Modelling ammonia-based aeration control shows over 65% energy savings possible

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Hach RTC-N system
Touch screen display of the Hach RTC-N system used for modelling.

By Bob Dabkowski, Robert Relph, George Wendorf, Kevin Menning and Melody White

A real-time model for nitrification control based on activated sludge model No.1, or ammonia-based aeration control, has become popular with water resource recovery facilities (WRRFs) due to the potential paybacks associated with energy savings and enhanced process control.

Often a WRRF will have infrastructure that is designed for the maximum capacity of the facility and therefore oversized for the current operation. Utilities that want to optimize their systems and achieve energy savings are challenged to do so with the existing infrastructure and will typically implement a capital improvement project to right-size their systems for the current and future conditions. These projects are best justified through payback analysis or return on investment calculations. This can be difficult to estimate with data based on composite samples that do not illustrate the effects of the high and low loading periods.

Recognizing this problem, utilities are looking for solutions that might allow them to model their process in real time and capture the entirety of the loading. At one WRRF, the staff understood these issues and discovered that the Hach® real-time controller for nitrification (RTC-N) could also be used as a real-time modelling system. The RTC-N calculates the required dissolved oxygen (DO) concentration needed to nitrify the incoming load in real-time. The calculated DO value could then be compared to the actual DO concentration in the aerobic volume to determine the potential energy savings by upgrading to ammonia-based aeration control. This provides a more robust justification for the capital improvement project.

Case study

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The WRRF in this study is a 115 million litres per day (MLD) design modified Ludzack-Ettinger process with an average daily flow of 48 MLD. The discharge permit for the concentration of nitrate nitrogen is below 10 mg/L NO₃-N.

The Hach RTC-N is a model-based controller which can be configured to either model a nitrification system, or control it, through a variable DO concentration. Configuring the real-time modelling system requires integrating inputs, entering limits on measurements and calculations, along with populating initial data to seed the model.

To properly model these processes in real-time, the following inputs must be collected in real-time:

  • Influent, return activated sludge (RAS), and, if present, internal recirculation (IRQ) flows;
  • Aeration influent and effluent ammonium concentrations;
  • Mixed liquor suspended solids (MLSS);
  • Temperature of the mixed liquor;
  • Average DO concentration of the aerobic volume, or the DO concentration in each zone (optional).

Once all the data is entered into the system and limits and setpoints are configured, real-time information is input ed from the field sensors, allowing the real-time modelling system to output the following information:

  • Required DO concentration in the aerobic volume to nitrify the given load;
  • Percentage of the mixed liquor which are nitrifiers;
  • Estimated sludge retention time (SRT) of the aerobic system;
  • Ammonia load to the aerobic system;
  • Maximum possible nitrification rate of the aerobic system;
  • Required nitrification rate to nitrify the incoming load.

Results

Evaluating the data reveals the benefits of real-time modelling. Figure 1 shows the variability of influent ammonia, even though the influent flow rate is fairly level. It also shows that a majority of the time the effluent ammonia was below detectable limits (<0.05mg/L NH4-N) with small spikes less than 1.5mg/L NH₄-N towards the end of the study period.

Figure 1 chart
Figure 1. Comparison of influent and effluent ammonia concentrations during a 15 day period.

The second level of real-time modelling is shown in Figure 2, trending the nitrification rate needed to nitrify the incoming ammonia load against the maximum possible nitrification rate of the system.

Figure 2 chart
Figure 2. Trending required real-time nitrification rate and real-time maximum nitrification rate.

Present in the trends in Figure 2 are two important operational observations. First, there are times on April 1 and April 3 that the needed nitrification rate spikes near or above the maximum nitrification rate. While this is not evident in the respective effluent ammonia data, it is evident through these modeled trends. It is important for operators to have this information, especially when deciding to treat sidestreams with high ammonia loads or other factors which could inhibit nitrification.

The second observation is that the actual nitrification rate is distant from the maximum rate during the effluent ammonia spikes on April 13, 14 and 15. This means that the spikes in effluent ammonia were not due to a problem from insufficient DO, but most likely due to a toxic load which inhibited nitrification quickly and briefly.

Lastly, in Figure 3 the real-time modelling system highlights the biggest benefit of all: the actual DO concentration as compared to the optimal DO concentration and the potential energy savings of performing ammonia-based aeration control. The optimal DO concentration is calculated to match the nitrification rate to the given load. This ensures complete nitrification of the load while attaining some effects from simultaneous nitrification/denitrification, and limited endogenous respiration in the aeration tank.

Figure 3 chart
Figure 3. Trends of optimal DO concentration, actual DO concentration and potential energy savings.

This suggests that the DO concentration could be reduced to decrease the nitrification rate and save energy. Based on this limited set of data, the reduction in energy usage by switching to ABAC would be approximately 65.8%.

Conclusions

The real-time modelling system is an effective tool for evaluating current operations and presenting how they can be improved and optimized through ammonia-based aeration control. Differences between the current operations and the improved operations could be used to justify upgrades to an existing aeration system or other projects to perform ammonia-based aeration control.

As a modelling tool, the system helps operators manage sidestreams by showing when the needed nitrification rate may be close to the maximum possible rate, indicating that the nitrification system is near its capacity. For engineers, the nitrifier concentration and nitrification rates combined with the optimal dissolved oxygen concentration allow for a more accurate and efficient aeration system design.

Bob Dabkowski, Kevin Menning and Melody White are with Hach. Robert Relph and George Wendorf are with the City of San Diego. This article appears in ES&E Magazine’s June 2019 issue.

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