Skip to main content
SpringerLink
Log in

Concept Formation in WWTP by Means of Classification Techniques: A Compared Study

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Although activated sludge process is a very widely used biologicalprocess in wastewater treatment plants (WWTP), and there areproperly functioning control loops such as that of dissolved oxygen,in practice, this type of plant requires a major time investment onthe part of the operator, involving many manual operations.Treatment plants work well most of the time, as long as there are not unforeseen occurrences. Normal operatingsituations (generally similar to design conditions) can be treatedmathematically by using efficient control algorithms. However, there aresituations in which the control system cannot properlymanage the plant, and in which the process can only be efficiently managedthanks to the operator‘s experience. This is a case in which aknowledge-based system may be useful. One of the difficulties inherent tothe development of a knowledge-based system is to obtain the knowledge base(i.e., knowledge acquisition), specially whendealing with a wide, complicated and ill-structured)field.

Among the aims of this work arethose to show how semi-automatic knowledge acquisition tools could helphuman experts to organize their knowledge about their domain and also, tocompare the power of different approaches of knowledge acquisition) to the same database.

In this paper are presented the results obtained fromapplying two different classification techniques to the development of knowledge-bases for the management of an activated sludge process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. A. Aamodt, “Towards robust expert systems that learn from experience. An architectural framework,” Proc. of 3rd European Knowledge Acquisition for Knowledge-Based Systems Workshop (EKAW-89), Paris, 1989, pp. 311–326.

  2. R. Bañares-Alcántara and J.W. Ponton, “Artificial intelligence techniques in chemical engineering process design,” Applications in Artificial Intelligence in Engineering, Computational Mechanics Publications, Bath, England, vol. II, pp. 581–607, 1992.

    Google Scholar 

  3. B. Becker, “The limits of knowledge acquisition,” Report FB-14. Dortmund University, 1987.

  4. J. Béjar, “Knowledge acquisition in ill-structured domains,” Ph.D. Thesis, Dept. de Llenguatges i Sistemes Informàtics, Universitat Politècenica de Catalunya, 1995.

  5. J. Béjar, U. Cortés, and M. Poch, “Linneo+: A classification methodology for ill-structured domains,” Research Report LSI-93-22-R. Dept. de Llenguatges i Sistemes Informatics, Universitat Politècnica de Catalunya. Submitted for publication, 1993.

  6. J. Béjar, U. Cortés, and M. Domingo, “Using domain theory to bias classification processes in Ill-domains,” Proc. of Sistemi multimeddiali intelligenti—Multimedia e beni culturali—Multimedia e formazioni. Ravello, Italy, Sept. 1994.

  7. Ll. Belanche and U. Cortés, “The nought attributes in knowledge-based systems,” Proc. of European Workshop on Verification & Validation (EUROVAV-91), Jesus College. Cambridge, UK, 1991, pp. 77–103.

    Google Scholar 

  8. Ll. Belanche, M. Sànchez, U. Cortés, and P. Serra, “A knowledge-based system for the diagnosis of waste-water treatment plants,” Proc. of 5th Int. Conf. on Industrial & Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-92). LNAI-604, Paderborn, 1992, pp. 324–336.

  9. L.D. Benefield and C.W. Randall, Biological Process Design for wastewater Treatment. Prentice-Hall: N.J. 1980.

    Google Scholar 

  10. A.G. Capodaglio, H.V. Jones, V. Novotny, and X. Feng, “Sludge bulking analysis and forecasting: application of system identification and artificial neural computing technologies,” Water Research, vol. 25,no. 10, pp. 1217–1224, 1991.

    Google Scholar 

  11. P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman, “AUTOCLASS: A Bayesian classification system,” Proc. of 5th Int. Conference on Machine Learning (ICML-1988), Ann Arbor, MI, 1988, pp. 54–64.

  12. J.J. Coleman, S. Krovvidy, R. Scott Summers, and W.G. Wee, “An AI approach for wastewater treatment systems,” Applied Intelligence, vol. 1,no. 3, pp. 247–261, Kluwer Academic Publishers, 1991.

  13. D. Couillard and S. Zhu, “Control strategy for the activated sludge process under shock loading,” Water Research, vol. 26,no. 5, pp. 649–655, 1992.

    Google Scholar 

  14. E. Czoagala and T. Rawlik, “Modelling of a fuzzy controller with application to the control of biological processes,” Fuzzy Sets and Systems, vol. 31, pp. 13–22, 1989.

    Google Scholar 

  15. A.P. Danyluk, “The use of explanations for similarity-based learning,” Proc. of International Joint Conference on Artificial Intelligence (IJCAI-7), 1987, pp. 274–276.

  16. T.G. Dietterich and R.S. Michalsky, “Inductive learning of structural descriptions: evaluation criteria and comparative review of selected methods,” Artificial Intelligence, vol. 16, pp. 257–294, 1981.

    Google Scholar 

  17. R. Dubes and A. Jain, Algorithms for Clustering Data, Prentice-Hall: Englewood Cliffs, USA, 1990.

    Google Scholar 

  18. F.E. Finch, O.O. Oyeleye, and M.A. Kramer, “A robust event-oriented methodology for diagnosis of dynamic process systems,” Computers & Chemical Engineering, vol. 14,no. 12, pp. 1379–1396, 1990.

    Google Scholar 

  19. D. Fisher, “Knowledge acquisition via incremental conceptual clustering,” Machine Learning, vol. 2, pp. 139–172, 1987.

    Google Scholar 

  20. D.H. Fisher and M.J. Pazzani, “Computational models of concept learning,” in Concept Formation: Knowledge and Experience in Unsupervised Learning, edited by D.H. Fisher, M.J. Pazzani, and P. Langley, Morgan Kaufmann: San Mateo, CA, 1991.

    Google Scholar 

  21. R. Gall and G. Patry, “Knowledge-based system for the diagnosis of an activated sludge plant,” in Dynamic Modelling and Expert Systems in Wastewater Engineering, edited by G. Patry and D. Chapman, Lewis Publishers: Chelsea, MI, 1989.

    Google Scholar 

  22. J.H. Gennari, P. Langley, and D. Fisher, “Model of Incremental Concept Formation,” Artificial Intelligence, vol. 40,nos. 1–3, pp. 11–61, 1989.

    Google Scholar 

  23. J. de Gràcia, “Evaluation of classification techniques for bioprocesses management: An application to an activated sludge reactor,” Master's Thesis. Dept. de Quimica, Universitat Autònoma de Barcelona, 1993.

  24. S.T. Hanson and M. Bauer, “Conceptual clustering, categorization and polymorphy,” Machine Learning, vol. 1, pp. 343–372, 1986.

    Google Scholar 

  25. F. Hayes-Roth and J. McDermott, “Knowledge acquisition from structural descriptions,” Proc. of the Int. Joint Conference on Artificial Intelligence (IJCAI-77), Cambridge, MA, 1977, pp. 356–362.

  26. N.J. Horan and C.R. Eccles, “The potential for expert systems in the operation and control of activated sludge plants,” Process Biochemistry, pp. 81–85, June, 1986.

  27. Y.L. Huang, G. Sundar, and L.T. Fan, “Min-cyanide: an expert system for cyanide waste minimization in electroplating plants.” Environmental Progress, vol. 10,no. 2, pp. 89–95, 1991.

    Google Scholar 

  28. M. Ke and M. Ali, “Induction in database systems: A bibliography,” Applied Intelligence, vol. 1, pp. 263–270, 1991.

    Google Scholar 

  29. I. Kononenko and I. Bratko, “Information-based evaluation criterion for classifiers' Performance,” Machine Learning, vol. 6,no. 1, pp. 67–80, 1991.

    Google Scholar 

  30. D.J. Krichten, K.D. Wilson, and K.D. Tracy, “Expert systems guide biological phosphorus removal,” Water Environmental Technology, pp. 60–64, Oct. 1991.

  31. S. Krovvidy and W.G. Wee, “Wastewater treatment systems from case-based reasoning,” Machine Learning, vol. 10, pp. 341–363, 1993.

    Google Scholar 

  32. J. Lapointe, B. Marcos, M. Veillette, G. Laflamme, and M. Dumontier, “Bioexpert—an expert system for wastewater treatment process diagnosis,” Computers & Chemical Engineering, vol. 13,no. 6, pp. 619–630, 1989.

    Google Scholar 

  33. J. Lebowitz, “Experiments with incremental concept formation: UNIMEM,” Machine Learning, vol. 2, pp. 103–138, 1987.

    Google Scholar 

  34. D.B. Lenat, “The role of heuristics in learning by discovery: Three case studies,” Machine Learning: An Artificial Intelligence Approach, edited by R.S. Michalsky, J.G. Carbonell, and T.M. Mitchell, Springer-Verlag: Berlin, 1984.

    Google Scholar 

  35. R. López de Mántaras, “A distance-based attribute selection measure for decision tree induction,” Machine Learning, vol. 6,no. 1, pp. 81–92, 1991.

    Google Scholar 

  36. K. Maeda, “A knowledge-based system for the wastewater treatment plant,” Future Generation Computer Systems, vol. 5, pp. 29–32, North Holland, 1989.

  37. P. Meseguer, “Towards a conceptual framework for expert systems validation,” AI Communications, vol. 5,no. 3, pp. 119–135, 1992.

  38. Metcalf & Eddy Inc., Wastewater Engineering: Treatment/Disposal/Reuse, McGraw-Hill, 3rd edition, 1991.

  39. R.S. Michalsky and R.E. Stepp, “Learning from observation: conceptual clustering,” Machine Learning: An Artificial Intelligence Approach, edited by R.S. Michalsky, J.G. Carbonell, and T.M. Mitchell, Springer-Verlag: Berlin, 1984.

    Google Scholar 

  40. R.S. Michalsky and R.E. Reinke, “Incremental Learning of decision rules: A method and experimental results,” Machine Intelligence, Oxford University Press, Oxford, UK, p. 11, 1986.

    Google Scholar 

  41. T.M. Mitchell, “Generalization as search,” Artificial Intelligence, vol. 18, pp. 203–226, 1982.

    Google Scholar 

  42. R. Moreno, C. de Prada, J. Lafuente, M. Poch, and G. Montague, “Non-linear predictive control of dissolved oxygen in the activated sludge process,” Proc. ICCAFT 5/IFAC-BIO 2 Conference, Keystone (CO), USA, 1992.

    Google Scholar 

  43. G. Patry and D. Chapman (Eds.), Dynamic Modelling and Expert Systems in Wastewater Engineering: Lewis Publishers: Chelsea,MI, 1989.

    Google Scholar 

  44. E. Plaza, A. Aamodt, A. Ram, W. van de Velde, and M. van Someren, “Integrated learning architectures,” Proc. European Conference on Machine Learning (ECML-93), Lecture Notes in Artificial Intelligence 667, Springer-Verlag, 1993, pp. 429–441.

  45. J.R. Quinlan, “Induction of decision trees,” Machine Learning, Kluwer Academic Publishers: Boston, MA, USA, 1986, vol. 1, pp. 81–106.

    Google Scholar 

  46. D. Riaño, “Automatic knowledge generation from data in classification domains,” Master's Thesis, Dept. de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, 1994.

  47. M. Sànchez, I. R-Roda, M. Poch, U. Cortés, and J. Lafuente, “DAI-DEPUR architecture: Distributed agents for real-time WWTP supervision and control,” Proc. of IFAC/IFIP/IMACS Symp. on Artificial Intelligence in Real Time Control (AIRTC'94), València, 1994, pp. 147–152.

  48. M. Sànchez, I. R-Roda, M. Poch, U. Cortés, and J. Lafuente, “DAI-DEPUR: An integrated and distributed architecture for wastewater treatment plants supervision,” Submitted to Artificial Intelligence in Engineering, 1995.

  49. R.C. Schank and S.C. Slade, “The future of artificial intelligence: Learning from experience,” Applied Artificial Intelligence, 5, pp. 97–107, Hemisphere Publishing Corporation, 1991.

  50. P. Serra, M. Sànchez, J. Lafuente, U. Cortés, and M. Poch, “DEPUR: Acknowledge based tool for wastewater treatment plants,” Engineering Applications of Artificial Intelligence, vol. 7,no. 1, pp. 23–30. Pergamon Press, 1994.

  51. L. Steels, “Components of expertise,” AI Magazine, vol. 11,no. 2, pp. 28–49, 1990.

    Google Scholar 

  52. G. Stephanopoulos and G. Stephanopoulos, “Artificial intelligence in the development and design of biochemical processes,” Trends in Biotechnology, pp. 241–249, Sept. 1986.

  53. S. Vere, “Multilevel counterfactuals for generalizations of relational concepts and productions,” Artificial Intelligence, vol. 14, pp. 138–164, 1980.

    Google Scholar 

  54. J.B. Weinberg, G. Biswas, and G.R. Koller, “Conceptual clustering with systematic missing values,” Proc. of 9th Int. Workshop on Machine Learning, 1992, pp. 464–469.

  55. P.H. Winston, “Learning structural descriptions from examples,” in The psychology of computer vision, edited by McGraw-Hill, New York, 1975.

  56. WPCF, “Operation of municipal wastewater treatment plants,” Manual of Practice, vol. 11, 2nd edition,Water Pollution Control Federation, 1990.

Download references

Author information

Authors and Affiliations

  1. Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Campus Nord-Edifici C5.C. Jordi Girona 1-3, 08034, Barcelona

    Miquel Sànchez, Ulises Cortés & Javier Béjar

  2. Department d‘Enginyeria Química, Universitat Autónoma de Barcelona, Edifici C. 08193 Bellaterra, Barcelona

    Joan De Grácia & Javier Lafuente

  3. Laboratori d‘Enginyeria Química i Ambiental, Universitat de Girona, Placa Hospital 6, 17071, Girona

    Manel Poch

Authors
  1. Miquel Sànchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ulises Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Béjar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joan De Grácia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Javier Lafuente
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manel Poch
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sànchez, M., Cortés, U., Béjar, J. et al. Concept Formation in WWTP by Means of Classification Techniques: A Compared Study. Applied Intelligence 7, 147–165 (1997). https://doi.org/10.1023/A:1008202113300

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008202113300

Search

Navigation