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In this work, a mechanistic dynamic model of continuous flow peracetic acid (PAA) disinfection was developed, calibrated and validated, assuming E. coli as indicator microorganism. The model was conceived as a 1-dimensional dispersion model integrating PAA first order decay and E. coli inactivation rate. Lab-scale batch experiments of PAA decay and E. coli inactivation experiments were performed to calibrate corresponding kinetic models. In each sample, conventional wastewater quality parameters were monitored. A PAA pilot reactor was set up to perform both tracer studies, for dispersion model calibration, and continuous flow disinfection experiments, to validate the integration of hydraulics and kinetics models, under both stationary and dynamic conditions. Linear regression models were calibrated to predict hydrodynamic dispersion, given the flow rate, and PAA decay parameters, given effluent quality and PAA dosage. Successful validation of the PAA disinfection model proved the importance of (i) considering the disinfection process as a dynamic system and (ii) integrating real-time estimation of process disturbances, being the initial E. coli concentration and the impact of effluent quality and PAA dosage on PAA decay kinetics. Importantly, novel inactivation models were proposed, as two different modifications of a literature model for thermal inactivation. These models are suitable for dynamic simulation of Eulerian models and can describe the typical triphasic behavior of inactivation kinetics., (Copyright © 2022. Published by Elsevier Ltd.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)