Providing wastewater treatment plants with predictive knowledge based on transition networks

Presents a progress report on integrating predictive skills into an integrated AI system for wastewater treatment plant (WWTP) supervision and control. Although the embedded approaches within the previously developed architecture, called DAI-DEPUR, such as numerical control knowledge, rule-based reasoning and case-based reasoning, are able to cope with the overall supervision task of a plant, one feature is missing: predictive knowledge. With the previous approaches, the supervisory system works reasonably well, but the actuation process always restores the normal operation of a WWTP tardily. Thus, the supervision is implemented in an a posteriori fashion, which can be very dangerous for the environment. The integration of a new kind of knowledge can overcome this problem of control systems