Automation of water treatment plant processes is difficult as a result of complex
chemical reactions and physical phenomena. It is further complicated by the
dramatic variation which can occur in raw water quality and the lack of general
algorithms which describe many of the processes. As a result, the use of
traditional process control techniques has had limited success in many water
treatment applications. Presented is a preliminary attempt at process automation
through a process control system utilizing artificial neural network (ANN)
models. This project has shown that the ANN approach is effective at modeling the
coagulation process for both turbidity and organics removal using raw water
quality and operating conditions. Proposed is an ANN model-based process control
that allows easy integration with supervisory control and data acquisition
(SCADA) systems. Preliminary results of ANN on-line process control at the
Rossdale Water Treatment Plant on the North Saskatchewan River are presented. The
ANN control system can be used as a control system for daily operations and can
aid operators in selecting chemical doses and operating conditions. The system
can also act as a virtual training platform for new operators. Includes 11 references, tables, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 290 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 16 |
| Published : | 01/01/1999 |
