Full Description

Automation of water treatment plant processes is difficult as a result of complexchemical reactions and physical phenomena. It is further complicated by thedramatic variation which can occur in raw water quality and the lack of generalalgorithms which describe many of the processes. As a result, the use oftraditional process control techniques has had limited success in many watertreatment applications. Presented is a preliminary attempt at process automationthrough a process control system utilizing artificial neural network (ANN)models. This project has shown that the ANN approach is effective at modeling thecoagulation process for both turbidity and organics removal using raw waterquality and operating conditions. Proposed is an ANN model-based process controlthat allows easy integration with supervisory control and data acquisition(SCADA) systems. Preliminary results of ANN on-line process control at theRossdale Water Treatment Plant on the North Saskatchewan River are presented. TheANN control system can be used as a control system for daily operations and canaid operators in selecting chemical doses and operating conditions. The systemcan also act as a virtual training platform for new operators. Includes 11 references, tables, figures.

Product Details

Edition: Vol. - No. Published: 01/01/1999 Number of Pages: 16File Size: 1 file , 290 KB