Development of smart data analytics tools to support wastewater treatment plant operation

A case study of applying chemometrics approach, k-means, a clustering algorithm to develop a real-time industrial process early warning system using online measurements was conducted. An online spectrophotometer was installed for an eighteen-month monitoring study between 2013 and 2015 at the inlet of a wastewater treatment plant. During this time a web-based prototype portal with data integration, visualization, prediction and anomaly detection functions for complex online data sets was developed in-house to assess the spectral data acquired by the spectrophotometer together with other databases (such as rainfall and temperature). Several chemometrics options, such as association analysis and feature selection, were used to extract useful operational information from the acquired data. In this paper, the anomaly detection function which includes pattern learning and comparison algorithms and a powerful user interface was described in detail. By using the functions, process upsets were successfully detected from the spectral data at the inlet of the treatment plant. The detected events/ upsets were then compared with the treatment plant logs and they were found aligned well, which proved that the anomaly detection technique was effective and has the potential to inform decision to assist plant operators. In addition, the proposed anomaly detection technique is also a flexible algorithm which works with any similar time series data to detect other process related issues to provide real-time warning to support treatment plant operations.