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Abstract

This paper reports on the design, implementation and real-time operation of advanced process monitoring techniques for wastewater treatment plants. The paper presents the development of a software platform and its implementation in a full-scale wastewater treatment plant. The software platform allows the real-time execution of advanced process monitoring techniques that are used for fault and process upset detection and identification. The statistical methods are part of a supervisory control level that allows for integration of the different facets of process monitoring and control, including fault detection, knowledge extraction and controller action. Results obtained by the real-time execution of such algorithms are presented.

Keywords

process monitoring real-time control recursive Principal Component Analysis wastewater treatment

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References

Brdys, M. A. , Grochowski, M. , Duzinkiewicz, K. and Chotkowski, W. 2002: Design of control structure for integrated wastewater treatment plant-sewer systems. In: Proceedings of the 1st Scientific & Technical Conference on Technology, Automation and Control of Wastewater and Drinking Water Systems. Gdansk, 21-40.

Chen, Q. , Kruger, U. , Meronk, M. and Leung, A. Y. T. 2004: Synthesis of T2 and Q statistics for process monitoring. Control Engineering Practice, 12, 745-755.

Chiang, L. H. , Russell, E. L. and Braatz, R. D. 2001: Fault detection and diagnosis in industrial systems. Berlin: Springer.

Duzinkiewicz, K. (Ed.) 2002: Deliverable 6: Algorithms for system monitoring. SMAC - Smart Control of Wastewater Treatment Systems. Report held at Technical University of Denmark, Denmark.

Golub, G. H. and Van Loan, C. F. 1983: Matrix computations. Oxford: North Oxford Academic.

Gonnet, G. 2002: Scientific computation ws 2001/2002: singular value decomposition and eigenvalue decomposition. Web. linneus20.ethz.ch:8080/2_2_1.html. (last accessed December 2004).

Jolliffe, I. 1986: Principal Component Analysis. New York: Springer-Verlag.

Li, W. , Yue, H. , Valle-Cervantes, S. and Qin, S. J. 2000: Recursive PCA for adaptive process monitoring. Journal of Process Control 10, 471-486.

Nielsen, M. K. , (Ed.) 2002: Deliverable 8: Knowledge extraction. SMAC - Smart Control of Wastewater Treatment Systems. Report held at: Technical University of Denmark, Denmark.

10.

Rosén, C. 2001: A chemometric approach to process monitoring and control. PhD thesis, Department of Industrial Electrical Engineering, Lund University.

11.

Rosén, C. and Lennox, J. A. 2001: Multivariate and multiscale monitoring of wastewater treatment operation. Water Research 35, 3402-3410.

12.

Rosénchez, A. (Ed.) 2004: Deliverable 13: Algorithm and software report. SMAC - Smart Control of Wastewater Treatment Systems. Report held at Technical University of Denmark, Denmark.

13.

Sánchez, A. , Katebi, M. R. and Johnson, M. A. 2003: Design and implementation of a real-time control platform for the testing of advanced control systems and data quality management in the wastewater industry. In: Proceedings of the 4th International Conference on Control and Automation. Montreal, Canada.

14.

Savic, D. A. , Davidson, J. W. and Davis, R. B. 1999: Data mining and knowledge discovery for the water industry. In: Water industry systems: modelling & optimisation applications. Research Studies Press.

15.

Wade, M.J. 2004: Process monitoring and knowledge extraction in wastewater treatment plants. PhD thesis. University of Strathclyde, Industrial Control Centre, Graham Hills Building, Glasgow.

16.

Yue, H. H. and Qin, S. J. 2001: Reconstruction-based fault identification using a combined index. Industrial & Engineering Chemistry Research 40, 4403-4414.

On real-time control and process monitoring of wastewater treatment plants: real-time process monitoring

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