Dynamic control of disinfection for wastewater reuse applying ORP/pH monitoring and artificial neural networks

Abstract

This study applies on-line pH and oxidation-reduction potential (ORP) monitoring and artificial neural network models to dynamically control the wastewater chlorination and dechlorination dosage for reuse purposes. A series of wastewater chlorination and dechlorination experiments were conducted in a continuous laboratory-scale reactor. The ORP and pH variations in raw wastewater, and chlorination and dechlorination reactors were monitored on-line. Artificial neural networks (ANNs) were used to build control models using the monitored ORP and pH data. Another series of continuous experiments were conducted to evaluate the proposed control strategy for meeting different requirements for total coliform counts and residual chlorine concentrations for different wastewater reclamation purposes. The dynamical controlled experimental results show that chlorination and dechlorination were effectively controlled, and that appropriate disinfection efficiencies were achieved and remaining chlorine residuals in effluent were controlled simultaneously for different treatment targets. This ANN control method is simple and has potential benefits in reducing chemical costs for wastewater chlorination and dechlorination.