Dynamic impact of cellulose and readily biodegradable substrate on oxygen transfer efficiency in sequencing batch reactors

Effect of thermal shock and sustained heat treatment on mainstream partial nitrification and microbial community in sequencing batch reactors

In order to develop a promising means of achieving mainstream short-cut nitrification, this study evaluated the effect of thermal shock on nitrite accumulation using intermittent offline and continuous inline heat treatment of biomass in sequencing batch reactors (SBRs). The SBRs fed with municipal wastewater were operated at a solid retention time of 7 days and nitrogen loading rate of 0.04 gN/L·d to 0.08 gN/L·d without the application of pre-treatment. Contrary to literature studies that showed suppression of nitrite-oxidizing bacteria at temperature 60 to 80 °C, nitrite accumulation was achieved temporarily when 20% of the biomass was heated for 2 h at 47 °C, as well as in continuously heated SBRs at 37 °C and 42 °C. The continuously heated reactors at 37 °C and 42 °C produced a maximum nitrite accumulation ratio (NAR) of 0.59 and 0.79, respectively, whereas the intermittent offline heating at 47 °C-2 h produced a NAR of 0.37. Although nitrite accumulation was stable only for 10-12 days in all heated reactors, this study demonstrates the achievement of mainstream partial nitrification (PN) at lower temperature (42 °C) than that reported in literature and also highlights the potential for achieving PN by implementing heat treatment of a portion of the return activated sludge (RAS) in biological nitrogen removal (BNR) systems. During the time when full nitrification was achieved, Nitrospira was more dominant than Nitrosomonas in all reactors at ratios of 1.4:1, 2.4:1, 2.4:1, and 3.7:1 for the control SBR (22 °C), 47 °C -2 h offline heating SBR, 37 °C SBR, and 42 °C SBR, respectively, suggesting that it may have played a role as a comammox bacteria capable of degrading ammonia to nitrates at elevated temperature.

Dynamic alpha factor prediction with operating data - a machine learning approach to model oxygen transfer dynamics in activated sludge

Aeration is an energy-intensive process of aerobic biological wastewater treatment. An accurate model of oxygen transfer dynamics in activated sludge tanks would improve design and operation of aeration systems. Such a model should consider spatial and diurnal variation of α-factor as well as site-specific conditions that impact oxygen transfer. For this dynamic prediction a machine learning approach was used for the first time. The data-driven method was based on long-term ex-situ off-gas measurements with pilot-scale reactors (5.8 m height, 8.3 m³ vol) coupled to full-scale activated sludge tanks on the sites of two conventional and a two-stage activated sludge treatment plant. The ex-situ off-gas method allowed to quantify theoretical off-gas parameters in non-aerated zones and thus consider the whole activated sludge tank. We introduced the α₀-factor to compare aerated and non-aerated zones under nonsteady-state conditions. Like the established α-factor for steady-state conditions, the α₀-factor describes oxygen transfer inhibiting effects in activated sludge. α₀-factor was lowest in upstream denitrification zones. This indicates an anoxic elimination of oxygen transfer inhibiting wastewater contaminants which improved oxygen transfer in subsequent aerobic zones. Random Forest models predicted α₀-factor reliably in all examined activated sludge tanks even for stormwater events and seasonal variation. Model development only required online sensor data already available to operators. Our results suggest that machine learning models can dynamically predict α-factors in a variety of activated sludge processes, thus considering site-specific conditions in model training without manual calibration.

Dynamic alpha factors: Prediction in time and evolution along reactors

The performance of aeration - one of the most costly processes at water resource recovery facilities - is heavily impacted by actual wastewater characteristics which are commonly taken into account using the alpha factor (α). This factor varies depending on hydraulic and organic loading; such variance includes both time and spatial fluctuations. In standard design practice, it is often considered as a fixed number, or at best, a predefined time series. The objective of this paper is to propose a new method of predicting plantwide trends in the α factor through the use of process modelling which can accommodate diurnal and seasonal variations. The authors' concept takes into account the dependence of α on sludge retention time in the form of degradation kinetics, the effects of organic loading (influent filtered COD), the presence or absence of anoxic zones, diffuser depth, and the impact of high MLSS found in certain, e.g., MBR, technologies. The developed model was calibrated using data from numerous facilities, relying on off-gas measurements and tests in clean and process water. Model validation was carried out against averaged α factor gradient data from one plant, and against diurnal air flow measurements from another. The Benchmark Simulation Model 1 configuration was used to demonstrate the applicability of the proposed model - in estimation of blower energy consumption and peak air flow requirements - comparing it with constant and scheduled α factor-based approaches.

Evaluating the potential impact of energy-efficient ammonia control on the carbon footprint of a full-scale wastewater treatment plant

An assessment was performed for elucidating the possible impact of different aeration strategies on the carbon footprint of a full-scale wastewater treatment plant. Using a calibrated model, the impact of different aeration strategies was simulated. The ammonia controller tested showed its ability in ensuring effluent ammonia concentrations compliant with regulation along with significant savings on aeration energy, compared to fixed oxygen set point (DO_sp) control strategies. At the same time, nitrous oxide emissions increased due to accumulation of nitrification intermediates. Nevertheless, when coupled with the carbon dioxide emissions due to electrical energy consumption for aeration, the overall carbon footprint was only marginally affected. Using the local average CO₂ emission factor, ammonia control slightly reduced the carbon footprint with respect to the scenario where DO_sp was fixed at 2 mg·L^-1. Conversely, no significant change could be detected when compared against the scenarios where the DO_sp was fixed. Overall, the actual impact of ammonia control on the carbon footprint compared to other aeration strategies was found to be strictly connected to the sources of energy employed, where the larger amount of low CO₂-emitting energy is, the higher the relative increase in the carbon footprint will be.

Oxygen Transfer in Two-Stage Activated Sludge Wastewater Treatment Plants

Aeration is an energy-intensive process of aerobic biological treatment in wastewater treatment plants (WWTP). Two-stage processes enable energy-efficient operation, but oxygen transfer has not been studied in depth before. In this study, α-factors were determined with long-term ex situ steady-state off-gas measurements in pilot-scale test reactors (5.8 m height, 8.3 m3) coupled to full-scale activated sludge basins. A two-stage WWTP with more than 1 Mio population equivalent was studied over 13 months including rain and dry weather conditions. Operating data, surfactant concentrations throughout the two-stage process, and the effect of reverse flexing on pressure loss of diffusers were examined. The values of αmean, αmin, and αmax for design load cases of aeration systems were determined as 0.45, 0.33, and 0.54 in the first high-rate carbon removal stage and as 0.80, 0.69, and 0.91 in the second nitrification stage, respectively. The first stage is characterized by a distinct diurnal variation and decrease in α-factor during stormwater treatment. Surfactants and the majority of the total organic carbon (TOC) load are effectively removed in the first stage; hence, α-factors in the second stage are higher and have a more consistent diurnal pattern. Proposed α-factors enable more accurate aeration system design of two-stage WWTPs. Fouling-induced diffuser pressure loss can be restored effectively with reverse flexing in both treatment stages.