Nondeterministic Computational Fluid Dynamics Modeling of Escherichia coli Inactivation by Peracetic Acid in Municipal Wastewater Contact Tanks

Abstract

Wastewater disinfection processes are typically designed according to heuristics derived from batch experiments in which the interaction among wastewater quality, reactor hydraulics, and inactivation kinetics is often neglected. In this paper, a computational fluid dynamics (CFD) study was conducted in a nondeterministic (ND) modeling framework to predict the Escherichia coli inactivation by peracetic acid (PAA) in municipal contact tanks fed by secondary settled wastewater effluent. The extent and variability associated with the observed inactivation kinetics were both satisfactorily predicted by the stochastic inactivation model at a 95% confidence level. Moreover, it was found that (a) the process variability induced by reactor hydraulics is negligible when compared to the one caused by inactivation kinetics, (b) the PAA dose required for meeting regulations is dictated equally by the fixed limit of the microbial concentration as well as its probability of occurrence, and (c) neglecting the probability of occurrence during process sizing could lead to an underestimation of the PAA dose required by as much as 100%. Finally, the ND-CFD model was used to generate sizing information in the form of probabilistic disinfection curves relating E. coli inactivation and probability of occurrence with the average PAA dose and PAA residual concentration at the outlet of the contact tank.

Defence strategies and antibiotic resistance gene abundance in enterococci under stress by exposure to low doses of peracetic acid

“…Commonly investigated PAA doses for disinfection treatment range between 10 and 400 mg L -1 min (Santoro et al, 2015), suggesting values below 50 mg L -1 min as low doses. However, most previous works do not estimate the actual PAA dose but only report initial PAA concentration and contact time, although these operating conditions are often insufficient to exhaustively describe the disinfection process because of PAA decay.…”

A generalized model for bacterial disinfection: Stochastic approach

“…For illustration, the analytical mean, standard deviation, and coefficient of variation, as given by Eqs. (16), (20), and (21), corresponding to the non-linear cases, (α ≥ 0, α ≠1, β ≥ 0), of the generalized stochastic model are validated with the same set of experimental data [25] deployed in the preceding case study. For this dataset, the initial number of bacteria at the termination period of disinfection, i.e., n 0 , is 146 cells per milliliter as identified in our earlier work [23].…”

“…In fact, numerous works offer unambiguous discourses on the need for the stochastic treatment of bacterial behavior, especially the bacteria's population dynamics [13][14][15][16]. Naturally, many of these works deal with the stochastic modeling of the inactivation, or disinfection, of bacterial populations [17][18][19][20].…”

“…The analytical mean, standard deviation, and coefficient of variation have been computed from Eqs. (16),(20), and(21), respectively, with these values of (α, β) and k α,β . For further validation, the analytical values have been compared with their corresponding Monte Carlo estimates obtained with the same values of n 0 , (α, β), and k α,β .…”

Disinfection Processes

“…Computational fluid dynamics (CFD) in a nondeterministic modeling framework was used to predict E. coli inactivation by PAA in secondary settled wastewater effluent; the required dose was affected equally by the effluent limit and by its probability of occurrence, and neglecting the probability of occurrence could lead to underestimation of the PAA dose (Santoro et al, 2015).…”