Application of Evidence Theory to Automate The Process of Removing Toxic Chromium Ions in Wastewater

  • Mohd Marzuki Mustafa Department of Electrical, Electronic Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia
  • Siti Rozaimah Syeikh Abdullah Department of Electrical, Electronic Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia
  • Rakmi Abdul Rahman Department of Chemical and Process Engineering, University Kebangsaan Malaysia
Keywords: Chromium removal process, Dempster-Shafer's evidence theory, evidence aggregation, information fusion, oxygen reduction potential (CRP)

Abstract

A new method to automate the batch process of removing toxic chromium ions from wastewater using Oempster-Shafer's (OS) evidence theory is described. The removal of the toxic chromium ions from wastewater is a good example of a process where conventional output or state feedback controllers cannot be simply applied because the concentration of the ion cannot be easily measured online or estimated from other measured parameters. The batch process of removing toxic chromium ions by adding a reducing agent involves reduction and oxidation (redox) reactions which are usually monitored using the oxidation reduction potential (ORP) probe. However, the relationship between ORP and concentration of chromium ions is difficult to establish, hence, a reliable online control is seldom achieved using output feedback control. The approach here is to treat the sequence of ORP values obtained at each sample interval as partial evidences with different degrees of belief to indicate whether the removal process has been completed or not. Using OS's theory of evidence these partial evidences are fused or aggregated to give a more reliable and robust real time control decision. In this paper, a modification is proposed to overcome deficiencies in the OS's combination rule in combining sequences of evidence from the same source. The algorithm based on this evidence theory has been tested in the laboratory, and the results obtained show that the algorithm is robust with respect to noise and process variation.

References

1. Belloir, F, and Billat, A. (2000). “Improvements of pattern recognition by using evidence theory. Application to tag identification," Proceedings of the 3rd International Conference on Information Fusion, TuB5-3-8.
2. Campbell, H. J., Scrivner, N. C., Batzarn, K., and White, R. F. (1978). “Evaluation of chromium removal from highly variable wastewater stream," Proceedings of 32nd Purdue Industrial Waste Conference, 102-115.
3. Carlson, A. B. (1975). Communication Systems, 2nd ed., McGraw-Hill, Tokyo.
4. Chao, J-J, Shao, K-C. and Jang, L-W. (1996). "Uncertain information fusion using belief measure and its application to signal classification," Proceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Intergration for Intelligent Systems, 151-157.
5. Crisafulli, S., and Medhurst, T. P. (1993). "Robust on-line digital differentiation with an application to underground coal mining," Proceeding of IFAC 12th Triennial World Congress, 631-634.
6. Goodridge, S. G., Luo, R. C., and Kay, M. G. (1994). "Multi-layered fuzzy behaviour fusion for real-time control of systems with Many Sensors," Proceedings of the 1994 IEEE International Conference on Multisensor Fusion and Integration for Intelligent System, 272-279.
7. Gumustekin, S., and Hall, R. W. (1996). "A selective vision algorithm based on aggregation of fuzzy evidence," Proceedings of the 1996 Biennial Conference of the Fuzzy Information Processing Society, 33–37.
8. Hau, H-Y, and Kashyap, R. L. (1989). "On the robustness of Dempster's rule of combination," Proceedings of IEEE International Workshop on Tools for Artificial Intelligence: Architectures, Languages and Algorithms, 578–582.
9. Hong, L. (1992). "Recursive temporal-spatial information fusion," Proceedings of the 31s Conference on Decision and Control, 3510–3511.
10. King, P. J., and Mamdani, E. H. (1977). "The application of fuzzy control systems to industrial processes," Automatica, 12, 235.
11. Klir, G. J., and Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall, New Jersey.
12. Loskiewicz-Buczak, A., and Uhrig, R. E. (1993). "Aggregation of evidence by fuzzy set operations for vibration monitoring," Proceedings of 3rd International Conference on Industrial Fuzzy Control and Intelligent System , 204-209.
13. Mahler, R. (1996). "Nonadditive probability, finite set statistics, and information fusion, Proceedings of the 34th Conference on Decision & Control, 1947-1952.
14. Maurius, G., Lasserre, V., and Foulloy, L. (2000). “Fuzzy modelling of measurement data acquired from physical sensors," IEEE Transactions on Instrumentation and Measurement, 49, 6, 1201-1205.
15. Mirhosseini, A. R., Yan, H., Chen, M., and Pham, T. (1998). "Human face recognition: A minimal evidence approach," Proceedings of the 6th International Conference on Computer Vision, 652-659.
16. Murphy, C.K. (1999). "Combining belief functions when evidence conflicts," Decision Support Systems, 29, 1-9.
17. Murphy, R. R. (1996). "Adaptive rule of combination for observations over time," Proceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, 125-131.
18. Mustafa, M. M., Rozaimah, S. A. S., and Rakmi, A. R. (2002). "Robust on-line control of hexavalent chromium reduction process using Kalman filter," J. of Process Control, 2, 405-412.
19. Pal, N. R., and Ghosh, S. (2001). "Some classification algorithms integrating Dempster-Shafer theory of evidence with the rank nearest neighbor rules," IEEE Transactions on Systems, Man, and Cybernatics-Part A: Systems and Applications, 31, 1, 59-66.
20. Qingdong, D., Lingyu, X., and Hai, Z. (2000). "D-S evidence theory applied to fault diagnosis of generator based on embedded sensor," Proceedings of 3rd International Conference on Information Fusion, TuD5-3-8.
21. Rozaimah S.A.S. Rakmi. A. R. Mustafa, M. M., Abu Bakar, M., and Amir, A. H. K. (2000). "Real-time control of hexavalent chromium reduction using ORP," Proceedings of the 14th Symposium of Malaysian Chemical Engineers, 533–542.
22. Rozaimah, S.: A. S. (1999). "Study of physicochemical treatment process of wastewater and its control via fuzzy logic and neural network," PhD Thesis, Universiti Kebangsaan Malaysia.
23. Russo, F. (1994). “A totally fuzzy approach to multisensor instrumentation: Estimation through data fusion," Proceedings of IEEE Instrumentation and Measurement Technology Conference, 82–85.
24. Shafer, G. (1976). A mathematical theory of evidence, Princeton University Press, Princeton, New Jersey.
25. Tahani, H., and Keller, J. M. (1990). "Information fusion in computer vision using the fuzzy integral," IEEE Transactions on Systems, Man, and Cybernatics," 20, 3, 733-741.
26. Tong, S., Shen, Y., and Liu, Z. (2000). “Data fusion using fuzzy measures and genetic algorithms," Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference, 2.
27. van der Wal, A. J., and Shao, M. (2000). "Sensor
fusion: The application of softcomputing in monitoring and control for railroad maintenance," Proceedings of the 3rd World Congress on Intelligent Control and Automation, 341-346.
28. Verikas, A., Malmqvist, K., and Bacauskiene, M. (2000). "Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images," Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 297-302.
29. Wang, X.-G., Qian, W.-H., Pagello, E., and Pei, R.-Q. (1996). "On the uncertainty and ignorance of statistical decision and evidence combination," Proceeding of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Intergration for Intelligent Systems, 166-173.
30. Wareham, D. G., Hall, K, J., and Mavinic, D. S. (1993). “Real-time control of wastewater treatment systems using ORP," Wat. Sci. Technol., 28, 11, 273–282.
31. Wu, Y. G., Yang, Y. Y., and Liu, K. (1996). "Obstacle detection and environment modeling based on multisensor fusion for robot navigation," Artificial Intelligence in Engineering, 10, 4, 323-323.
32. Yager, R. R. (1995). "Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory," IEEE Transactions on Systems, Man, and Cybernatics, Vol. 25(8), 1221-1230.
Published
2005-12-31
How to Cite
Mustafa, M. M., Syeikh Abdullah, S. R., & Rahman, R. A. (2005). Application of Evidence Theory to Automate The Process of Removing Toxic Chromium Ions in Wastewater. ASEAN Journal of Chemical Engineering, 5(1), 84-98. Retrieved from https://jurnal.ugm.ac.id/v3/AJChE/article/view/7638
Issue
Vol 5 No 1 (2005)
Section
Articles

Copyright holder for articles is ASEAN Journal of Chemical Engineering. Articles published in ASEAN J. Chem. Eng. are distributed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. 

Authors agree to transfer all copyright rights in and to the above work to the ASEAN Journal of Chemical Engineering Editorial Board so that the Editorial Board shall have the right to publish the work for non-profit use in any media or form. In return, authors retain: (1) all proprietary rights other than copyright; (2) re-use of all or part of the above paper in their other work; (3) right to reproduce or authorize others to reproduce the above paper for authors’ personal use or for company use if the source and the journal copyright notice is indicated, and if the reproduction is not made for the purpose of sale.