Optimized biological nitrogen removal of high-strength ammonium wastewater by activated sludge modeling

Wastewater containing high ammonium concentrations is produced from various industrial activities. In this study, the author used a complex activated sludge model, improved by utilizing BioWin © (EnviroSim, Hamilton, Canada) simulation software, to gain understanding of the problem of instability in biological nitrogen removal (BNR). Specifically, the study focused on BNR in an industrial wastewater treatment plant that receives high-strength ammonium wastewater. Using the data obtained from a nine-day sampling campaign and routinely measured data, the model was successfully calibrated and validated, with modifications to the sensitive stoichiometric and kinetic parameters. Subsequently, the calibrated model was employed to study various operating conditions in order to optimize the BNR. These operating conditions include alkalinity addition, sludge retention time, and the COD/N ratio. The addition of a stripping step and modifications to the configuration of the aerators are suggested by the author to increase the COD/N ratio and therefore enhance denitrification. It was found that the calibrated model could successfully represent and optimize the treatment of the high-strength ammonium wastewater.


INTRODUCTION
Wastewater containing high-ammonium concentrations is produced from various industrial activities, such as the petrochemical, steel manufacturing, pharmaceutical, fertilizer, and food industries (Carrera et

MATERIALS AND METHODS
An industrial WWTP, located in Helwan, Egypt, was used in the case study. This facility, in operation since 1989, receives high-strength ammonium wastewater from a coke-oven plant in the vicinity. The WWTP was designed for organic and nitrogen removal and employs the oxidation ditches process.
Recently, operational problems have been encountered in this plant relevant to nitrogen removal because the total nitrogen level does not meet the Egyptian standard for industrial effluents. Therefore, the process had to be optimized for BNR. The author used the BioWin built-in model to simulate and optimize the process performance of the plant. The study was conducted according to the 'Good Modeling Practice' protocol which was developed by Rieger et al. (). The BioWin software facilitates analysis of the effects of various operational parameters (such as SRT, HRT, and the like) on the biological treatment performance.
The first step in the study was to understand the plant configurations. The staff of the WWTP provided us with operational and technical data relating to the WWTP's performance. The plant includes an equalization lagoon with a total volume of 81,600 m 3 , a buffering tank, two oxidation ditch modules with a total volume of 7,000 m 3 , and two secondary clarifiers with a total volume of 3,200 m 3 . A simplified schematic diagram of the industrial WWTP is presented in Figure 1. The industrial wastewater, which has an average flow rate of 3,680 m 3 /d, flows into the equalization lagoon and is subsequently pumped to the WWTP at a semi-constant rate. The wastewater from the administration buildings of an industrial facility, with an average flow of 480 m 3 /d, is pumped in at the inlet of the biological reactors with the industrial wastewater flow to compensate for the lack of nutrients encountered in this type of industrial wastewater.
Because of the wide variations of pH in the influent, the pH level is observed and controlled before the oxidation ditches. pH control includes lime and phosphoric acid addition. The return sludge (Q return ) is pumped to the inlet of the biological step and mixed with influent. The excess sludge (Q ex ) is drawn out to be dried. Three brush aerators are used to aerate each of the oxidation ditches. Each brush aerator has a constant power supply of 45 kW. Finally, the treated water is recycled and reused in the coke oven process, while the unneeded water is disposed of at a wastewater treatment facility. Operational data and measurements dating back to the start of the operation of the WWTP were obtained from the plant. The raw sewage temperature was 16-32 W C, with an annual average of   Table 1. The characteristics of the mixed influent wastewater   for the parameter, and N is the number of observations.

RESULTS AND DISCUSSION WWTP current performance
While evaluating the present condition of the Helwan WWTP (Table 1)  The system of oxidation ditches functions well with high ammonium wastewater and wastewater that contains toxic substances. The dilution of the influent wastewater by the recycled stream of water reduces the inhibition effect, and flexible aeration could help the nitrification-denitrification process.

Model calibration and validation
At the start of the experiment, it was difficult for the model to reach equilibrium for such high-ammonium wastewater. This

Optimization of the current situation
The current status of the WWTP is that a high ammonia concentration of about 515 mg/L is present in the effluent.
Nitrite is accumulated at 23 mg/L as NOB are probably partially inhibited by the high ammonia concentration. No nitrate is present in the effluent, which means that all nitrate was denitrified. The residual alkalinity and pH in the effluent were 80 mg/L as CaCO 3 and 7.13, respectively. The optimization strategy for nitrogen removal was to study the alternative operational parameters that exert the most influence on nitrogen removal. The optimization was done in several steps and the effluent quality after each step is included in Table 3. technique. The other alternative is using pre-denitrification, which requires creating an anoxic zone at the inlet of the treatment process, a suitable COD/N ratio, and recirculation for nitrate. Oxidation ditches sustain the required recirculation for the pre-denitrification process. The current aerator configuration in the WWTP is not suitable for predenitrification as the pre-anoxic zone is small. In order to increase the pre-denitrification rate, the volume of the anoxic zone at the beginning of the oxidation ditches had to be increased. This could be done by moving the brush aerator from reactor T2 to reactor T9 (compare Figures 2   and 4). By performing this simple modification in the aerator configuration, the pre-denitrification zone was increased from 5 to 40% of the total tank volume. As shown in Table 3, the nitrification rate was increased slightly as the  ammonia content in the effluent decreased to 508 mg/L. Consequently, residual alkalinity and pH decreased to 29.5 mg/L and 6.6, respectively. The pH is below its optimum value for nitrification, which is between 7.5 and 8.0. A minimum residual alkalinity of 50-100 mg/L is required to ensure adequate buffering. The next optimization step should be optimizing the buffering capacity.
Taking into consideration the organic and inorganic constituents in the coke-oven wastewater in this study, an inadequate amount of inorganic carbon is usually the first factor that leads to the instability of the nitrification process (Anthonisen et al. ). Alkalinity in water determines the availability of inorganic carbon needed for the metabolism of nitrifying bacteria. The required alkalinity for the removal of 1 mg of ammonia is approximately 7.14 mg as CaCO 3 .
Mixed influent wastewater has a TKN value of about 823 mg/L (Table 1), which stoichiometrically needs about 5,877 mg/L of CaCO 3 for complete nitrification. This alkalinity is much more than the 560 mg/L that the mixed influent wastewater contains (Table 1). Lack of alkalinity could cause inhibition of the nitrification process (Carrera et al. ). Increasing the alkalinity by adding bicarbonates or carbonates as external sources of alkalinity could be needed for the stability of the nitrification process. It should be noted that the added alkalinity will increase the operating cost dramatically. Denitrification will reduce the required alkalinity as 3.57 mg/L of alkali is produced per 1 mg/L of NO 3 -N nitrified. Thus, performing simultaneous nitrification and denitrification will reduce the alkalinity needed for complete nitrification by half.
In order to optimize the buffering capacity needed for the nitrification process, the alkalinity was increased stepwise by adding lime to the influent raw wastewater.
The alkalinity was increased as shown in Table 3 from an existing value of 562 mg/L as CaCO 3 to 2,440 mg/L as CaCO 3 . At that level of added lime, the influent pH increased to 10.8, as shown in Table 3. Lime addition after that point can increase the operation cost dramatically.
Moreover, it could be difficult to dissolve carbonates and bicarbonates in the wastewater completely at these target concentrations. With such a high pH value in the influent, ammonia stripping could be an economical option. This will be discussed next. During the increase in alkalinity, the software notifies the user that a limitation in the phosphorus concentrations required for the biological process has occurred. To fix this, the phosphorus concentration in the mixed influent wastewater was increased from its current value of 6.23 to 8 mg/L. Phosphorus is an important factor in the growth of the bacteria. Therefore, phosphorus has to be added at the same rate as the alkalinity is increased, else the bacterial growth would be constrained. At that optimization level, the pH in the influent became 10.8. Most of the ammonia is converted to FA between pH 10.8 and 11.5, which is advantageous to the stripping process.
Ammonia stripping will remove a large portion of the FA existing in the influent and reduce its inhibitory effect on the nitrification process. The ammonia stripping process is extremely effective for treating high-strength ammonium (Maranon et al. ). Adding an ammonia stripping unit would be feasible and less costly, taking into account that the influent already has a high free-ammonia content. In addition, ammonia stripping would facilitate alteration of the COD/N ratio, which is one of the most effective parameters in nitrogen treatment, relevant to nitrification and denitrification for high-strength ammonium wastewater. For effective nitrification, the COD/N ratio should be kept low to minimize the competition between autotrophic and heterotrophic bacteria. However, the influence of this ratio might be lower in BNR systems than conventional activated sludge systems (Carrera et al. , ). As in BNR systems, most of the COD is removed in the pre-denitrification zone, which minimizes the competition between autotrophic and heterotrophic bacteria in the aerobic zone. As regards predenitrification, a higher COD/N ratio is preferred and stoichiometrically should be more than 4.2 in order to realize the full BNR (Carrera et al. ). Such a COD/N ratio is not common in this type of wastewater. In the WWTP used in the current study, the actual COD/N ratio was 1.43.
Benefiting from the high pH in the influent wastewater, a stripping unit was added to the industrial wastewater stream ( Figure 4). There is no built-in ammonia stripper in the BioWin software. However, the ammonia stripping process is represented in the BioWin model. Therefore, the ammonia stripper was represented as an aerated reactor with a high airflow rate. and 90%) on the effluent quality were tested, as shown in Table 3. Ammonia stripping would facilitate reducing ammonia contents, which enables the system to remove the rest of the ammonia and organic nitrogen biologically.
As shown in Table 3, the nitrification process improved significantly as a result of using the ammonia stripping unit.
Ammonia stripping at only 80% efficiency was efficient in reducing ammonia concentration in the effluent to below 3 mg/L. No nitrite was accumulated when using ammonia stripping, which means no inhibition of NOB occurred.
The pH in the reactors was about 7.6, which is optimal for the nitrification process. Low nitrate was found in the effluent, which decreases as the efficiency of ammonia stripping increases. This is to be expected as COD/N was optimal for denitrification and varied between 4 and 5.1 for ammonia stripping efficiencies between 80 and 90%. Phosphorus was reduced to its initial value of 6.23 mg/L with no problem as the nitrification rate was reduced by using ammonia stripping.
The disadvantage of the ammonia stripping process is that the ammonia pollutant was transferred from the water to the air (EPA ); consequently, it is recommended that the stripped ammonia be recovered by employing a closed loop. In a closed loop, the air is sent to an absorber where concentrated ammonium sulfate is formed. Then, the clean air is recycled back to the ammonia stripper without emitting ammonia into the air.
As a final step in the optimization study, the effect of different SRT values on WWTP performance was tested.
The SRT could be a crucial optimization variable, as the nitrifying bacteria are extremely sensitive and require a considerable time for growth (Kim et al. ). To study the effect of the SRT, a stepwise modification of SRT between 30 and 60 days was performed, the output of which is presented in After all these optimization steps, the plant effluent was found to be acceptable according to the specifications of Egypt for industrial wastewater discharged into municipal wastewater networks. The final configuration of the optimized industrial WWTP is shown in Figure 4. The performance of the plant was optimized for nitrogen with all the previously mentioned alterations. The optimization was done by applying the following modifications: • increasing the pre-anoxia by moving the brush aerator from reactor T2 to reactor T9 to increase the pre-denitrification capacity; • increasing the alkalinity to 2,440 mg/L as CaCO 3 from its current value of 562 mg/L by adding lime to the industrial wastewater; • using an ammonia stripping unit with a minimum efficiency of 80% for the industrial wastewater, benefiting from the high pH due the added alkalinity; • SRT to be kept high (between 40 and 50 days) for such types of industrial wastewater.

CONCLUSIONS
In this study, the BioWin model was calibrated and vali-