Data-Driven Approach of KPI Monitoring and Prediction with Application to Wastewater Treatment Process

Abstract

In this paper, a data-driven scheme of key performance indicator (KPI) monitoring, prediction and KPI related fault detection is applied to the wastewater treatment process (WWTP). By means of a data-driven realization of the so-called left coprime factorization (LCF) of the process, the efficient monitoring and prediction of chemical oxygen demand (COD) concentration in the effluent flow are realized both for the situation that COD is measurable and unmeasurable. The well established Benchmark Simulation Model no. 1 (BSM1) is utilized for the demonstration of the effectiveness of this approach.