Artificial neural network modeling of full-scale UV disinfection for process control aimed at wastewater reuse

• Published in: Journal of environmental management 2021-12, Vol.300, p.113790-113790, Article 113790
• Main Authors: Foschi, Jacopo, Turolla, Andrea, Antonelli, Manuela
• Format: Article
• Language: English
• Subjects: Artificial neural network; Control; E. coli; Reuse; UV disinfection; Wastewater; Zero inflated dataset
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2021.113790

Accurate modeling of wastewater ultraviolet disinfection is fundamental as support for process optimization and control. Detailed modeling of hydrodynamics and fluence rate via computational fluid dynamics, coupled to laboratory studies of inactivation kinetics, are usually the preferred approach for UV disinfection modeling. Despite this approach often provides accurate predictive performance, it requires significantly high computational time, making it unfeasible for real-time process control. In this study, to enable an effective process control, black-box regression models were assessed as a modeling alternative for UV disinfection, synthesizing hydrodynamics, fluence rate and inactivation kinetics. UV disinfection of a full-scale wastewater treatment plant in Italy was monitored for 10 months, measuring influent and effluent E. coli concentration, turbidity, absorbance at 254 nm, temperature and flow rate at different UV doses. Considering the usually observed distribution of effluent E. coli concentration and the zero inflation of the collected dataset, Poisson, zero-inflated Poisson and Hurdle generalized linear models were tested, as well as two-part models coupling a classifier describing the E. coli zero-count events and a regressor estimating the magnitude of E. coli concentrations in positive-count events. The two-part artificial neural network model showed the best predictive performance, being able of both describing nonlinearities and handling the high proportion of null values in the dataset. The deployment of this model to control ultraviolet disinfection was simulated, estimating a plausible 63% energy saving. [Display omitted] •A neural network model for UV wastewater disinfection modeling was developed.•The model was successfully tested on a full-scale UV disinfection unit.•An effective method to handle zero inflation of E. coli counts data were developed.•The benefits of the UV model as a process control tool were highlighted.