A Knowledge-based system for the diagnosis of wastewater treatment plants
Authors
Pau Gargallo 5
Departament de Llenguatges i Sistemes de Informació
Universitat Politècnica de Catalunya
Barcelona 08028
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. Cortés 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.
Postscript
You may want a postcript version of the article:
A Knowledge-based system for the diagnosis of wastewater treatment plants
Back to the Artifical Intelligence Section page
Last modified on .
Page mantainer: webia@lsi.upc.es