A Knowledge-Based System for the Diagnosis of Waste-Water Treatment Plants

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. 1. Introduction A malfunction of a waste water treatment plant is a social and biological hard problem. A poorly treated waste water outside the plant can achieve dangerous ..