A Knowledge-based system for the diagnosis of wastewater treatment plants

Authors

Miquel Sànchez-Marrè miquel@lsi.upc.es
Lluís Belanche belanche@lsi.upc.es
Ulises Cortés ia@lsi.upc.es

Pau Gargallo 5
Departament de Llenguatges i Sistemes de Informació
Universitat Politècnica de Catalunya
Barcelona 08028

Pau Serra

Grup AGBAR S.A.
Passeig de Sant Joan 45
08009 Barcelona, Catalonia, Spain

Abstract

In this work we discuss the development of an expert system with approximate reasoning which resorts to a new methodology for attribute selection in knowledge-based systems. First, we make a survey of the purifying process and its problems, as well as those of conventional automatic control methods applied to industrial processes. Next, we establish a definition of the relevance concept for a given set of attributes, which includes the special case of non-relevant attributes or nought attributes. A new heuristic is here proposed in such a way that it finds out the more relevant attributes from those initially selected by the expert, reducing the cost of the formation & validation of decision rules and helping to clarify the underlying structure of a non well-structured domain as are waste-water treatment plants.

Citation:

L. Belanche, M. Sànchez, U. CortÚs and P. Serra `` A knowledge-based system for the diagnosis of waste-water treatment plant ''. Proceedings of 5th International Conference on Industrial and Engineering Applications of AI and Expert Systems IEA/AIE-92. Paderborn, Germany, June 92. Lecture Notes in Artificial Intelligence 604, pp. 324-336. Springer-Verlag.

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