Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

Process Biochemistry

Volumn 43, Issue 10, 2008, Pages 1107-1113

  Lee, Min Woo ^a   Hong, Sung Hun ^a   Choi, Hyeoksun ^a   Kim, Ji Hoon ^b   Lee, Dae Sung ^b   Park, Jong Moon ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b Kyungpook National University   (South Korea)

Author keywords

Multivariate statistical process control; Neural network; Principal component analysis; Remote monitoring; Software sensor; Wastewater treatment plant

Indexed keywords

BIOLOGICAL WATER TREATMENT; CHEMICAL OXYGEN DEMAND; CONCURRENCY CONTROL; CONTROL THEORY; MONITORING; NEURAL NETWORKS; NITROGEN; NONMETALS; ORGANIC COMPOUNDS; PHOSPHORUS; PROCESS CONTROL; PROCESS ENGINEERING; PRODUCTION CONTROL; QUALITY CONTROL; REAL TIME SYSTEMS; REMOTE CONTROL; RURAL AREAS; SENSOR NETWORKS; SENSORS; SEWAGE PUMPING PLANTS; STATISTICAL METHODS; STATISTICAL PROCESS CONTROL; SURFACE TREATMENT; WASTEWATER RECLAMATION; WATER SUPPLY; WATER TREATMENT; WATER TREATMENT PLANTS;

BIOLOGICAL WASTEWATER TREATMENT PLANTS; CONTROL CENTERS; LOCAL OPERATORS; MULTI-SENSOR SYSTEMS; MULTIVARIATE STATISTICAL PROCESS CONTROL; MULTIVARIATE STATISTICAL PROCESSES; NETWORK-BASED SOFTWARE; NEURAL NETWORK; OPTIMUM OPERATIONAL; ORGANIC MATTER; PLANT SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; REMOTE MONITORING; REMOTE MONITORING SYSTEMS; RUNNING COSTS; SOFTWARE SENSOR; SOFTWARE SENSORS; WASTE WATER SYSTEMS; WASTE WATER TREATMENT PLANTS; WASTEWATER TREATMENT PLANT;

WASTEWATER TREATMENT;

EID: 50449095583     PISSN: 13595113     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.procbio.2008.06.002     Document Type: Article

References (22)

1. Lardon L, Punal A, Steyer J-P, Roca E, Lema J, Lambert S, Ratini P, Frattesi S, Bernard O. Specifications of modular internet-based remote supervision systems for wastewater treatment plants. In: Proceedings of the 15th biennial european conference on artificial intelligence, workshop 14: binding environmental sciences and artificial intelligence, France. 2002, 5.1.
2. Lee D.S., Jeon C.O., Park J.M., and Chang K.S. Hybrid neural network modelling of a full-scale industrial wastewater treatment process. Biotechnol Bioeng 78 (2002) 670-682
3. Lee D.S., and Vanrolleghem P.A. Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. Biotechnol Bioeng 82 (2002) 489-497
4. Wise B.M., and Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. J Process Control 6 (1996) 329-348
5. Lee D.S., Lee M.W., Woo S.H., Kim Y.-J., and Park J.M. Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant. Process Biochem 41 9 (2006) 2050-2057
6. Lee D.S., Vanrolleghem P.A., and Park J.M. Adaptive multiscale principal component analysis. J Biotechnol 116 (2005) 195-210
7. Li W., Yue H., Valle-Cervants S., and Qin S.J. Recursive PCA for adaptive process monitoring. J Process Control 10 (2000) 471-486
8. Vanrolleghem P.A., and Lee D.S. On-line monitoring equipment for wastewater treatment processes: state of the art. Water Sci Technol 47 2 (2003) 1-34
9. Bhat N., and McAvoy T.J. Use of neural nets for dynamic modeling and control of chemical process system. Comput Chem Eng 14 (1990) 573-583
10. Lee D.S., Vanrolleghem P.A., and Park J.M. Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant. J Biotechnol 115 (2005) 317-328
11. Zhang Q., Reid J.F., Litchfield J.B., Ren J., and Chang S.-W. A prototype neural network supervised control system for Bacillus thuringiensis fermentations. Biotechnol Bioeng 43 (1994) 483-489
12. Pai T.Y., Chuang S.H., Ho H.H., Yu L.F., Su H.C., and Hu H.C. Predicting performance of grey and neural network in industrial effluent using online monitoring parameters. Process Biochem 43 2 (2008) 199-205
13. Çinar O. New tool for evaluation of performance of wastewater treatment plant: artificial neural network. Process Biochem 40 9 (2005) 2980-2984
14. Lee D.S., and Park J.M. Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor. J Biotechnol 75 (1999) 229-239
15. Linko S., Luopa J., and Zhu Y.-H. Neural networks as 'software sensors' in enzyme production. J Biotechnol 52 (1997) 257-266
16. Montague G.A., Morris A.J., and Tham M.T. Enhancing bioprocess operability with generic software sensors. J Biotechnol 25 (1992) 183-201
17. American Public Health Association/American Water Works Association/Water Environment Federation. Standard methods for the examination of water and wastewater. 19th ed. Washington, DC:APHA; 1995.
18. Lennox B., Montague G.A., Hiden H.G., Kornfeld G., and Goulding P.R. Process monitoring of an industrial fed-batch fermentation. Biotechnol Bioeng 74 (2001) 125-135
19. Baffi G., Martin E.B., and Morris A.J. Non-linear dynamic projection to latent structures modeling. Chemom Intell Lab Syst 52 (2000) 5-22
20. Qin S.J., and McAvoy T.J. Nonlinear FIR modeling via a neural net PLS approach. Comput Chem Eng 20 (1996) 147-159
21. Dunia D., Qin J., Edgar T.F., and McAvoy T.J. Identification of faulty sensors using principal component analysis. AIChE J 42 (1992) 2797-2812
22. Krzanowshi W.J. Cross-validation in principal component analysis. Biometrics 43 (1987) 575-584