A knowledge-based approach to the deflocculation problem: integrating on-line, off-line, and heuristic information.
A knowledge-based approach for the supervision of the deflocculation problem in activated sludge processes was considered and successfully applied to a full-scale plant. To do that, a methodology that integrates on-line, off-line and heuristic information has been proposed. This methodology consists of three steps: (i). development of a decision tree (which involves knowledge acquisition and representation); (ii). implementation into a rule-based system; and (iii). validation. The set of symptoms most useful in diagnosing the deflocculation problem has been identified, the different branches to diagnose pin-point floc and dispersed growth have been built (using generic and specific knowledge), and all this knowledge has been codified into an object-oriented shell. The results obtained in the application of this knowledge-based approach to the Granollers WWTP (which treats about 130000 inhabitants-equivalents) showed that the system was able to identify correctly the problem with reasonable accuracy. Our positive experience building this system suggests that this approach is a practical and valuable element to include in an intelligent supervisory system combining numerical and reasoning techniques.
Chemical and Environmental Engineering Laboratory, University of Girona, Campus Montilivi s/n, Girona E-17071, Spain. quim@lequia.udg.es
J
J
Comas
I
I
Rodríguez-Roda
M
M
Sànchez-Marrè
U
U
Cortés
A
A
Freixó
J
J
Arráez
M
M
Poch
D001185:Artificial Intelligence; D001331:Automation; D003663:Decision Trees; D005427:Flocculation; D008962:Models, Theoretical; D012722:Sewage; D014865:Waste Disposal, Fluid
D012722:Sewage