MODEL-BASED AERATION SYSTEMS DESIGN - CASE STUDY NANSEMOND WWTP Leiv Rieger 1*, Charles B. Bott 2, William J. Balzer 2 and Richard M. Jones 1 1 EnviroSim Associates Ltd., Hamilton, Ontario, Canada. 2 HRSD (Hampton Roads Sanitation District), Virginia Beach, Virginia, USA. * Email: rieger@envirosim.com. ABSTRACT A simulation project was conducted for HRSD s Nansemond WWTP to evaluate the savings potentials for different aeration control strategies based on ammonia and DO measurements. Special emphasis was put on modeling and analyzing plant constraints including blower minimum and maximum capacities, diffuser specifications, and mixing requirements. The results show significant savings potential with ammonia- and/or DO-based aeration control strategies. Detailed modeling of the air distribution system allowed the design of a control concept tailored to the Nansemond plant. Feed-forward ammonia control was only active at low temperature (12 C) and did not significantly lower effluent ammonia peaks. Therefore feedforward control was not selected for full-scale implementation as it could not justify additional investment and O&M costs against improved effluent quality or reduced risk. KEYWORDS: Aeration control, ammonia-based control, feedforward control, methanol savings, energy reduction. INTRODUCTION The Nansemond WWTP has undergone a major upgrade of its treatment capacity, changing from a 3-stage VIP process to a 5-stage Bardenpho process configuration to fulfill stricter effluent requirements. The current limits are: TN < 8 mg N/L and TP < 2. mg P/L on an annual average basis. Nansemond is also part of a seven plant combined discharge limit for TN of 2,7 tons/year (6 mill. pounds/year) which could demand better performance than the concentrationbased limits, and more stringent limits could be imposed in the future. The plant needs significant external carbon addition to fulfill the low nutrient effluent limits; this is a major operational cost. A project has been conducted to identify cost-effective aeration control strategies through simulation. The simulation study has been completed, and extensive sensor testing is underway as part of the full-scale implementation. The implementation project including additional experiments and measuring campaigns has been started and will be finished late 212.
Objectives The plant s goal is to improve TN removal while at the same time reducing aeration energy consumption and dosing of external carbon (by allowing effluent NH -N up to 1 mg N/L). Specific constraints were effluent PO -P <.5 mg/l and secondary effluent NO 2 -N < 1 mg N/L because hypochlorite is used for disinfection, though this might be acceptable if the controller can maintain sufficiently high NH concentrations in the secondary effluent for chloramination. MATERIAL AND METHODS Nansemond Treatment Plant The Nansemond Treatment Plant located in Suffolk, Virginia, USA receives about 6, m 3 /d (16 mgd) and a load of about 25, population equivalents. Figure 1 shows the plant layout. Figure 1. Layout Nansemond WWTP. It was decided to focus on the activated sludge part of the plant excluding the old Aeration tanks 1-3 and secondary clarifiers 1-2. The following process units were used in this project: Influent Channel. AAA stage with 1 cell unaerated (anaerobic), 3 cells unaerated (anoxic), and 2 cells aerated. Full volume of Aeration Influent Channel (including section connecting Aer1-3). Aeration tanks -7: Aeration zone 2, Aeration zone 3, second unaerated (anoxic) zone. Full volume re-aeration channel (including section connecting Aer1-3)
Clarifiers 3,, and 5. External carbon dosage points to first and second anoxic zones. Aeration System The Nansemond plant is equipped with four blowers of different capacity. Table 1 lists the minimum and maximum capacity in terms of air flow rate. The blower control is based on header pressure and provides air to three independently controllable aeration zones plus two air flow controlled zones (Aer Influent Channel for mixing and the Re-aeration Channel). Zone 1: DO setpoint = 2.5 mg/l (DO probe in AAA_E) Zone 2: DO setpoint = 2. mg/l (DO probe in Aer-7_a) Zone 3: DO setpoint = 1. mg/l (DO probe in Aer-7_g) Aer Influent Channel: Air flow rate setpoint = 333 Nm 3 /hr (22 scfm) Re-aeration Channel: Air flow rate setpoint = 3896 Nm 3 /hr (2293 scfm) Tapered Aquarius 9 membrane diffusers are installed in the aerated tanks (see Table 3 for detailed setup) and single drop diffusers in the aeration tank influent channel and the re-aeration channel. Table 1. Blower capacities. Blower min (5%) max Nm 3 /hr scfm Nm 3 /hr scfm Blower 1 7,65,5** 16,99 1, Blower 2 18,52 1,9 * 38,112 22,32 Blower 3 18,52 1,9 38,112 22,32 Blower 9,25 5,5** 2,558 12,1 * Tested on 28 Oct, 211 by HRSD; **calculated Process Model It was decided to model only the newer part of the Aeration Tanks (Aer -7) as this is planned to be the standard operation scheme. The seven parallel trains of the AAA stage and the four trains of the AER-7 stage were collapsed into one modeled train with the total combined volume. The three secondary clarifiers were also modeled as one model element. Figure 2 shows the resulting configuration as implemented in BioWin.. The number of tanks in series used in the final BioWin model was set up according to the diffuser grid distribution and the hydraulic behavior was checked against an estimation method by Fujie et al. (1983). A compromise between aeration grids and hydraulics was necessary: to reduce the number of modeled tanks in series, aeration grids with the same number of diffusers and the same tank area were combined. Table 3 shows the resulting tank configuration and information on the diffuser distribution.
Methanol AAA Influent Inf channel AAA_A AAA_B AAA_C AAA_D AAA_E AAA_F AER Inf channel Aer-7_a Aer-7_bc Aer-7_de Aer-7_fg Aer-7_h Aer-7_i Aer-7_j Reaer. Ch. SC3--5 Effluent Methanol Aer-7 WAS Figure 2. BioWin Configuration of the Nansemond WWTP. Operational Settings The following settings were used in the model: No ferric dosage was considered in the study. It was decided to use methanol as the carbon source even though glycerol has been used at the plant. This was based on methanol being the standard carbon source and therefore can be used as a standard for comparison. Plant internal flows (recycles, wastage, etc.) were set as follows: o NRCY (mixed liquor recycle to AAA_B): fixed flow-paced rate of 2% relative to the incoming flow according to current plant settings. o CRCY (return activated sludge recycle): 5% of incoming flow o Methanol dosage at two dosage points (AAA B and AER-7 h). o The WAS flow rate was adjusted to maintain the SRTs shown in Table 2. Table 2. Target SRTs for base case at different temperatures and resulting MLSS concentrations and WAS flow rates. Temperature Target SRT MLSS Q WAS for Base Case 12 C 18.2 d 27 mg TSS/L 1,136 m 3 /d (3, gal/d) 2 C 12 d 187 mg TSS/L 1,71 m 3 /d (6, gal/d) 3 C 8 d 125 mg TSS/L 2,65 m 3 /d (7, gal/d) Aeration system The aeration system was modeled at a level of detail that allowed analyzing the potential savings due to advanced control and to evaluate the impact of aeration system constraints (e.g. blower capacity, mixing requirements, diffuser specifications). An important step was modeling of the diffuser distribution and the calibration of the aeration model to fit the diffuser type used in the real plant.
Table 3. Diffuser distribution per modeled reactor. Aeration Zone Aeration Grids / modeled reactor (see Figure 2) # Diffusers per stage (over all trains) Volume per stage 1 AAA_E 1,82 3,971 1,9,53 m 3 gal AAA_F 1,82 3,971 1,9,53 # Diffusers per Aeration Zone 3,6 2 Aer-7_a 1,692,67 1,7,275 Aer-7_bc 2 x 1,52 8,812 2,327,8 Aer-7_de 2 x 1,32 8,812 2,327,8 7,236 3 Aer-7_fg 2 x 1,2 8,627 2,279,12 2, (3)* Aer-7_h 6 3,225 851,82 Aer-7_i 6 3,183 8,86 Aer-7_j 6 3,225 851,82 (+18) * Cells Aer-7_h-j are swing zones but were unaerated in this study. When aerated controlled by valve of Zone 3. BioWin s aeration model was calibrated to fit the given aeration performance tables for the Aquarius 9 membrane disc diffuser elements. Figure 3 shows the standard oxygen transfer efficiency (SOTE) after calibration. Table lists the new parameters. Table. Calibrated parameter values of BioWin aeration model for Aquarius 9 membrane disc diffusers. Aeration model parameter Units Calibrated Defaults k1 in C = k1(pc)^.25 + k2 1/d.636 2.5656 k2 in C = k1(pc)^.25 + k2 1/d 1.556.32 Y in Kla = C Usg ^ Y for Usg in units of m 3 /m 2 /d.91.82
SOTE (%/m) SOTE (%/m) 1 1 9 8 7 6 DD = 6% DD = 6% DD = 7% DD = 7% DD = 8% DD = 8% DD = 1% DD = 1% DD = 12% DD = 12% 9 8 7 6 DD = 6% DD = 6% DD = 7% DD = 7% DD = 8% DD = 8% DD = 1% DD = 1% DD = 12% DD = 12% 5 5 1 2 3 5 6 7 8 AIR FLOW PER DIFFUSER (m 3 /h/diffuser) 25 5 AIR FLOW PER UNIT TANK AREA (m 3 /d/m 2 ) Figure 3. SOTE versus air flow per diffuser and versus air flow per unit tank area after calibration. The air flow to the Aeration Tank Influent Channel and the Re-aeration Channel were controlled in the model to fit the actual plant setpoints. No specific calibration for the single drop diffusers was carried out but typical parameters for coarse bubble aeration were selected (Table 5). Table 5. Parameter values of BioWin aeration model for single drop diffusers. Aeration model parameter Units Selected k1 in C = k1(pc)^.25 + k2 1/d.5 k2 in C = k1(pc)^.25 + k2 1/d.38 Y in Kla = C Usg ^ Y for Usg in units of m 3 /m 2 /d 1.5 Air distribution Each aeration zone has its own control valve and a probe to measure the dissolved oxygen concentration. Air distribution is modeled based on the assumption that the most significant impact on pressure drops comes from the diffusers. A new Air Distribution Tool as implemented in the BW ler 2. was used to model the distribution according to diffuser area and valve position. Figure shows a screenshot of the Air Distribution Tool, Figure 5 shows the interactions of the different models, and Figure 6 gives a schematic representation of the Air Distribution Tool.
Figure. Screenshot of Air Distribution Tool in BW ler 2.. Aeration system model Blower controller Air distribution controller Air distribution tool Oxygen transfer model Biokinetic model Sensor model Figure 5. Schematic representation of aeration control as implemented in BioWin and the BW ler. Air flow from blowers BW ler Air Distribution Tool Flow split Air valve Aeration Zone 1 Air header Air valve Aeration Zone 2 Diff. grid AAA_E Diff. grid AAA_F Diff. grid Aer-7_a Diff. grid Aer-7_b Diff. grid Aer-7_c Diff. grid Aer-7_d Diff. grid Aer-7_e Air valve Aeration Zone 3 Diff. grid Aer-7_f Diff. grid Aer-7_g Figure 6. Schematic representation of the BW ler Air Distribution Tool.
Flow (mgd) Loading Pattern A dynamic influent data set of one week as input for the scenario analyses was created based on the following information: To calculate typical average influent concentrations, a routine data set from January 21 until March 211 was analyzed (Table 6 shows averaged loads). Hourly flow data from January 28 until March 211 were analyzed with EnviroSim s Flow Tool software. The outcomes are diurnal variations for weekdays and weekend days (Figure 7). The analysis showed no significant weekly variation in flow pattern. Diurnal patterns for concentrations of TSS, BOD 5, COD tot, TKN, TP were taken from three intensive sampling days of a special measurement campaign (17, 18, 23 September 27) (Figure 8). Table 6. Average load primary clarifier effluent as used in the simulations. Flow COD TKN NH-N Ptot PO-P ISS m 3 /d mgd kg/d kg/d kg/d kg/d kg/d kg/d 62,132 16.1 273 2928 2273 631 537 179 25 Average Diurnal Patterns (Dry Weather) 2 15 1 5.2..6.8 1 Time (hours) Monday Tuesday Wednesday Thursday Friday Saturday Sunday Figure 7. Average diurnal patterns for different weekdays.
Factor (-) Average Diurnal Conc. Patterns (Dry Weather) 1.6 1. 1.2 1..8.6..2. 2 6 8 1 12 1 16 18 2 22 2 Time (hours) COD TKN NH-N PT PO-P ISS Figure 8. Average diurnal concentration patterns for different compounds. The wastewater characteristics were adapted from previous simulation studies of the Nansemond plant (EnviroSim, 25). SCENARIO ANALYSIS Input Scenarios Four influent scenarios were simulated covering 3 different temperature ranges and a single day ammonia peak in addition to normal dry weather conditions: Input 1) Dry weather conditions at average temperature of 12 C Input 2) Dry weather conditions at average temperature of 2 C Input 3) Dry weather conditions at average temperature of 3 C Input ) Ammonia peak at average temperature of 12 C Since no measurements were available to justify a specific ammonia peak in the influent, four potential causes for an ammonia peak on top of the normal pattern were discussed: Internal cause e.g. by ammonia-rich digester supernatant dosage. However, at Nansemond the dosage is fully equalized and most of the ammonia should be removed by the Ostara process. A storm event pushing the content of the primaries with high ammonia concentration through the plant. This requires an extreme hydraulic peak and this is not expected to happen at Nansemond. HRSD also does not relate ammonia effluent violations to storm events. Septage dosage takes place between 7 am and 7 pm at Nansemond and may contribute to ammonia peaks. However, no specific peak in the available diurnal data could be identified. Single industrial discharges may cause ammonia influent peaks, but no specific peak was visible in the available diurnal patterns.
TKN (mg/l) It is therefore difficult to decide on the shape and heights of a potential peak. Because of no clear evidence for ammonia peaks it was decided to focus on the impact of an ammonia feed-forward controller on the plant performance during hypothetical peak conditions. A TKN peak over hours (see Figure 9) was created. Only the TKN concentration was increased; this simulates a worst case industrial discharge. 8 7 6 5 3 2 1 TKN peak 1 2 3 5 6 7 Days of week Figure 9. Artificial TKN peak on top of normal diurnal pattern. Simulation Procedure Dynamic simulations of each scenario combination were run for 7 days to reach quasi steadystate and then run for another 7 days to create the final data set. An exception was influent scenario, where the final run (including one day of increased ammonia load) was based on the final run of influent scenario 1 (12 C) without the ammonia peak. Strategies A focus was to account for existing equipment constraints such as blower maximum and minimum turn-down capacity, diffuser specifications and mixing requirements. The evaluation included dissolved oxygen (DO) and ammonia/do feed-back and feed-forward control strategies. Table 7 lists the control strategies as analyzed in this project, and Table 2 provides the SRT and resulting MLSS concentrations for different temperatures. Base scenario: The base scenario for all further analysis followed the actual aeration control of Nansemond during operation from January March 211: DO setpoint Zone 1 = 2.5mg/L, Zone 2 = 2 mg/l and Zone 3 = 1 mg/l. To show the full benefit of ammonia based control for supplemental carbon dosage, two NO x feed-back controllers are used in all scenarios and manually tuned to reach the targeted TN effluent concentration of 5 mg TN/L. These manipulate the methanol dosage with the NOx probes located in AAA_C and Aer-7_i, respectively. The NOx setpoint depends on the temperature and the aeration control strategy tested:
12 C: o Dosage 1 setpoint = 1.5 mg NO x -N/L o Dosage 2 setpoint = 1.8-2. mg NO x -N/L (depending on control strategy) 2 C: o Dosage 1 setpoint = 1.5 mg NO x -N/L o Dosage 2 setpoint = 2.7-3.3 mg NO x -N/L (depending on control strategy) 3 C: o Dosage 1 setpoint = 1.5 mg NO x -N/L (2 mg NO x -N/L in strategy 2b with a reduced base flow rate of 32.8 L/d (8 gal/d)) o Dosage 2 setpoint = 3.1/3.5 mg NO x -N/L ( strategies 2b and 3b have a reduced base flow rate of 189.3 L/d (5 gal/d)) As discussed above, control of the internal recycle rate (NRCY) was not part of this study and was set to 2% of the influent flow rate. The RAS flow rate (CRCY) was set to 5% of the influent flow. Aeration control strategies: The following aeration control strategies were evaluated. For more details on probe locations and control loops see Table 7. Base case: Strategy 1: Strategy 2a: Strategy 2b: Strategy 3a: Strategy 3b: Strategy : Existing strategy DO probes moved to AAA_F and Aer-7_bc Ammonia control PID with DO setpoint.5-2 mgdo/l 2a but DO setpoint -2 mgdo/l Ammonia control on-off with DO setpoint.5/2 mgdo/l 3a but DO setpoint /2 mgdo/l Feed-forward/feed-back ammonia control strategies 2- were based on the DO probe locations of control strategy 1 (moved to the second reactor of aeration zones 1 and 2).
Table 7. Simulated aeration control strategies. # strategies Setpoints Comments Base case for study DO control DO set-points: Zone 1 = 2.5 mg/l (DO probe in AAA_E) Zone 2 = 2 mg/l(do probe in Aer-7_a) Zone 3 = 1 mg/l 1 DO control DO set-points: Zone 1 = 2.5 mg/l (DO probe in AAA_F) Zone 2 = 2 mg/l(do probe in Aer-7_bc) Zone 3 = 1 mg/l 2a 2b 3a 3b NH FB PID controller manipulates DO setpoints strategy 2a but with no lower limit for airflow NH FB high/low controllers manipulate DO setpoints strategy 3a but with no lower limit for airflow NH FF + FB controller combined through max condition on top of DO control loops Zone 1: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between.5 2.5 mg/l Zone 2: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between.5 2.5 mg/l Zone 3: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between.5 2.5 mg/l Zone 1: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between.5 2.5 mg/l Zone 2: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between.5 2.5 mg/l Zone 3: NH,AER-7_fg setpoint = 1 mg/l => DO set-points between 2.5 mg/l Zone 1: Zone 2: Zone 3: NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point =.5 mg/l NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point =.5 mg/l NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point =.5 mg/l Zone 1: NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point = mg/l Zone 2: NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point = mg/l Zone 3: NH > 1.2 mg/l => DO set-point = 2 mg/l NH <.8 mg/l => DO set-point = mg/l Full description is given below DO probes moved into middle of zones 1&2 PI controller for DO; PID ammonia controller changes DO setpoints No lower air flow limit PI controller for DO; high-low ammonia controller changes DO setpoints PI controller for DO; on-off ammonia controller changes DO setpoints Feed-forward ammonia controller: On the basis of an additional ammonia sensor in the middle of the anoxic zone (AAA_C) and a flow measurement, the feed-forward controller calculates the time when increased aeration is necessary. A simplified model compares the incoming ammonia load with an estimated nitrification capacity (Eq. 1). where: nit state = ratio of influent NH -N load to estimated nitrification capacity L NH,Inf = Measured NH -N influent load [g/d] r nit = Nitrification rate [g/(m 3 *d)] Vol BR = Volume aerated bio-reactors [m 3 ] (Eq. 1)
The nitrification rate is estimated according to Eq. 2 for the base aeration strategy with DO concentration of 2 mg/l. (Eq. 2) where: X AOO = Concentration of ammonia oxidizing organisms (from steady state and low aeration strategy, see Eq. 3) [g/m 3 ] max,t = Actual max. growth rate at given temp. = max,2 * exp(.15*(t-2 C)) [1/d] max,2 =.75 [1/d] (max. growth rate at 2 C) Y AOO =.2 [g COD/g N] (autotrophic yield) T = Actual temperature [ C] The concentration of nitrifiers depends on the mean ammonia load and removal rate: where: L NH,removed = Mean NH -N influent load NH feed-back controller set-point [g/d] SRT = Sludge retention time [d] b AOO,T = Max. decay rate at given temp. = b AOO,2 * exp(.15*(t-2)) [1/d] b AOO,2 =.2 [1/d] (Max. decay rate at 2 C) (Eq. 3) To simplify the modeled controller, the X AOO concentration was calculated upfront for the three different temperatures and SRTs used (12, 2, and 3 C): Table 8. Estimated and simulated (steady-state in BioWin) concentrations of Ammonia Oxidizing Organisms (AOO). Temperature and SRT 12 C SRT=18.2 d 2 C SRT=12 d 3 C SRT=8 d Calculated X AOO conc. [mg COD/L] (Eq. 3) 28.5 21.5 15.5 BioWin steady-state X AOO conc. [mg COD/L] 29 21.5 16 An on-off controller is used to switch the DO setpoints of all three aeration zones to 2.5 mg/l if the load is higher than a specified nitrification capacity (nit state ). The switching criteria can be used to tune the controller. Table 9 shows the used switching criteria and setpoints. Table 9. Switching criteria and DO setpoints for feed-forward ammonia controller. DO setpoints [mg DO/L] Switching criteria Aeration zone 1 Aeration zone 2 Aeration zone 3 Increasing nit state > 1. 2.5 2.5 2.5 Decreasing nit state <.8... In this control strategy, the NH probe was located at the middle of the first anoxic zone. If the probe is moved to the influent, it may become necessary to introduce a delay function for the measured variable to account for the hydraulic retention time in the anaerobic and anoxic zones. The combination of feed-forward and feed-back was implemented by a selector that chooses the controller with the higher air demand (Figure 1). This means that when an ammonia peak enters the plant, the feed-forward probe will measure it first and the feed-forward controller will ask for
increased aeration. Since the feed-back probe does not yet see the peak, it still demands low aeration. The selector switches to the feed-forward controller when the nit state > 1. (i.e. the DO setpoint adjusts to 2.5 mg/l). When the nitrification capacity is estimated as sufficient (i.e. nit state <.8) the feed-forward controller demands no aeration (DO setpoints of. mg/l) and therefore the selector will switch back to the feed-back controller. The model to predict the nitrification capacity does not need to be very accurate because the feed-back controller will guarantee sufficient aeration under non-peak conditions. A further simplification would be to set the concentration of nitrifiers (for instance based on steady-state calculations with BioWin) according to different seasons. In addition, the switching criteria of the on-off controller (for the ratio ammonia load/nitrification capacity) can be used to tune how fast the feed-forward controller becomes active. Feed-forward control (NH load) DO setpoint Selector (max condition) DO setpoints DO setpoint Feed-back control (NH conc.) Measured variable M valve ler DO controller 1 DO controller 2 DO controller 3 Signal line Air flow Pressure control Q NH O 2 O 2 M M M O 2 P Blowers NH Aeration Zone 1 Aeration Zone 2 Aeration Zone 3 Figure 1. scheme strategy (combination of ammonia feed-forward and feed-back manipulating the DO setpoints of the 3 DO controllers). RESULTS AND DISCUSSIONS This section discusses different aspects of the evaluated control strategies with respect to nutrient removal and resulting air flows with the related energy consumption. A separate section discusses aeration system constraints such as blower capacity and mixing requirements. The last section discusses feed-forward versus feed-back control strategies. Nutrient Removal Nitrification: A first evaluation identified optimal DO probe locations in each aerated zone to ensure sufficient DO throughout the reactors under dynamic loading. Moving the DO probes into tanks AAA_F (instead of AAA_E) and Aer-7_bc (instead of Aer-7_a) showed the best results. The DO controllers with relocated probes (strategy 1) were used in all following scenarios. Figure 11 shows the impact of moving the DO probe in aeration zone 1 from the first (solid lines) into the second reactor (dotted lines). Since the number of diffusers and tank volume is the same in both reactors, controlling the DO concentration in the second reactor reduces the overaeration in AAA_F. With a setpoint of 2.5 in the second reactor (AAA_F) a sufficiently high DO concentration in the first reactor (AAA_E) can be maintained in all temperature scenarios (not shown).
DO[mg/L] DO[mg/L] DO[mg/L] DO[mg/L] 5.. 3. 2. 1.. 5.. 3. 2. 1. Base AAA_E C1 AAA_E Base AAA_F C1 AAA_F. 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 11. DO concentrations in aeration zone 1 at 12 C for base case compared to control strategy 1. In aeration zone 2 the DO probe should be moved to the second reactor to guarantee sufficient provision of oxygen. Figure 12 shows low DO concentrations for reactors Aer-7_bc and Aer- 7_de when the DO probe is located in the first reactor (Aer-7_a). Moving the probe into the second reactor (Aer-7_bc) results in sufficiently high oxygen levels in all tanks. At higher temperatures the base scenario shows over-aeration in the rear reactors. strategy 1 results in improved DO levels (Figure 13). 5.. 3. 2. 1.. 5.. 3. 2. 1. Base Aer-7_a Base Aer-7_bc Base Aer-7_de C1 Aer-7_a C1 Aer-7_bc C1 Aer-7_de. 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 12. DO concentrations in aeration zone 2 at 12 C for base case (DO probe in Aer- 7_a) compared to control strategy 1 (DO probe in Aer-7_bc).
TN [mg N/L] DO[mg/L] DO[mg/L] 5.. 3. 2. 1.. 5.. 3. 2. Base Aer-7_a Base Aer-7_bc Base Aer-7_de 1.. C1 Aer-7_a C1 Aer-7_bc C1 Aer-7_de 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 13. DO concentrations in aeration zone 2 at 3 C for base case (DO probe in Aer- 7_a) compared to control strategy 1 (DO probe in Aer-7_bc). Denitrification and total nitrogen removal: The improvement in denitrification through ammonia control ( strategies 2-) is not directly visible as the external carbon dosage (methanol) was controlled to exactly meet the effluent limit for TN concentrations (Figure 1). Instead, the savings in methanol dosage should be evaluated. Figure 15a-c shows the dosage rates for the tested control strategies at different temperatures. The bars in these figures show the total dosage rates, with the rates at each dosage point as indicated by the shading within the overall bar. Effluent TN 8. 7. 6. 5.. 3. 2. 1.. 12 deg C 2 deg C 3 deg C Figure 1. TN concentrations in the effluent for different control strategies and temperatures.
a) 12 C b) 2 C c) 3 C Base 1 2a 2b 3a 3b Base 1 2a 2b 3a 3b Base 1 2a 2b 3a 3b Methanol dosage AAA Methanol dosage Aer-7 2 6 8 1, 1,2 1, gal/d 2 6 8 1, 1,2 1, gal/d Methanol dosage AAA Methanol dosage Aer-7 2 6 8 1, 1,2 1, gal/d Methanol dosage AAA Methanol dosage Aer-7 Figure 15. Methanol dosage for different control strategies at a) 12 C, b) 2 C, and c) 3 C. Figure 15 shows that only minor reductions can be achieved at 12 C indicating that the total aerated volume is required to nitrify. This is supported by the fact that the ammonia feed-back controller demands reduced aeration most of the time at 2 and 3 C. At 2 C the saving potential when introducing ammonia control is significant; this is even more pronounced at 3 C. The control strategies without a lower DO setpoint limit reach the largest saving potentials due to the best conditions for denitrification with the lowest presence of oxygen. Ammonia-based control ( strategies 2-) resulted in significant aeration savings (see below). However, the reduction in external carbon dosage (methanol) was even more significant; the best scenarios showed a reduction in methanol use of over 5% (Figure 16). Ammonia control essentially results in larger anoxic zones, more denitrification using influent carbon, and a part of the targeted TN effluent limit is ammonia; hence less nitrate must be denitrified.
TP [mg P/L] Methanol savings [%] Percentage methanol savings 6 5 3 2 1 12 deg C 2 deg C 3 deg C 1 2a 2b 3a 3b Figure 16. Methanol savings for different control strategies and temperatures. Enhanced biological phosphorus removal: Optimization of bio-p performance was not a main objective of this study and performance was only checked for potential negative impacts of the aeration control strategies. The results (Figure 17) showed reduced effluent TP at higher temperatures but no significant impact for the different control strategies. Effluent TP.8.7.6.5..3.2.1. 12 deg C 2 deg C 3 deg C Figure 17. Average effluent TP concentrations for different control strategies and temperatures. The model results should be evaluated with care as the model was not calibrated in detail. From the results, it is expected that low DO should not have an impact on the overall P uptake performance as the volume (aerated zones and secondary anoxic zone) should be sufficient even at reduced anoxic P-uptake rates. However, there is a danger of secondary P release when adding carbon other than methanol. Phosphorous accumulating organisms may compete with other heterotrophic organisms for the carbon source leading to some P release. The P uptake may then not be sufficient to meet the effluent limits under un-aerated conditions. Further evaluations will be carried out in the next project phases.
Nitrite [mg N/L] Nitrite accumulation: One of HRSD s concerns about introducing ammonia control was that the resulting low DO concentrations may lead to significant accumulation of nitrite in the system with potential impacts on bio-p and possible elevated nitrite concentrations in the effluent. Figure 18 shows simulation results for the different control strategies and temperatures (flow weighted averages from dynamic simulations). In agreement with current theory, the control strategies without lower air flow limits or DO limits (2b, 3b, and ) show elevated nitrite of up to 2.5 mg N/L, however, not at low temperatures (12 C). Although there is concern that elevated secondary effluent nitrite may be generated due to partial nitrification, if the nitrite is fully denitrified in the second anoxic zone, this would not impact chlorination. Furthermore, if the control system is capable of retaining ~1 mg/l of ammonia in the secondary effluent, this would alleviate chlorination problems caused by elevated nitrite. These possibilities will be evaluated during the full-scale testing program and nitrite-specific online instrumentation. 3. 2.5 Effluent Nitrite 2. 1.5 1..5. 12 deg C 2 deg C 3 deg C Figure 18. Average effluent nitrite concentrations for different control strategies and temperatures. Air flow and energy consumption In addition to improved TN removal through control, the second control goal was to reduce aeration energy consumption at Nansemond. The results (Figure 19) show reduction potentials of approximately 5% at 12 C, 2% at 2 C, and over 3% at 3 C.
Airflow [scfm] Energysavings [%] Percentage energy savings 35 3 25 2 12 deg C 15 2 deg C 1 3 deg C 5 1 2a 2b 3a 3b Figure 19. Energy savings for different control strategies and temperatures. System constraints Blower capacity: The simulation results indicate sufficient blower capacity for all scenarios. However, the evaluation clearly shows that the minimum turn-down of the blowers becomes a limiting factor, and even more so when ammonia control is introduced. Mixing requirements: Assuming a theoretical air flow requirement for mixing of.12 scfm/ft 2 (e.g. USEPA, 1989 for resuspending settled sludge), none of the control strategies can guarantee the minimum air flow for mixing in aeration zone 3 (Figure 2). When ammonia control is applied, even aeration zone 1 with the highest air flow drops below the threshold. 5,,5 Air flow Aer_fg Base Air flow Aer_fg 1 Air flow Aer_fg 2a Air flow Aer_fg 2b, Air flow Aer_fg 3a Air flow Aer_fg 3b 3,5 Air flow Aer_fg Min mixing Aer_fg 3, 2,5 2, 1,5 1, 5 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 2. Air flows to aeration zone 3 for different control strategies and minimum air flow requirements for mixing (red dotted line) (at 3 C). Channel mixing: With the current operational settings, a big part of the overall air flow is used for channel mixing; about 27% of the total air flow in control strategy 1 at 2 C and about 36% in control strategy 2b (with ammonia control) (see Figure 21 and Figure 22). Using the correlation for kwh per scfm as determined for blower #2 and an energy cost of $.63/kWh this results in mixing costs of $5, to $8, per year. Figure 23 shows the costs for channel
$ per year mixing calculated by subtracting the fixed air flow into the Aeration Influent Channel and the Re-aeration Channel from the simulated total airflow for different control strategies and temperatures. Based on these results, experiments to find more efficient ways for mixing have been started. Aeration Inf. Channel 13% Air flow strategy 1 Re-aeration Channel 1% Aeration zone 1 23% Air flow strategy 2b Re-aeration Channel 19% Aeration zone 1 16% Aeration zone 3 5% Aeration zone 2 5% Aeration Inf. Channel 17% Aeration zone 3 1% Aeration zone 2 38% Figure 21. Air flow distribution strategy 1 (2 C). Figure 22. Air flow distribution strategy 2b (2 C). Mixing costs 9, 8, 7, 6, 5,, 3, 2, 1, 12 deg C 2 deg C 3 deg C Figure 23. Annual costs for mixing based on measured efficiency of blower #2. Air flow per diffuser: Minimum and maximum air flow per diffuser is another design criterion for aeration systems. Figure 25 and Figure 26 include suggested manufacturer specifications. These are recommended ranges to ensure optimum efficiency and should not be seen as hard limits. However, concerns have been raised that permanently operating below the lower limit may result in increased diffuser fouling and loss of oxygen transfer efficiency over time. This would reduce the energy savings of the control strategies. The higher limit in air flow per diffuser was never reached in the simulated scenarios, but may also impact diffuser efficiency if permanently violated. Interestingly, the ammonia-based control strategies should improve the air flow per diffuser ratio in zone 3 because a certain amount of ammonia is maintained at the end of the aerated zone. Pure
Airflow/difuser [scfm] Airflow/difuser [scfm] NH conc. [mg/l] DO control strategies result in a sudden drop in oxygen requirements because ammonia removal is complete by zones one or two (Figure 2) and consequently very low air flows are needed in the last aeration zone (compare Figure 25 and Figure 26). 1 8 6 2 NH Aer-7_a NH Aer-7_de NH Aer-7_bc NH Aer-7_fg. 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 2. Ammonia concentration profile throughout aerated reactors for base case at 2 C. 5 3 2 2 deg C: strategy 1 Aeration zone 1 Aeration zone 2 Aeration zone 3 Min airflow/diffuser Max airflow/diffuser 1 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 25. Air flow per diffuser for control strategy 1 at 2 C. 5 3 2 2 deg C: strategy 2a Aeration zone 1 Aeration zone 2 Aeration zone 3 Min airflow/diffuser Max airflow/diffuser 1 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug 8 Aug Figure 26. Air flow per diffuser for control strategy 2a at 2 C.
DO [mg/l] DO [mg/l] DO [mg/l] Feed-forward vs. feed-back To analyze potential benefits of ammonia feed-forward control, control strategy included a feed-forward controller to account for influent ammonia peaks. Under normal dry weather loading conditions, ammonia feed-forward control was only active at very low temperatures (Figure 27 active feed-forward control visible as DO concentrations of 2.5 mg/l) but when active it had very limited impact on effluent concentrations (Figure 28). 3 Dry weather 12 C strategy 2b (FB) strategy (FF+FB) 2 1 3 Dry weather 2 C strategy 2b (FB) strategy (FF+FB) 2 1 3 Dry weather 3 C strategy 2b (FB) strategy (FF+FB) 2 1 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug Figure 27. Comparison of DO levels for control strategy 2b (Feed-back) and (Feedforward+Feed-back) at different temperatures.
NH -N / DO [mg/l] NH -N [mg/l] 5 Dry weather 12 C NH: Strategy 2b (FB) NH: Strategy (FF+FB) 3 2 1 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug Figure 28. Ammonia concentrations in last aerated reactor for control strategy 2b (Feedback) and (Feed-forward+Feed-back) at 12 C. Figure 29 shows a test with an artificial ammonia peak created to analyse the feed-forward controller performance. As can be seen, the feed-forward controller (ler ) only achieved a minor reduction of the effluent ammonia peak by about.3 mg NH -N/L relative to ammonia feedback control (ler 2b). 5 3 Dry weather 12 C + ammonia peak Feed-back control: DO Feed-back control: NH Feed-forward+Feed-back control: DO Feed-forward+Feed-back contro: NH 2 1 1 Aug 2 Aug 3 Aug Aug 5 Aug 6 Aug 7 Aug Figure 29. Ammonia in the last aerated reactor and DO throughout the aerated zones to compare Feed-back control against Feed-forward+Feed-back control. An artificial influent ammonia peak was created to test the feed-forward controller performance. These results are in accordance with other projects (Rieger et al., 212) and can be explained by the limited control authority of feed-forward ammonia controllers: Conversion of ammonia to nitrate requires sufficient dissolved oxygen (DO), ammonia as substrate, a number of nutrients, nitrifying biomass(es), and a sufficiently long aerobic sludge retention time to avoid washout of nitrifiers. Optimal DO concentrations are approximately 2-2.5 mg/l; higher concentrations will not further improve nitrification kinetics. Therefore ammonia controllers should be used in conjunction with DO control to prevent over-aeration at high ammonia concentrations.
Cost savings [$/year] The limited control authority of ammonia FF controllers is different from controlling say denitrification through adding external carbon, where the denitrification essentially is linked to carbon addition directly. With ammonia control, increasing aeration to account for increased loading may help, but the nitrification capacity often is limited by the concentration of nitrifiers. The impact of an NH feed-forward controller is limited to increasing aeration earlier than a feed-back controller and in this way (i) lower the ammonia concentration in the reactor to a minimum providing dilution for the arriving ammonia peak, or (ii) prevent the DO concentration from dropping to low levels because of a slow reaction of the feed-back DO controller. COST SAVINGS Figure 3 shows the potential savings related to reduced dosage of methanol with ammoniabased aeration control (compared to control strategy 1). The calculations are based on methanol costs of $1.539/gal. The results show reduction potentials of approximately $6, 8,/year at 12 C, $155, 31,/year at 2 C, and $1, 31,/year at 3 C. The lower savings potentials at 3 C compared to 2 C for control strategies 2a and 3a are due to the already high denitrification rate in the base scenario at 3 C. Cost savings Methanol, 35, 3, 25, 2, 15, 1, 5, 1 2a 2b 3a 3b 12 deg C 2 deg C 3 deg C Figure 3. Methanol cost savings for different control strategies and temperatures. Figure 31 shows the calculated cost savings based on a correlation between air flow and kwh for blower #2 and an energy price of $.63/kWh. The results show reduction potentials of approximately $1, 2,/year at 12 C, $5, 67,/year at 2 C, and $6, 9,/year at 3 C. The on-off ammonia controllers (C2a and b and C) show lower reduction potentials than the controllers using a PID controller (C3a and b). This is due to the fact that the blowers are being switched on-and-off at short intervals leading to less efficient blower operation and due to the limited output settings to over-aeration when ammonia is slightly elevated.
Cost savings [$/year] Cost savings Energy 1, 9, 8, 7, 6, 5,, 3, 2, 1, 1 2a 2b 3a 3b 12 deg C 2 deg C 3 deg C Figure 31. Energy cost savings for different control strategies and temperatures. CONCLUSIONS Selection of the optimal controller is a multi-criteria decision and should take into account: Effluent quality Greenhouse gas emissions Savings in energy consumption Savings in methanol (or other external carbon) Equipment wear and tear Energy consumption peaks Complexity of the control strategy Investment and O&M costs In terms of effluent quality and savings in methanol, control strategy 3b (ammonia on-off control) seems to be the optimal one. However, the results show slightly higher average energy consumption and also some extreme peaks in power uptake. This may lead to a higher energy price per kwh if peak demand charges apply. Taking the additional hardship on the equipment into account (e.g. start-stop of blowers, valve positioning), control strategy 2b (ammonia PID control) may be the preferred option with only slightly lower performance. A major question is whether the control strategies 2b and 3b should be ruled out because of their potential to create elevated nitrite concentrations; this may lead to increased emissions of nitrous oxide. It should be noted that the on-off control strategies reduce the nitrite concentrations. Comparing the scenarios 2a/3a with 2b/3b shows that introducing lower limits for DO (.5 mg/l) reduced nitrite concentrations to insignificant levels. The feed-forward control evaluated in this study is only active at lower temperatures (12 C scenario) and only reduce ammonia effluent peaks marginally. Further studies should analyse whether the hypothetically created ammonia peak is realistic or if steeper and higher peaks
occur. Based on the results of this study, the marginal benefits from feed-forward control seem not to justify additional investment and O&M costs. The simulation study demonstrated significant savings potential with ammonia- and/or DO-based aeration control strategies. Additional insights for the full-scale implementation were gained by modeling equipment constraints such as blower capacity, mixing requirements, and air flow per diffuser. The next step is to transfer the experience from the simulations to the real plant. REFERENCES EnviroSim (25). Nansemond Treatment Plant - BioWin Calibration Report. Prepared for Hampton Roads Sanitation District, January 25. Fujie, K.; Sekizawa, T.; Kubota, H. (1983) Liquid mixing in activated sludge aeration tank. J. Ferment. Technol., 61(3), 295 3. Rieger, L.; Takács, I.; Siegrist, H. (212) Improving nutrient removal while reducing energy use at three Swiss WWTPs using advanced control. Water Environ. Res., 8(2), 171-189. USEPA (U.S. Environmental Protection Agency, Office of Research and Development) (1989) Design Manual: Fine Pore Aeration Systems. EPA/625/1-89/23. U.S. E.P.A., Cincinnati, OH, USA.