Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks

Abstract

Disinfection is one of the most critical processes for municipal wastewater treatment. However, traditional chemical dosing approaches do not consider how changes in water quality and process operation can alter disinfection performance. This work aims to develop novel disinfection models for precise prediction of peracetic acid (PAA) performance that considers real-time changes in water quality. Artificial and recurrent neural networks (ANN and RNN, respectively) are trained to predict PAA at various locations throughout the disinfection basin, CT (a function of the active concentration and contact time), and pre- and postdisinfection Escherichia coli using online and laboratory data. An ANN is found to predict PAA concentrations at an error rate comparable to that of an online analyzer. Additionally, an ANN can predict CT more accurately than a conventional first-principles method both with and without an online analyzer. An ANN with a lagged response variable can predict E. coli in a fraction of the time of an RNN, but with a slightly increased error. The integration of the models developed in this work into existing monitoring and control systems could provide treatment facilities with more robust and dynamic disinfection control without the need for costly analyzers.