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 pinpoint 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 130,000 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.