Improving the management of rapid filtration in a DWTP using k-Means clustering

Rapid filtration have been used since the early stages of drinking water treatment plants (DWTP) as an effective physical barrier against pathogens and suspended solids. The seemingly simple operation consists of setting filter run-time and backwashing conditions, however plants that operate big amounts of filters require an effective and informed maintenance planning to avoid failures of the filters. Several causes can affect filters performance such as: Irregular filter bed due to hydraulic impacts, ineffective backwash, presence of fine particles or sensor failures, among others. Currently, this malfunctioning can be detected using online available data, but this task is very time consuming without an adequate data-mining algorithm. The objective of this work is to develop a flexible framework for providing a data-based diagnosis of filters performance, based on clustering analysis. For this purpose, two statistical clustering techniques were compared (k-Means and hierarchical clustering) using the clean bed headloss and a saturation index as main features. The developed tool rates the 48 filters of a full-scale DWTP in a traffic light colour as a visual indicator of performance according to which cluster they belong, therefore indicating where the attention is needed. The presented methodology can increase process knowledge and provide a basis for decision-making and planning of maintenance tasks in large filtration systems.