Coagulation monitoring through a learning and expertise methodology for drinking water
The present work proposes a learning classification method to identify the functional states of a coagulation process for the treatment of surface water and production of drinking water. Supervisory control and diagnosis were performed using the LAMDA (Learning Algorithm for Multivariate Data Analysis) classification technique. This expert learning method involves the processing and aggregation of all information stemming from an environmental process, and it allows the incorporation of the user’s knowledge. The study shows that it is possible to refine the diagnosis by taking into account the information obtained from common sensors (e.g., temperature, suspended solids, pH, conductivity, dissolved oxygen) together with the predicted coagulant dosage, as computed with an intelligent software sensor developed previously. The Rocade drinking water plant located at Marrakech, Morocco was chosen to test the method.