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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 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; @ESEMAG

• 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. The second level of real-time modelling is shown in Figure 2, trending the nitrification rate needed to nitrify the incomcontinued overleaf…

September 21 — 25, 2019 Chicago, Illinois




June 2019  |  53

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Environmental Science & Engineering Magazine | June 2019