AWWA WQTC57125

A significant amount of research has been conducted in the past ten years to propose alternative solutions for the on-line prediction of the optimal coagulant dose as a function of raw water quality characteristics (such as turbidity, pH, conductivity, etc.). A jar-test is usually applied in order to control the coagulant dose. However, this method is manual and cannot be performed in a continuous manner. Moreover, it implies the presence of a qualified operator. In the large water treatment plants a jar-test is performed daily, while in small plants the frequency is even lower because of the associated O&M costs. Different methods can be used for the on-line control and automatic regulation of the coagulation process. The main objective of this study was to perform feedback regulation based on the water quality assessment immediately after coagulant injection. This method implies the use of a specific sensor for the coagulation. This analyzer evaluates continuously the coagulant addition to the raw water and sends a signal directly proportional to the result of the coagulation. Studies performed in the past have shown the potential interest of implementing this tool on some full-scale water treatment plants. However, this method is not adaptable to all types of raw water quality. A second approach developed a mathematical model for the coagulation process using the raw water quality descriptive parameters. This method linked the optimal coagulant dose to different raw water quality parameters. This approach has been difficult to develop in the past due to the complexity of the coagulation process characterized by a strong non-linearity between the dose of the chemicals and the raw water quality parameters. Modelling techniques have recently been used such as the artificial neural network (ANN) adapted for the complex non-linear systems and which proved to be very promising for the development of a specific algorithm for the coagulant dose prediction. Includes 5 references, table, figures.

Product Details

Edition:

Vol. - No.

Published:

11/01/2002

Number of Pages:

9

File Size:

1 file , 380 KB