Fuzzy Automata Identification Based on Knowledge Discovery in Datasets for Supervision of a WWT Process

This paper describes a methodology for the design of a supervisory system applied to a wastewater treatment process. A behavioral model is build by the joint participation of the process expert together with clustering techniques applied to measured signals. A fuzzy automaton becomes a heuristic model of the process under supervision. In the application examples, a real data for a activated sludge wastewater plant was used. The automaton states are identified by experts as the significant operation situations and the measured variables of the plant generate the transitions. Thus, this model reflects the dynamics of the continuous system underneath.