Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

IEEE Transactions on Fuzzy Systems

Volumn 23, Issue 5, 2015, Pages 1761-1776

  Dovžan, Dejan ^a   Logar, Vito ^a   Škrjanc, Igor ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Adaptation of Fuzzy Model; eFuMo; Evolving Fuzzy Model; Evolving Mechanisms; Fault Detection; Soft sensor; Waste Water Treatment Plant; WWTP

Indexed keywords

EFFLUENT TREATMENT; EFFLUENTS; ENERGY CONSERVATION; QUALITY CONTROL; WASTE TREATMENT; WASTEWATER; WASTEWATER TREATMENT; WATER QUALITY; WATER TREATMENT; WATER TREATMENT PLANTS;

EFUMO; EVOLVING FUZZY MODELS; FUZZY MODELING; SOFT SENSORS; WWTP;

FAULT DETECTION;

EID: 84975318439     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2014.2379252     Document Type: Article

References (65)

1. F. Baggiani and S. Marsili-Libelli, "Real-time fault detection and isolation in biological wastewater treatment plants," Water Sci. Technol., vol. 60, no. 11, pp. 2949-2961, 2009.
2. R. Isermann, Fault-Diagnosis Systems: An Introduction From Fault Detection to Fault Tolerance. Heidelberg, Germany: Springer-Verlag, 2006.
3. D. Vrečko, N. Hvala, and M. Stražar, "The application ofmodel predictive control of ammonia nitrogen in an activated sludge process." Water Sci. Technol., vol. 64, no. 5, pp. 1115-1121, 2011.
4. 5 soft sensor design using local learning," in Proc. Int. Conf. Comput. Appl. Syst. Modeling, Taiyuan, China, Oct. 22-24, 2010, vol. 3, pp. 584-589.
5. G. Bavdazž, N. Hvala, J. Kocijan, and D. Juričič, "Nelinearni algoritem za estimacijo stanj in identifikacijo parametrov šaržnega biološkega procesa," Elektrotehniški vestnik, vol. 68, pp. 57-63, 2001.
6. C. K. Yoo and I.-B. Lee, "Soft sensor and adaptive model-based dissolved oxygen control for biological wastewater treatment processes," Environ. Eng. Sci., vol. 21, no. 3, pp. 331-340, 2004.
7. H. Ye, F. Luo, and Y. Xu, "Dissolved oxygen generic model control of wastewater treatment process based on immune optimization least squares support vectormachine," in Proc. 8th World Congr. Intell. Control Autom., 2010, pp. 5082-5086.
8. U. Holmberg, G. Olsson, and B. Andersson, "Simultaneous DO control and respiration estimation," Water Sci. Technol., vol. 21, pp. 1185-1195, 1989.
9. C. F. Lindberg, "Control and estimation strategies applied to the activated sludge process," Ph.D. dissertation, Dept. Mater. Sci. Syst. Control Group, Uppsala Univ., Stockholm, Sweden, 1997.
10. Y. Kim, H. Bae, K. Poo, J. Kim, T. Moon, S. Kim, and C. Kim, "Soft sensor using PNN model and rule base for wastewater treatment plant," in Advances in Neural Networks (Lecture Notes in Computer Science Series). Berlin, Germany: Springer, 2006, vol. 3973, pp. 1261-1269.
11. D. Cecil and M. Kozlowska, "Software sensors are a real alternative to true sensors," Environ. Modelling Softw., vol. 25, pp. 622-625, 2010.
12. D.-J. Choi and H. Park, "A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process," Water Res., vol. 35, no. 16, pp. 3959-3967, 2001.
13. M. W. Lee, S. H. Hong, H. Choi, J.-H. Kim, D. S. Lee, and J. M. Park, "Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors," Process Biochem., vol. 43, no. 10, pp. 1107-1113, 2008.
14. V. Alcaraz-Gonzales, J. Harmand, A. Rapaport, and J. P. Steyer, "Software sensors for highly uncertain WWTPs: A new approach based on interval observers," Water Res., vol. 36, pp. 2515-2524, 2002.
15. E. P. Tao, W. H. Shen, T. L. Liu, and X. Q. Chen, "Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process," Chemometrics Intell. Lab. Syst., vol. 128, pp. 49-55, 2013.
16. M. J. Fuente, D. Garcia-Alvarez, G. I. Sainz-Palmero, and P. Vega, "Fault detection in a wastewater treatment plant based on neural networks and PCA," in Proc. 20th Mediterranean Conf. Control Autom., Jul. 2012, pp. 758-763.
17. H. Haimi, M. Mulas, F. Corona, and R. Vahala, "Data-derived soft-sensors for biological wastewater treatment plants: An overview," Environ. Modelling Softw., vol. 47, pp. 88-107, 2013.
18. S. X. Ding, Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools (Advances in Industrial Control Series), 2nd ed. Berlin, Germany: Springer, 2013.
19. J. Korbicz, J. M. Koscielny, Z. Kowalczuk, and W. Cholewa, Fault Diagnosis-Models, Artificial Intelligence and Applications. Berlin, Germany: Springer Verlag, 2004.
20. J. Shing and R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665-685, May/Jun. 1993.
21. M. F. Azeem, H. Hanmandlu, and N. Ahmad, "Structure identification of generalized adaptive neuro-fuzzy inference systems," IEEE Trans. Fuzzy Syst., vol. 11, no. 5, pp. 666-681, Oct. 2003.
22. D. Dovžan and I. Škrjanc, "Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes," ISA Trans., vol. 50, no. 2, pp. 159-169, 2011.
23. D. Dovžan and I. Škrjanc, "Recursive clustering based on a Gustafson-Kessel algorithm," Evolving Syst., vol. 2, no. 1, pp. 15-24, 2011.
24. J. Platt, "A resource allocating network for function interpolation," Neural Comput., vol. 3, no. 2, pp. 213-225, 1991.
25. C. F. Juang and C. T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol. 6, no. 1, pp. 12-32, Feb. 1998.
26. F. J. Lin, C. H. Lin, and P.H. Shen, "Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive," IEEE Trans. Fuzzy Syst., vol. 9, no. 5, pp. 751-759, Oct. 2001.
27. S. G. Tzafestas and K. C. Zikidis, "NeuroFAST: On-line neurofuzzy ART-based structure and parameter learning TSK model," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 31, no. 5, pp. 797-802, Oct. 2001.
28. N. K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, Apr. 2002.
29. P. P. Angelov and D. P. Filev, "An approach to on-line identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 484-497, Feb. 2004.
30. E. Lughofer and E. P. Klement, "FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems," in Proc. IEEE Int. Conf. Fuzzy Syst., Reno, NV, USA, May 22-25, 2005, pp. 915-920.
31. M. Pratama, S. G. Anavatti, P. Angelov, and E. Lughofer, "PANFIS: A novel incremental learning machine," IEEE Trans. Neural Netw. Learning Syst., vol. 25, no. 1, pp. 55-68, Jan. 2014.
32. H. J. Rong, N. Sundararajan, G. B. Huang, and P. Saratchandran, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction," Fuzzy Sets Syst., vol. 157, no. 9, pp. 1260-1275, 2006.
33. G. Leng, G. Prasad, and T. M. McGinnity, "An on-line algorithm for creating self-organizing fuzzy neural networks," Neural Netw., vol. 17, pp. 1477-1493, 2004.
34. G.-B. Huang, P. Saratchandran, and N. Sundararajan, "Arecursive growing and pruning RBF (GAP-RBF) algorithm for function approximations," in Proc. 4th Int. Conf. Control Autom., Montreal, QC, Canada, Jun. 2003, pp. 10-12.
35. N. K. Kasabov, "Evolving fuzzy neural networks for supervised/unsupervised on-line knowledge-based learning," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 31, no. 6, pp. 902-918, Dec. 2001.
36. N. Kasabov, "Evolving fuzzy neural networks - Algorithms, applications and biological motivation," in Proc. Methodologies Conceptation, Des. Appl. Soft Comput., 1998, pp. 271-274.
37. S. Wu and M. J. Er, "Dynamic fuzzy neural networks: A novel approach to function approximation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 30, no. 2, pp. 358-364, Apr. 2000.
38. S. Wu, M. J. Er, and Y. Gao, "A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks," IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 578-594, Aug. 2001.
39. H. Soleimani-B, C. Lucas, and B. N. Araabi, "Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling," Evolving Syst., vol. 1, no. 1, pp. 59-71, 2010.
40. P. Angelov, "Evolving Takagi-Sugeno fuzzy systems from streaming data (eTS+)," in Evolving Intelligent Systems: Methodology and Applications. Hoboken, NJ, USA: Wiley, 2010, pp. 21-50.
41. E. Lughofer, "Flexible evolving fuzzy inference systems from data streams (FLEXFIS++)," in Learning in Non-Stationary Environments: Methods and Applications. New York, NY, USA: Springer, pp. 205-245.
42. A. Kalhor, B. Araabi, and C. Lucas, "An online predictormodel as adaptive habitually linear and transiently nonlinear model," Evolving Syst., vol. 1, no. 1, pp. 29-41, 2010.
43. J. Rubio, "SOFMLS: Online self-organizing fuzzy modified least-squares network," IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1296-1309, Dec. 2009.
44. E. Lughofer, "On-line assurance of interpretability criteria in evolving fuzzy systems - achievements, new concepts and open issues," Inf. Sci., vol. 251, pp. 22-46, 2013.
45. E. Lughofer, Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications (Studies in Fuzziness and Soft Computing series), vol. 266. New York, NY, USA: Springer, 2011.
46. I. Rodrigez-Roda, M. Sanchez-Marre, J. Comas, J. Baeza, J. Colprim, J. Lafuente, U. Cortes, and M. Poch, "A hybrid supervisory system to support WWTP operation: Implementation and validation," Water Sci. Technol., vol. 45, nos. 4/5, pp. 289-297, 2002.
47. C. L. Hwang and L. J. Chang, "Fuzzy neural-based control for nonlinear time-varying delay systems," IEEE Trans. Syst. Man Cybern. B, Cybern., vol. 37, no. 6, pp. 1471-1485, Dec. 2007.
48. D. Dovžan, V. Logar, and I. Škrjanc, "Solving the sales prediction problem with fuzzy evolving methods," in Proc. IEEE Congr. Evolutionary Comput., Brisbane, Australia, Jun. 2012, pp. 1-8.
49. O. Nelles, "Local linear model tree for on-line identification of time invariant nonlinear dynamic systems," in Proc. Int. Conf. Artif. Neural Netw., Bochum, Germany, 1996, pp. 115-120.
50. J. de Jesús Rubio, "Stability analysis for an online evolving neuro-fuzzy recurrent network," in Evolving Intelligent Systems Methodology and Applications. Hoboken, NJ, USA: Wiley, 2010, pp. 173-199.
51. L. Hartert, M. S. Mouchaweh, and P. Billaudel, "A semi-supervised dynamic version of fuzzy k-nearest neighbours tomonitor evolving systems," Evolving Syst., vol. 1, pp. 3-15, 2010.
52. I. Škrjanc, B. Hartmann, O. Banfer, O. Nelles, A. Sodja, and L. Teslič, "Supervised hierarchical clustering in fuzzy model identification," IEEE Trans. Fuzzy Syst., vol. 19, no. 6, pp. 1163-1176, Dec. 2011.
53. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA, USA: Kluwer, 1981.
54. D. Dovžan and I. Škrjanc, "Recursive fuzzy model identification," in Proc. Advances Comput. Sci. Eng., Sharm El Sheikh, Egypt, Mar. 15-17, 2010, pp. 1-6.
55. E. Lughofer and P. Angelov, "Handling drifts and shifts in on-line data streams with evolving fuzzy systems," Appl. Soft Comput., vol. 11, no. 2, pp. 2057-2068, 2011.
56. K. J. strom and B. Wittenmark, Adaptive Control, 2nd ed. Reading, MA, USA: Addison-Wesley, 1995.
57. S. W. Tung, C. Quek, and C. Guan, "SaFIN: A self-adaptive fuzzy inference network," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 1928-1940, Dec. 2011.
58. R. Rosa, F. Gomide, D. Dovžan, and I. Škrjanc, "Evolving neural network with extreme learning for system modeling," in Proc. IEEE Conf. Evolving Adaptive Intell. Syst., Linz, Austria, Jun. 2-4w, 2014, pp. 11-34.
59. V. Kadirkamanathan and M. Niranjan, "A function estimation approach to sequential learning with neural networks," Neural Comput., vol. 5, no. 6, pp. 954-975, 1993.
60. L. Yingwei, N. Sundararajan, and P. Saratchandran, "A sequential learning scheme for function approximation using minimal radial basis function (RBF) neural networks," Neural Comput., vol. 9, pp. 461-478, 1997.
61. P. Angelov and D. Filev, "Simpl-eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models," in Proc. IEEE Int. Conf. Fuzzy Syst., Reno, NV, USA, May 22-25, 2005, pp. 1068-1073.
62. P. Angelov and R. Yager, "A new type of simplified fuzzy rule-based system," Int. J. General Syst., vol. 41, no. 2, pp. 163-185, 2011.
63. G. Huang, Q. Zhu, and C. Siew, "Extreme learning machine: A new learning scheme of feedforward neural networks," in Proc. IEEE Int. Joint Conf. Neural Netw., 2004, pp. 985-990.
64. R. Rosa, R. Ballini, and F. Gomide, "Evolving hybrid neural fuzzy network for system modeling and time series forecasting," in Proc. IEEE Int. Conf. Mach. Learning Appl., Miami, FL, USA, Dec. 4-7, 2013, pp. 378-383.
65. P. Angelov and Z. Xiaowei, "Evolving fuzzy systems from data streams in real-time," in Proc. Int. Symp. Evolving Fuzzy Syst., 2006, pp. 29-35.