Characterization of alum floc in water treatment by image analysis and modeling

Abstract

In many water treatment plants, flocculation is the key unit concerning the performance of water treatment. For this reason, monitoring the flocculation (i.e. floc size) is a crucial issue to achieve the acceptable performance for the process. Generally, flocculation is monitored by naked eye or using complex, sample-based methods. This is laborious and expensive, however, and should be either automated or alternative methods for estimating the floc quality should be developed if possible. In this paper, we present an online characterization system for estimating the most essential quality parameters of floc using digital images taken in the flocculation unit. In addition, we compare the surface area of the floc particles defined using the images with other measurement data collected from the process, and create a multivariable regression model for it. We also illustrate the dependencies between the floc properties and other process variables using a self-organizing map.

Keywords

• water treatment
• water quality
• image analysis
• flocculation
• regression
• self-organizing map