A fuzzy neural network-based soft sensor for modeling nutrient removal mechanism in a full-scale wastewater treatment system

References

• Kadlec , P. , Gabrys , B. and Strandt , S. 2009 . Data-driven soft sensors in the process industry . Comput. Chem. Eng. , 33 : 795 – 814 .
   Web of Science ®Google Scholar
• Fortuna , L. , Graziani , S. , Rizzo , A. and Xibilia , M. 2007 . Soft sensors for monitoring and control of industrial processes , London : Springer .
   Google Scholar
• Wold , S. , Sjöström , M. and Eriksson , L. 2001 . PLS-regression: A basic tool of chemometrics . Chemometr. Intell. Lab , 58 : 109 – 130 .
   Web of Science ®Google Scholar
• Jolliffe , I.T. 2002 . Principal Component Analysis , second ed. , New York , NY : Springer .
   Google Scholar
• Liu , H. , Kang , O. , Kim , M. , Oh , T. , Lee , S. , Kim , J.T. and Yoo , C. 2013 . Sustainable monitoring of indoor air pollutants in an underground subway environment using self-validating soft sensors . Indoor Built Environ , 22 : 94 – 109 .
   Google Scholar
• Liu , J. , Chen , D.-S. and Shen , J.-F. 2010 . Development of self-validating soft sensors using fast moving window partial least squares . Ind. Eng. Chem. Res. , 49 : 11530 – 11546 .
   Google Scholar
• Liu , Y. , Huang , D. , Li , Y. and Zhu , X. 2012 . Development of a novel self-validating soft sensor . Korean J. Chem. Eng. , 29 : 1135 – 1143 .
   Google Scholar
• Ge , Z. and Song , Z. 2010 . A comparative study of just-in-time-learning based methods for online soft sensor modeling . Chemometr. Intell. Lab , 104 : 306 – 317 .
   Web of Science ®Google Scholar
• Oh , T. , Liu , H. , Kim , M. , Lee , S. , Yeo , M.-K. and Yoo , C. 2012 . External analysis-based fuzzy PLS model for prediction and monitoring in MBR . Desalin. Water Treat. , 43 : 185 – 192 .
   Web of Science ®Google Scholar
• Bishop , C.M. 2006 . Pattern recognition and machine learning , New York , NY : Springer .
   Google Scholar
• Vapnik , V.N. 1998 . Statistical learning theory , New York , NY : Wiley .
   Google Scholar
• J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, NJ, 1997.
   Google Scholar
• Wu , J. , Zhang , G. , Zhang , Q. , Zhou , J. and Wang , Y. 2011 . Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter . Energy Build. , 43 : 1685 – 1693 .
   Web of Science ®Google Scholar
• Ráduly , B. , Gernaey , K.V. , Capodaglio , A.G. , Mikkelsen , P.S. and Henze , M. 2007 . Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study . Environ. Modell. Softw. , 22 : 1208 – 1216 .
   Web of Science ®Google Scholar
• Mjalli , F.S. , Al-Asheh , S. and Alfadala , H.E. 2007 . Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance . J. Environ. Manage. , 83 : 329 – 338 .
   PubMed Web of Science ®Google Scholar
• Molga , E. , Cherbański , R. and Szpyrkowicz , L. 2005 . Modeling of an industrial full-scale plant for biological treatment of textile wastewaters: Application of neural networks . Ind. Eng. Chem. Res. , 45 : 1039 – 1046 .
   Google Scholar
• Yan , W. , Shao , H. and Wang , X. 2004 . Soft sensing modeling based on support vector machine and Bayesian model selection . Comput. Chem. Eng. , 28 : 1489 – 1498 .
   Google Scholar
• Ge , Z. and Song , Z. 2008 . Online monitoring of nonlinear multiple mode processes based on adaptive local model approach . Control Eng. Pract. , 16 : 1427 – 1437 .
   Google Scholar
• Pai , T.Y. , Wan , T.J. , Hsu , S.T. , Chang , T.C. , Tsai , Y.P. , Lin , C.Y. , Su , H.C. and Yu , L.F. 2009 . Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent . Comput. Chem. Eng. , 33 : 1272 – 1278 .
   Google Scholar
• Civelekoglu , G. , Yigit , N.O. , Diamadopoulos , E. and Kitis , M. 2009 . Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network . Water Sci. Technol. , 60 : 1475 – 1487 .
   PubMedGoogle Scholar
• Huang , M. , Ma , Y. , Jinquan , W. and Yan , W. 2009 . Simulation of a paper mill wastewater treatment using a fuzzy neural network . Expert Syst. Appl. , 36 : 5064 – 5070 .
   Google Scholar
• Huang , M. , Wan , J. , Ma , Y. , Zhang , H. , Wang , Y. , Wei , C. , Liu , H. and Yoo , C. 2011 . A GA-based neural fuzzy system for modeling a paper mill wastewater treatment process . Ind. Eng. Chem. Res. , 50 : 13500 – 13507 .
   Web of Science ®Google Scholar
• Chung , I.F. , Lin , C.-J. and Lin , C.-T. 2000 . A GA-based fuzzy adaptive learning control network . Fuzzy Set. Syst. , 112 : 65 – 84 .
   Web of Science ®Google Scholar
• Takagi , T. and Sugeno , M. 1985 . Fuzzy identification of systems and its applications to modeling and control . IEEE Trans. Syst. Man Cybern. , 15 : 116 – 132 .
   Web of Science ®Google Scholar
• Sugeno , M. and Kang , G.T. 1988 . Structure identification of fuzzy model . Fuzzy Set. Syst. , 28 : 15 – 33 .
   Web of Science ®Google Scholar
• Kim , M. , Kim , Y. , Prabu , A. and Yoo , C. 2009 . A systematic approach to data-driven modeling and soft sensing in a full-scale plant . Water Sci. Technol. , 60 : 363
   PubMedGoogle Scholar