiltration modelling: "black-box" and semiphysical approaches
    • Piron E., Latrille E., and Rene F. Application of artificial neural networks for crossflow microfiltration modelling: "black-box" and semiphysical approaches. Comp. Chem. Eng. 21 9 (1997) 1021-1030
    • (1997) Comp. Chem. Eng. , vol.21 , Issue.9 , pp. 1021-1030
    • Piron, E.1    Latrille, E.2    Rene, F.3
  • 27
    • 0032843364 scopus 로고    scopus 로고
    • Dynamic modeling of crossflow microfiltration of bentonite suspension using recurrent neural networks
    • Hamachi M., Cabassud M., Davin A., and Peuchot M.M. Dynamic modeling of crossflow microfiltration of bentonite suspension using recurrent neural networks. Chem. Eng. Process. 38 (1999) 203-210
    • (1999) Chem. Eng. Process. , vol.38 , pp. 203-210
    • Hamachi, M.1    Cabassud, M.2    Davin, A.3    Peuchot, M.M.4
  • 28
    • 12344318181 scopus 로고    scopus 로고
    • Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal
    • Aydiner C., Demir I., and Yildiz E. Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal. J. Membr. Sci. 248 (2005) 53-62
    • (2005) J. Membr. Sci. , vol.248 , pp. 53-62
    • Aydiner, C.1    Demir, I.2    Yildiz, E.3
  • 29
    • 20444445001 scopus 로고    scopus 로고
    • Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions
    • Chellam S. Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions. J. Membr. Sci 1-2 (2005) 35-42
    • (2005) J. Membr. Sci , Issue.1-2 , pp. 35-42
    • Chellam, S.1
  • 31
    • 0032508908 scopus 로고    scopus 로고
    • Neural networks for prediction of ultrafiltration transmembrane pressure-application to drinking water production
    • Delgrange N., Cabassud C., Cabassud M., Durand-Boulier L., and Laine J.M. Neural networks for prediction of ultrafiltration transmembrane pressure-application to drinking water production. J. Membr. Sci. 150 (1998) 111-123
    • (1998) J. Membr. Sci. , vol.150 , pp. 111-123
    • Delgrange, N.1    Cabassud, C.2    Cabassud, M.3    Durand-Boulier, L.4    Laine, J.M.5
  • 32
    • 0032486814 scopus 로고    scopus 로고
    • Dynamic ultrafiltration of proteins-a neural network approach
    • Bowen W.R., Jones M.G., and Yousef H.N.S. Dynamic ultrafiltration of proteins-a neural network approach. J. Membr. Sci. 146 (1998) 225-235
    • (1998) J. Membr. Sci. , vol.146 , pp. 225-235
    • Bowen, W.R.1    Jones, M.G.2    Yousef, H.N.S.3
  • 33
    • 0032216181 scopus 로고    scopus 로고
    • Prediction of the rate of crossflow membrane ultrafiltration of colloids: a neural network approach
    • Bowen W.R., Jones M.G., and Yousef H.N.S. Prediction of the rate of crossflow membrane ultrafiltration of colloids: a neural network approach. Chem. Eng. Sci. 53 22 (1998) 3793-3802
    • (1998) Chem. Eng. Sci. , vol.53 , Issue.22 , pp. 3793-3802
    • Bowen, W.R.1    Jones, M.G.2    Yousef, H.N.S.3
  • 34
    • 0034672848 scopus 로고    scopus 로고
    • Neural network models for ultrafiltration and backwashing
    • Teodosiu C., Pastravanu O., and Macoveanu M. Neural network models for ultrafiltration and backwashing. Water Res. 34 18 (2000) 4371-4380
    • (2000) Water Res. , vol.34 , Issue.18 , pp. 4371-4380
    • Teodosiu, C.1    Pastravanu, O.2    Macoveanu, M.3
  • 35
    • 0034351573 scopus 로고    scopus 로고
    • Neural networks for long term prediction of fouling and backwash efficiency in ultrafiltration for drinking water production
    • Delgrange-Vincent N., Cabassud C., Cabassud M., Durand-Bourlier L., and Laine J.M. Neural networks for long term prediction of fouling and backwash efficiency in ultrafiltration for drinking water production. Desalination 131 (2000) 353-362
    • (2000) Desalination , vol.131 , pp. 353-362
    • Delgrange-Vincent, N.1    Cabassud, C.2    Cabassud, M.3    Durand-Bourlier, L.4    Laine, J.M.5
  • 36
    • 0035357231 scopus 로고    scopus 로고
    • Dynamic crossflow ultrafiltration of colloids: a deposition probability cake filtration approach
    • Bowen W.R., Yousef H.N.S., and Calvo J.I. Dynamic crossflow ultrafiltration of colloids: a deposition probability cake filtration approach. Sep. Purif. Technol. 24 (2001) 297-308
    • (2001) Sep. Purif. Technol. , vol.24 , pp. 297-308
    • Bowen, W.R.1    Yousef, H.N.S.2    Calvo, J.I.3
  • 37
    • 0036984589 scopus 로고    scopus 로고
    • Studies on the applicability of artificial neural network (ANN) in continuous stirred ultrafiltration
    • Bhattacharjee C., and Singh M. Studies on the applicability of artificial neural network (ANN) in continuous stirred ultrafiltration. Chem. Eng. Technol. 25 12 (2002) 1187-1192
    • (2002) Chem. Eng. Technol. , vol.25 , Issue.12 , pp. 1187-1192
    • Bhattacharjee, C.1    Singh, M.2
  • 39
    • 0037056539 scopus 로고    scopus 로고
    • Monitoring and control of TMP and feed flow rate pulsatile operations during ultrafiltration in a membrane module
    • Curcio S., Calabro V., and Iorio G. Monitoring and control of TMP and feed flow rate pulsatile operations during ultrafiltration in a membrane module. Desalination 145 1-3 (2002) 217-222
    • (2002) Desalination , vol.145 , Issue.1-3 , pp. 217-222
    • Curcio, S.1    Calabro, V.2    Iorio, G.3
  • 40
    • 0141676388 scopus 로고    scopus 로고
    • Dynamic prediction of milk ultrafiltration performance: a neural network approach
    • Razavi S.M.A., Mousavi S.M., and Mortazavi S.A. Dynamic prediction of milk ultrafiltration performance: a neural network approach. Chem. Eng. Sci. 58 (2003) 4185-4195
    • (2003) Chem. Eng. Sci. , vol.58 , pp. 4185-4195
    • Razavi, S.M.A.1    Mousavi, S.M.2    Mortazavi, S.A.3
  • 41
    • 0043169729 scopus 로고    scopus 로고
    • Dynamic modeling of milk ultrafiltration by artificial neural network
    • Razavi S.M.A., Mousavi S.M., and Mortazavi S.A. Dynamic modeling of milk ultrafiltration by artificial neural network. J. Membr. Sci. 220 18 (2003) 47-58
    • (2003) J. Membr. Sci. , vol.220 , Issue.18 , pp. 47-58
    • Razavi, S.M.A.1    Mousavi, S.M.2    Mortazavi, S.A.3
  • 42
    • 33751162882 scopus 로고    scopus 로고
    • Integration of Matlab and LabWiew to design innovative control systems applicable to membrane processes
    • Hamburg, Germany, September 28-October 01, 2004
    • Curcio S., Calabro V., and Iorio G. Integration of Matlab and LabWiew to design innovative control systems applicable to membrane processes. Proceedings of the EuroMembrane 2004. Hamburg, Germany, September 28-October 01, 2004 (2004)
    • (2004) Proceedings of the EuroMembrane 2004
    • Curcio, S.1    Calabro, V.2    Iorio, G.3
  • 43
    • 20744456034 scopus 로고    scopus 로고
    • Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network
    • Rai P., Majumdar G.C., DasGupta S., and De S. Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network. J. Food Eng. 71 3 (2005) 273-281
    • (2005) J. Food Eng. , vol.71 , Issue.3 , pp. 273-281
    • Rai, P.1    Majumdar, G.C.2    DasGupta, S.3    De, S.4
  • 44
    • 10944249746 scopus 로고    scopus 로고
    • Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach
    • Curcio S., Scilingo G., Calabro V., and Iorio G. Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach. J. Membr. Sci. 246 2 (2005) 235-247
    • (2005) J. Membr. Sci. , vol.246 , Issue.2 , pp. 235-247
    • Curcio, S.1    Scilingo, G.2    Calabro, V.3    Iorio, G.4
  • 45
    • 33751174542 scopus 로고    scopus 로고
    • Reduction and control of flux decline in crossflow membrane processes modeled by artificial neural networks
    • Curcio S., Calabro V., and Iorio G. Reduction and control of flux decline in crossflow membrane processes modeled by artificial neural networks. J. Membr. Sci. 286 (2006) 125-132
    • (2006) J. Membr. Sci. , vol.286 , pp. 125-132
    • Curcio, S.1    Calabro, V.2    Iorio, G.3
  • 46
    • 0038054285 scopus 로고    scopus 로고
    • Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks
    • Shetty G.R., and Chellam S. Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks. J. Membr. Sci. 217 (2003) 69-86
    • (2003) J. Membr. Sci. , vol.217 , pp. 69-86
    • Shetty, G.R.1    Chellam, S.2
  • 47
    • 0037441163 scopus 로고    scopus 로고
    • Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks
    • Shetty G.R., Malki H., and Chellam S. Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks. J. Membr. Sci. 212 (2003) 99-112
    • (2003) J. Membr. Sci. , vol.212 , pp. 99-112
    • Shetty, G.R.1    Malki, H.2    Chellam, S.3
  • 48
    • 0034631748 scopus 로고    scopus 로고
    • Predicting salt rejection at nanofiltration membranes using artificial neural networks
    • Bowen W.R., Jones M.G., Webfoot J.S., and Yousef H.N.S. Predicting salt rejection at nanofiltration membranes using artificial neural networks. Desalination 129 (2000) 147-162
    • (2000) Desalination , vol.129 , pp. 147-162
    • Bowen, W.R.1    Jones, M.G.2    Webfoot, J.S.3    Yousef, H.N.S.4
  • 49
    • 33846786458 scopus 로고    scopus 로고
    • Rejection and modeling of sulphate and potassium salts by nanofiltration membranes: neural network and Spiegler-Kedem model
    • Al-Zoubi H., Hilal N., Darwish N.A., and Mohammad A.W. Rejection and modeling of sulphate and potassium salts by nanofiltration membranes: neural network and Spiegler-Kedem model. Desalination 206 (2007) 42-60
    • (2007) Desalination , vol.206 , pp. 42-60
    • Al-Zoubi, H.1    Hilal, N.2    Darwish, N.A.3    Mohammad, A.W.4
  • 50
    • 0028989580 scopus 로고
    • Simulation of membrane separation by neural networks
    • Niemi H., Bulsari A., and Palosaari S. Simulation of membrane separation by neural networks. J. Membr. Sci. 102 (1995) 185-191
    • (1995) J. Membr. Sci. , vol.102 , pp. 185-191
    • Niemi, H.1    Bulsari, A.2    Palosaari, S.3
  • 51
    • 24944461921 scopus 로고    scopus 로고
    • Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks
    • Zhao Y., Taylor J.S., and Chellam S. Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks. J. Membr. Sci. 263 (2005) 38-46
    • (2005) J. Membr. Sci. , vol.263 , pp. 38-46
    • Zhao, Y.1    Taylor, J.S.2    Chellam, S.3
  • 52
    • 27744448208 scopus 로고    scopus 로고
    • Modeling of an RO water desalination unit using neural networks
    • Abbas A., and Al-Bastaki N. Modeling of an RO water desalination unit using neural networks. Chem. Eng. J. 114 (2005) 139-143
    • (2005) Chem. Eng. J. , vol.114 , pp. 139-143
    • Abbas, A.1    Al-Bastaki, N.2
  • 53
    • 46449122568 scopus 로고    scopus 로고
    • Neural networks for predictive modeling and optimization of large-scale commercial water desalination plants
    • Al-Shayji K., and Liu Y.A. Neural networks for predictive modeling and optimization of large-scale commercial water desalination plants. Proc. IDA World Congress Desalination Water Science, vol. 1 (1997) 1-15
    • (1997) Proc. IDA World Congress Desalination Water Science, vol. 1 , pp. 1-15
    • Al-Shayji, K.1    Liu, Y.A.2
  • 54
    • 0037065279 scopus 로고    scopus 로고
    • Predictive modeling of large-scale commercial water desalination plants: data-based neural network and model-based process simulation
    • Al-Shayji K., and Liu Y.A. Predictive modeling of large-scale commercial water desalination plants: data-based neural network and model-based process simulation. Ind. Eng. Chem. Res. 41 25 (2002) 6460-6474
    • (2002) Ind. Eng. Chem. Res. , vol.41 , Issue.25 , pp. 6460-6474
    • Al-Shayji, K.1    Liu, Y.A.2
  • 55
    • 0035917629 scopus 로고    scopus 로고
    • Adaptive receptive fields for radial basis functions
    • Jafar M.M., and Zilouchian A. Adaptive receptive fields for radial basis functions. Desalination 135 (2001) 83-91
    • (2001) Desalination , vol.135 , pp. 83-91
    • Jafar, M.M.1    Zilouchian, A.2
  • 56
    • 34248587417 scopus 로고    scopus 로고
    • Neural networks modeling of hollow fiber membrane processes
    • Shahsavand A., and Pourafshari Chenar M. Neural networks modeling of hollow fiber membrane processes. J. Membr. Sci. 297 (2007) 59-73
    • (2007) J. Membr. Sci. , vol.297 , pp. 59-73
    • Shahsavand, A.1    Pourafshari Chenar, M.2
  • 57
    • 33750307883 scopus 로고    scopus 로고
    • Radial basis function neural networks based modeling of the membrane separation process: hydrogen recovery from refinery gases
    • Wang L., Shao C., Wang H., and Wu H. Radial basis function neural networks based modeling of the membrane separation process: hydrogen recovery from refinery gases. J. Nat. Gas Chem. 15 (2006) 230-234
    • (2006) J. Nat. Gas Chem. , vol.15 , pp. 230-234
    • Wang, L.1    Shao, C.2    Wang, H.3    Wu, H.4
  • 58
    • 27644555231 scopus 로고    scopus 로고
    • Modeling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment
    • Geissler S., Wintgens T., Melin T., Vossenkaul K., and Kullmann C. Modeling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment. Desalination 178 (2005) 125-134
    • (2005) Desalination , vol.178 , pp. 125-134
    • Geissler, S.1    Wintgens, T.2    Melin, T.3    Vossenkaul, K.4    Kullmann, C.5
  • 59
    • 33745941576 scopus 로고    scopus 로고
    • Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network
    • Cinar O., Hasar H., and Kinaci C. Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network. J. Biotechnol. 123 (2006) 204-209
    • (2006) J. Biotechnol. , vol.123 , pp. 204-209
    • Cinar, O.1    Hasar, H.2    Kinaci, C.3
  • 60
    • 1942422715 scopus 로고    scopus 로고
    • Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks
    • Lee W.Y., Park G.G., Yang T.H., Yoon Y.G., and Kim C.S. Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks. Int. J. Hydrogen Energy 29 (2004) 961-966
    • (2004) Int. J. Hydrogen Energy , vol.29 , pp. 961-966
    • Lee, W.Y.1    Park, G.G.2    Yang, T.H.3    Yoon, Y.G.4    Kim, C.S.5
  • 61
    • 11844305914 scopus 로고    scopus 로고
    • A hybrid neural network model for PEM fuel cells
    • Ou S., and Achenie L.E.K. A hybrid neural network model for PEM fuel cells. J. Power Sources 140 2 (2005) 319-330
    • (2005) J. Power Sources , vol.140 , Issue.2 , pp. 319-330
    • Ou, S.1    Achenie, L.E.K.2
  • 70
    • 7444262843 scopus 로고    scopus 로고
    • Modeling of metal ion removal from wastewater by electrodialysis
    • Mohammadi T., Moheb A., Sadrzadeh M., and Razmi A. Modeling of metal ion removal from wastewater by electrodialysis. Sep. Purif. Technol. 41 (2005) 73-82
    • (2005) Sep. Purif. Technol. , vol.41 , pp. 73-82
    • Mohammadi, T.1    Moheb, A.2    Sadrzadeh, M.3    Razmi, A.4
  • 71
    • 0024880831 scopus 로고
    • Multi-layer feedforward networks are universal approximations
    • Hornik K., Stinchombe M., and White H. Multi-layer feedforward networks are universal approximations. Neural Networks 2 (1989) 359-366
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchombe, M.2    White, H.3
  • 72
    • 0028416331 scopus 로고
    • Neural networks in civil engineering. I. Principles and understanding
    • Flood I., and Kartam N. Neural networks in civil engineering. I. Principles and understanding. J. Comp. Civil Eng. 8 (1994) 131-148
    • (1994) J. Comp. Civil Eng. , vol.8 , pp. 131-148
    • Flood, I.1    Kartam, N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.