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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 preand 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.

Most cold-climate biological nutrient removal facilities experience poor settling mixed liquor during winter, resulting in treatment capacity throughput limitations. The Metro Wastewater Reclamation District in Denver, Colorado, operated two full-scale secondary treatment trains to compare the existing biological nutrient removal configuration (Control) to one that was modified to operate with an anaerobic selector and with hydrocyclone selective wasting (Test) to induce granulation. Results from this evaluation showed that the Test achieved significantly better settling behaviour than the Control. The difference in the mean diluted SVI30 between the Test and Control were statistically significant (P < 0.05), with values of 77 ± 17 and 135 ± 25 mL/g observed for the Test and Control respectively. These settling results were accompanied by differences in the particle size distribution, with notably higher settling velocities commensurate with increasing particle size. The degree of granulation observed in the Test train was between 32 and 56% of the mass greater than ≥250 μm in particle size whereas 16% of the mixed liquor in the Control was ≥250 μm over the entire study period. The improved settling behaviour of the Test configuration may translate into an increase of secondary treatment capacity during winter by 32%.

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