메뉴 건너뛰기




Volumn 44, Issue 11, 2005, Pages 3973-3982

Application of a moving-window-adaptive neural network to the modeling of a full-scale anaerobic filter process

Author keywords

[No Author keywords available]

Indexed keywords

ANAEROBIC DIGESTION; COMPUTER SIMULATION; ERROR ANALYSIS; IMPULSE RESPONSE; MATHEMATICAL MODELS; NEURAL NETWORKS; ORGANIC ACIDS; SENSITIVITY ANALYSIS; WASTEWATER TREATMENT;

EID: 20344389745     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie048944a     Document Type: Article
Times cited : (31)

References (26)
  • 1
    • 0020793849 scopus 로고
    • Anaerobic fixed film wastewater treatment
    • Switzenbaum, M. S. Anaerobic fixed film wastewater treatment. Enzyme Microb. Technol. 1983, 5, 242.
    • (1983) Enzyme Microb. Technol. , vol.5 , pp. 242
    • Switzenbaum, M.S.1
  • 2
    • 0033038506 scopus 로고    scopus 로고
    • The use of the anaerobic baffled reactor (ABR) for wastewater treatment: A review
    • Barber, W. P.; Stuckey, D. C. The use of the anaerobic baffled reactor (ABR) for wastewater treatment: a review. Water Res. 1999, 33, 1559.
    • (1999) Water Res. , vol.33 , pp. 1559
    • Barber, W.P.1    Stuckey, D.C.2
  • 3
    • 0031284870 scopus 로고    scopus 로고
    • Modelling and simulation of anaerobic stratified biofilm for methane production and prediction of multiple steady states
    • Gupta, N.; Gupta, S. K.; Ramachandran, K. B. Modelling and simulation of anaerobic stratified biofilm for methane production and prediction of multiple steady states. Chem. Eng. J. 1997, 65, 37.
    • (1997) Chem. Eng. J. , vol.65 , pp. 37
    • Gupta, N.1    Gupta, S.K.2    Ramachandran, K.B.3
  • 4
    • 0032485039 scopus 로고    scopus 로고
    • Consecutive reaction kinetics involving distributed fraction of methanogens in fluidized-bed bioreactors
    • Wu, C. S.; Huang, J. S.; Van, J. L.; Jih, C. G. Consecutive reaction kinetics involving distributed fraction of methanogens in fluidized-bed bioreactors. Biotechnol. Bioeng. 1998, 57, 367.
    • (1998) Biotechnol. Bioeng. , vol.57 , pp. 367
    • Wu, C.S.1    Huang, J.S.2    Van, J.L.3    Jih, C.G.4
  • 5
    • 0033526422 scopus 로고    scopus 로고
    • A comprehensive model of anaerobic bioconversion of complex substrates to biogas
    • Angelidaki, I.; Ellegaard, L.; Ahring, B. K A comprehensive model of anaerobic bioconversion of complex substrates to biogas. Biotechnol. Bioeng. 1999, 63, 363.
    • (1999) Biotechnol. Bioeng. , vol.63 , pp. 363
    • Angelidaki, I.1    Ellegaard, L.2    Ahring, B.K.3
  • 6
    • 0035923336 scopus 로고    scopus 로고
    • Dynamic model development and parameter identification for an anaerobic wastewater treatment process
    • Bernard, O.; Hadj-Sadok, Z.; Dochain, D.; Genovesi, A.; Steyer, J. P. Dynamic model development and parameter identification for an anaerobic wastewater treatment process. Biotechnol. Bioeng. 2001, 75, 424.
    • (2001) Biotechnol. Bioeng. , vol.75 , pp. 424
    • Bernard, O.1    Hadj-Sadok, Z.2    Dochain, D.3    Genovesi, A.4    Steyer, J.P.5
  • 8
    • 0034256936 scopus 로고    scopus 로고
    • A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems
    • Tay, J. H.; Zhang, X. A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Res. 2000, 34, 2849.
    • (2000) Water Res. , vol.34 , pp. 2849
    • Tay, J.H.1    Zhang, X.2
  • 9
    • 0037142398 scopus 로고    scopus 로고
    • Hybrid neural network modeling of a full-scale industrial wastewater treatment process
    • Lee, D. S.; Jeon, C. O.; Park, J. M.; Chang, K. S. Hybrid neural network modeling of a full-scale industrial wastewater treatment process. Biotechnol. Bioeng. 2002, 78, 670.
    • (2002) Biotechnol. Bioeng. , vol.78 , pp. 670
    • Lee, D.S.1    Jeon, C.O.2    Park, J.M.3    Chang, K.S.4
  • 10
    • 0032099978 scopus 로고    scopus 로고
    • Automatic early stopping using cross validation: Quantifying the criteria
    • Prechelt, L. Automatic early stopping using cross validation: quantifying the criteria. Neural Networks 1998, 11, 761.
    • (1998) Neural Networks , vol.11 , pp. 761
    • Prechelt, L.1
  • 11
    • 0033105479 scopus 로고    scopus 로고
    • A comparison of the ability of black box and neural network models of ARX structure to represent a fluidized bed anaerobic digestion process
    • Premier, G. C.; Dinsdale, R.; Guwy, A. J.; Hawkes, F. R.; Hawkes, D. L.; Wilcox, S. J. A comparison of the ability of black box and neural network models of ARX structure to represent a fluidized bed anaerobic digestion process. Water Res. 1999, 33, 1027.
    • (1999) Water Res. , vol.33 , pp. 1027
    • Premier, G.C.1    Dinsdale, R.2    Guwy, A.J.3    Hawkes, F.R.4    Hawkes, D.L.5    Wilcox, S.J.6
  • 12
    • 0037197169 scopus 로고    scopus 로고
    • Application of neural network for simulation of upflow anaerobic sludge blanket (UASB) reactor performance
    • Sinha, S.; Bose, P.; Jawed, M.; John, S.; Tare, V. Application of neural network for simulation of upflow anaerobic sludge blanket (UASB) reactor performance. Biotechnol. Bioeng. 2002, 77, 806.
    • (2002) Biotechnol. Bioeng. , vol.77 , pp. 806
    • Sinha, S.1    Bose, P.2    Jawed, M.3    John, S.4    Tare, V.5
  • 13
    • 0030780449 scopus 로고    scopus 로고
    • Partial least squares modeling of an activated sludge plant: A case study
    • Teppola, P.; Mujunen, S. P.; Minkkinen, P. Partial least squares modeling of an activated sludge plant: a case study. Chemom. Intell. Lab. Syst. 1997, 38, 197.
    • (1997) Chemom. Intell. Lab. Syst. , vol.38 , pp. 197
    • Teppola, P.1    Mujunen, S.P.2    Minkkinen, P.3
  • 14
    • 0031696282 scopus 로고    scopus 로고
    • FIR model identification: Parsimony through kernel compression with wavelets
    • Nikolaou, M.; Vuthandam, P. FIR model identification: parsimony through kernel compression with wavelets. AIChE J. 1998, 44, 141.
    • (1998) AIChE J. , vol.44 , pp. 141
    • Nikolaou, M.1    Vuthandam, P.2
  • 15
    • 0033105287 scopus 로고    scopus 로고
    • Model selection in neural networks
    • Anders, U.; Korn, O. Model selection in neural networks. Neural Networks 1999, 12, 309.
    • (1999) Neural Networks , vol.12 , pp. 309
    • Anders, U.1    Korn, O.2
  • 16
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modeling
    • Qin, S. J. Recursive PLS algorithms for adaptive data modeling. Comput. Chem. Eng. 1998, 22, 503.
    • (1998) Comput. Chem. Eng. , vol.22 , pp. 503
    • Qin, S.J.1
  • 17
    • 0037419837 scopus 로고    scopus 로고
    • Identification on demand using a blockwise recursive partial least-squares technique
    • Vijaysai, P.; Gudi, R. D.; Lakshminarayanan, S. Identification on demand using a blockwise recursive partial least-squares technique. Ind. Eng. Chem. Res. 2003, 42, 540.
    • (2003) Ind. Eng. Chem. Res. , vol.42 , pp. 540
    • Vijaysai, P.1    Gudi, R.D.2    Lakshminarayanan, S.3
  • 18
    • 0030724606 scopus 로고    scopus 로고
    • Anaerobic pre-treatment of petrochemical effluents: Therephthalic acid wastewater
    • Kleerebezem, R.; Mortier, J.; Hulshoff Pol, L. W.; Lettinga, G. Anaerobic pre-treatment of petrochemical effluents: therephthalic acid wastewater. Water Sci. Technol. 1997, 36, 237.
    • (1997) Water Sci. Technol. , vol.36 , pp. 237
    • Kleerebezem, R.1    Mortier, J.2    Hulshoff Pol, L.W.3    Lettinga, G.4
  • 19
    • 0030660430 scopus 로고    scopus 로고
    • Pilot study of UASB process treating PTA manufacturing wastewater
    • Cheng, S. S.; Ho, C. Y.; Wu, J. H. Pilot study of UASB process treating PTA manufacturing wastewater. Water Sci. Technol. 1997, 36, 73.
    • (1997) Water Sci. Technol. , vol.36 , pp. 73
    • Cheng, S.S.1    Ho, C.Y.2    Wu, J.H.3
  • 21
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagan, M. T.; Menhaj, M. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 1994, 5, 989.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 989
    • Hagan, M.T.1    Menhaj, M.2
  • 22
    • 0028905904 scopus 로고
    • Process analysis, monitoring and diagnosis, using multivariate projection methods
    • Kourti, T.; MacGregor, J. F. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom. Intell. Lab. Syst. 1995, 28, 3.
    • (1995) Chemom. Intell. Lab. Syst. , vol.28 , pp. 3
    • Kourti, T.1    MacGregor, J.F.2
  • 24
    • 0030202174 scopus 로고    scopus 로고
    • Development of adaptive neural networks for flexible control of batch processes
    • Dirion, J. L.; Cabassud, M.; Le Lann, M. V.; Casamatta, G. Development of adaptive neural networks for flexible control of batch processes. Chem. Eng. J. 1996, 63, 65.
    • (1996) Chem. Eng. J. , vol.63 , pp. 65
    • Dirion, J.L.1    Cabassud, M.2    Le Lann, M.V.3    Casamatta, G.4
  • 25
    • 0042538707 scopus 로고    scopus 로고
    • Identification and control of anaerobic digesters using adaptive, on-line trained neural networks
    • Emmanouilides, C.; Petrou, L. Identification and control of anaerobic digesters using adaptive, on-line trained neural networks. Comput. Chem. Eng. 1997, 21, 113.
    • (1997) Comput. Chem. Eng. , vol.21 , pp. 113
    • Emmanouilides, C.1    Petrou, L.2
  • 26
    • 0035988097 scopus 로고    scopus 로고
    • Advanced controlling of anaerobic digestion by means of hierarchical neural networks
    • Holubar, P.; Zani, L.; Hager, M.; Froschl, W.; Radak, Z.; Braun, R. Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Res. 2002, 36, 2582.
    • (2002) Water Res. , vol.36 , pp. 2582
    • Holubar, P.1    Zani, L.2    Hager, M.3    Froschl, W.4    Radak, Z.5    Braun, R.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.