Detecting faulty machinery of waste water treatment plant using statistical analysis & machine learning

Abstract

The goal of wastewater treatment is to eliminate contaminants from wastewater and convert them into effluent/discharge that can be reintroduced into the water cycle. In order to monitor, analyze plant performance, and decrease environmental pollution in wastewater treatment facilities, a model for fault detection must be developed. In this study, we examine different time and cost-efficient machine learning approaches to monitor the operation of a Waste Water Treatment Plant (WWTP) and identify plant faults as an alternative to human, laboratory-based time consuming, costly, and challenging techniques. This will allow us to develop a time and cost-efficient approach to detect such problems. To discover plant defects, we collect one year of unsupervised WWTP data and convert the data into supervised data. Using several machine learning algorithms based on water quality standard measurements (pH, BOD, COD, and suspended solid), we establish whether or not the data is valid.