Wastewater quality monitoring system using sensor fusion and machine learning techniques

Water Research

Volumn 46, Issue 4, 2012, Pages 1133-1144

  Qin, Xusong ^a   Gao, Furong ^a   Chen, Guohua ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Boosting IPW PLS; Online monitoring; Turbidity; UV Vis spectroscopy; Variable weighting; Wastewater treatment

Indexed keywords

CHEMICAL OXYGEN DEMAND; EFFLUENTS; ITERATIVE METHODS; LEAST SQUARES APPROXIMATIONS; MACHINE LEARNING; PARTIAL DISCHARGES; PILOT PLANTS; SPECTROMETERS; TURBIDITY; ULTRAVIOLET VISIBLE SPECTROSCOPY; WASTEWATER TREATMENT; WATER QUALITY;

BOOSTING-IPW-PLS; MACHINE LEARNING TECHNIQUES; ONLINE MONITORING; PARTIAL LEAST SQUARE (PLS); TOTAL SUSPENDED SOLIDS; UV/ VIS SPECTROSCOPY; VARIABLE WEIGHTING; WATER QUALITY MONITORING SYSTEMS;

MONITORING;

CHEMICAL OXYGEN DEMAND; DOMESTIC WASTE; LEARNING; QUALITATIVE ANALYSIS; SENSOR; SPECTROMETER; SUSPENDED LOAD; TURBIDITY; ULTRAVIOLET RADIATION; VISIBLE SPECTRUM; WASTE TREATMENT; WASTEWATER; WATER QUALITY;

ARTICLE; BIOSENSOR; BOOSTING ITERATIVE PREDICTOR WEIGHTING PARTIAL LEAST SQUARE; CALIBRATION; CONTROLLED STUDY; MACHINE LEARNING; ONLINE MONITORING; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO; TURBIDITY; ULTRAVIOLET SPECTROSCOPY; WASTE WATER; WASTE WATER MANAGEMENT; WASTE WATER QUALITY MONITORING SYSTEM; WATER ANALYSIS; WATER POLLUTION; WATER POLLUTION CONTROL; WATER QUALITY;

EID: 84856112872     PISSN: 00431354     EISSN: 18792448     Source Type: Journal    
DOI: 10.1016/j.watres.2011.12.005     Document Type: Article

References (42)

1. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974, 19:716-723.
2. American Public Health Association (APHA), American Water Works Association, Water Environment Federation Standard Methods for the Examination of Water and Wastewater 2005, American Public Health Association, New York. 21st ed.
3. Bookman S.K.E. Estimation of biochemical oxygen demand in slurry and effluent using ultraviolet spectrophotometry. Water Research 1997, 31:372-374.
4. Centner V., Massart D.L., de Noord O.E., de Jong S., Vandeginste B.M., Sterna C. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry 1996, 68:3851-3858.
5. Chambers B., Jones G. Optimization and uprating of activated sludge plants by efficient process design. Water Science and Technology 1988, 20:121-132.
6. Chen D., Hu B., Shao X.G., Su Q.D. Variable selection by modified IPW (iterative predictor weighting)-PLS (partial least squares) in continuous wavelet regression models. Analyst 2004, 129:664-669.
7. Chen G., Chen X., Yue P.L. Electrocoagulation and electroflotation of restaurant wastewater. Journal of Environmental Engineering 2000, 126:858-863.
8. Drainage Services Department of the Government of the Hong Kong Special Administrative Region (DSD-HKSAR) Sewage Service Charging Scheme 2011, [Online]. Available at:, (accessed 10.09.11). http://www.dsd.gov.hk/EN/Sewage_Services_Charging_Scheme/index.html.
9. Drucker H. Improving regressors using boosting techniques. Proceedings of the 14th International Conferences on Machine Learning 1997, 107-115. Morgan Kaufmann, San Mateo, CA. D.H. Fisher (Ed.).
10. Duffy N., Helmbold D. Boosting methods for regression. Machine Learning 2002, 47:153-200.
11. Esteban J., Starr A., Willetts R., Hannah P., Bryanston-Cross P. A review of data fusion models and architectures: towards engineering guidelines. Neural Computing & Applications 2005, 14:273-281.
12. Fogelman S., Blumenstein M., Zhao H. Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks. Neural Computing & Applications 2006, 15:197-203.
13. Forina M., Casolino C., Millan C.P. Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems. Journal of Chemometrics 1999, 13:165-184.
14. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 1997, 55:119-139.
15. Friedman J. Greedy function approximation: a gradient boosting machine. Annals of Statistics 2001, 29:1189-1232.
16. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting. Annals of Statistics 2000, 28:337-374.
17. Geladi P., Kowalski B. Partial least-squares regression: a tutorial. Analytica Chimica Acta 1986, 185:1-17.
18. Goicoechea H.C., Olivieri A.C. Wavelength selection for multivariate calibration using a genetic algorithm: a novel initialization strategy. Journal of Chemical Information and Modeling 2002, 42:1146-1153.
19. Hoskuldsson A. PLS regression methods. Journal of Chemometrics 1988, 2:211-228.
20. Klein L.A. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making 2004, SPIE Press, Bellingham, Wash.
21. Lourenco N.D., Chaves C.L., Novais J.M., Menezes J.C., Pinheiro H.M., Diniz D. UV spectra analysis for water quality monitoring in a fuel park wastewater treatment plant. Chemosphere 2006, 65:786-791.
22. Lu X., Jiang J.H., Wu H.L., Shen G.L., Yu R.Q. Variable-weighted PLS. Chemometrics and Intelligent Laboratory Systems 2007, 85:140-143.
23. Lutz R.W., Kalisch M., Buhlmann P. Robustified L2 boosting. Computational Statistics and Data Analysis 2008, 52:3331-3341.
24. Olsson G., Nielsen M.K., Yuan Z., Lynggaard-Jensen A., Steyer J.-P. Instrumentation, Control and Automation in Wastewater Systems 2005, IWA Publishing, London.
25. Pouet M.-F., Thomas O., Jacobsen B.N., Lynggaard-Jensen A., Quevauviller P. Conclusions of the workshop on methodologies for wastewater quality monitoring. Talanta 1999, 50:759-762.
26. Ridgeway G. The state of boosting. Computing Science and Statistics 1999, 31:172-181.
27. Roig B., Chalmin E., Touraud E., Thomas O. Spectroscopic study of dissolved organic sulfur (DOS): a case study of mercaptans. Talanta 2002, 56:585-590.
28. Russell S., Marshallsay D., MacCraith B., Devisscher M. Non-contact measurement of wastewater polluting load - the Laodmon project. Water Science and Technology 2003, 47:79-86.
29. Shao X., Bian X., Cai W. An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. Analytica Chimica Acta 2010, 666:32-37.
30. Swierenga H., Wülfert E., de Noord O.E., de Weijer A.P., Smilde A.K., Buydens L.M.C. Development of robust calibration models in near infrared spectrometric applications. Analytica Chimica Acta 2000, 411:121-135.
31. Tan C., Wang J., Wu T., Qin X., Li M. Determination of nicotine in tobacco samples by near-infrared spectroscopy and boosting partial least squares. Vibrational Spectroscopy 2010, 54:35-41.
32. Thomas O., Constant D. Trends in optical monitoring. Water Science and Technology 2004, 49:1-8.
33. US Environmental Protection Agency (EPA) Method 1664, Revision A: N-Hexane Extractable Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane Extractable Material (SGT-HEM; Non-Polar Material) by Extraction and Gravimetry 1999, [Online]. Available at:, (accessed 04.06.11). http://water.epa.gov/scitech/methods/cwa/oil/upload/2007_07_10_methods_method_oil_1664.pdf.
34. Vapnik V.N. The Nature of Statistical Learning Theory 2000, Springer, New York. second ed.
35. Varshney P.K. Multisensor data fusion. Electronics & Communication Engineering Journal 1997, 245-253.
36. Wold S., Ruhe A., Wold H., Dunn W. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing 1984, 5:735-743.
37. Wu, X.L., 2007. An applied research on information fusion and ensemble learning for spectral analysis of water quality. Ph.D. thesis, Zhejiang University, Hangzhou, China.
38. Wu X.L., Li Y.J., Wu T.J. A boosting-partial least squared method for ultraviolet spectroscopic analysis of water quality. Chinese Journal of Analytical Chemistry 2006, 8:1091-1095.
39. Wu X.L., Li Y.J., Wu T.J. Application of multi-spectral information fusion for water quality analysis. Chinese Journal of Analytical Chemistry 2007, 12:1716-1720.
40. Zemel R., Pitassi T. A gradient-based boosting algorithm for regression problems. Advances in Neural Information Processing Systems 2001, vol. 13. MIT Press, Cambridge, MA.
41. Zhang M.H., Xu Q.S., Massart D.L. Boosting partial least squares. Analytical Chemistry 2005, 77:1423-1431.
42. Zhou Y.P., Cai C.B., Huan S., Jiang J.H., Wu H.L., Shen G.L., Yu R.Q. QSAR study of angiotensin II antagonists using robust boosting partial least squares regression. Analytica Chimica Acta 2007, 593:68-74.