Optimization of A2O Process by Changing Anoxic Tank Volume Based on GPS-X Simulation

Optimization of A2O Process by Changing Anoxic Tank Volume Based on GPS-X Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization of A2O Process by Changing Anoxic Tank Volume Based on GPS-X Simulation Shuhan Lei, Jianqiang Zhao, Junkai Zhao, Shuting Xie, Ju Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2058607/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract In order to improve the nitrogen removal efficiency of the anaerobic-anoxic-aerobic process (A2O), the effect of anoxic tank volume on the total nitrogen (TN) removal efficiency and total phosphorus (TP) removal efficiency were investigated under different ratios of chemical oxygen demand (COD) to nitrogen (C/N). The results showed that under C/N=7, the HRT of anoxic tank increased from 1.8 h to 3.6 h had little effects on the removal efficiency of COD, but largely improving the removal efficiency of TN and TP which increased from 66.8% and 93.8% to72.0% and 95.2%, respectively. Under C/N=10, increasing the volume of the anoxic tank did not significantly improve the removal efficiency of COD, TN and TP. The optimal HRTs of anoxic tank and the corresponding volume ratios of anaerobic, anoxic and aerobic were 4.0 h, 4.3 h and 1:2.5:6.75, 1:2.69:6.75 simulated by GPS-X software under C/N=7 and C/N=10, respectively. The results proposed that the appropriate elevated the volume of the anoxic tank could help A2O process to meet the TN discharge concentration lower than 15 mg/L. Under low C/N, changing the HRT of anoxic tank to improve the pollutant removal efficiency of A2O reactor was more significantly, so this method was more suitable for the case of insufficient carbon source. A2O process anoxic tank HRT GPS-X optimal HRTs Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights Increased anoxic tank volume can improve the pollutant removal efficiency of A 2 O. Six critical parameters were found with the sensitivity analysis and screening. The developed GPS-X model predicts the effect of changing A 2 O conditions. The optimal HRTs of anoxic tank were 4.0 h under C/N=7. 1 Introduction Anaerobic-anoxic-aerobic process (AAO), also known as A 2 O process, is a traditional biological nitrification, denitrification and phosphorus removal wastewater treatment technology, and was widely applied in wastewater treatment plants (WWTPs) (Jia et al., Tian and Iop, 2019, Wang et al., 2019). However, the current A 2 O process also had certain defects and some improvements still need to be improved. For example, the competition between aerobic phosphate accumulating organisms (PAO) and denitrifying bacteria for carbon source becomes more intense under low ratios of chemical oxygen demand (COD) to nitrogen (C/N), and the anaerobic environment of anaerobic phosphorus release is influenced by nitrate in sludge mixture reflux(Tian and Iop, 2019). These phenomena often lead to the effluent total nitrogen（TN）concentration in some WWTPs beyond the standard level. Therefore, it is urgent to improve the relevant treatment process and corresponding WWTPs structure to enhance the TN removal efficiency (TNRE) (Wang et al., 2019). The removal efficiency of A 2 O is linked with hydraulic retention time (HRT), C/N, dissolved oxygen (DO) and so on. Recent investigations have shown that extending the HRT in the anoxic tank can improve the efficiency of nitrogen and phosphorus removal. In the anoxic tank, the nitrate (NO 3 - -N) in the return stream of the mixture from the aerobic tank, together with the organic matter and intracellular storage of heterotrophic bacteria from the anaerobic tank, undergoes denitrification by exogenous denitrification (e.g. ordinary heterotrophic denitrifying organisms, OHOs), endogenous denitrification (e.g. denitrifying glycogen-accumulating organisms, DGAOs) to nitrogen gas, and then the nitrogen removal was achieved. In the denitrification process, endogenous denitrifying polyphosphorus (e.g. denitrifying polyphosphate-accumulating organisms, DPAOs) utilized nitrate nitrogen (NO x - -N) as electron acceptor and poly-β-hydroxyalkanoate (PHA) as electron donor to phosphorus uptake. Insufficient denitrification process would increase NO 3 - -N concentration in effluent and thus reduce TNRE (Tabraiz et al., 2018) . Extended HRT in anoxic tank could make denitrification process more sufficient. The results by Shen et al. showed that the removal efficiency of the inverted A 2 O was optimized under the anoxic, anaerobic and aerobic tank capacity ratios of 3:2:4, and the average removal efficiency of COD, ammonia nitrogen(NH 4 + -N), TN and TP up to 88.75%, 80.12%, 65.33% and 62.53%, respectively(yang et al., 2020). Moreover, the results of Brown et al. showed that TNRE increased only from 76% to 89%, while TP removal efficiency (TPRE) significantly increased from 40% to 82% in an anaerobic/anoxic/aerobic membrane bioreactor, under the HRT in anaerobic, anoxic and aerobic tank ranged from 0.5 to 3 h, 1to 5 h and fixed at 8 h, respectively. In order to achieve better TNRE and TPRE simultaneously, it was necessary to balance and optimize the HRT of each reaction tank and reduce the adverse effects of each other. Therefore, the optimized HRT was 2-h anaerobic and 4-h anoxic(Brown et al., 2011). However, the volume of the biological reaction tank was linked with HRT and running cost, so it was necessary to choose suitable HRT. Practical studies identify suitable HRT require much long time for experimental analysis and costly. Instead, it is essential to use simulation to fully utilize the predictive capabilities of the model, which can be utilized to efficiently and easily filter out the best design solution, so as to reduce the laboratory test time and cost. Modeling is the process of representing reality in a simplified way. A series of mathematical procedures and equations consists of time-dependent variables and parameters through which the model was defined(Faris et al., 2022). Current mainstream wastewater treatment software include Biowin, GPS-X, STOAT, WEST, of which Biowin and GPS-X were widely applied(Wei et al., 2018). GPS-X is a mature wastewater treatment simulation software, and numerous recent studies have utilized GPS-X software to simulate and predict wastewater treatment processes which was capable of simulating wastewater treatment processes, such as sequencing batch reactor (SBR), aeration biofilter, oxidation ditch, etc. Its contact growth model includes BAF model and denitrification filter, etc. In addition, the model includes a variety of settlement models, aerobic (anaerobic) digestion models which can be built and simulated in response to the designer's needs(Abbasi et al., 2021, Hellal and Abou-Elela, 2021, Li et al., 2021, Mu'Azu et al., 2020). Kobeyev, et al. utilized GPS-X software to simulate the application of membrane bioreactor (MBR), SBR and reverse osmosis with upflow anaerobic sludge blanket for wastewater treatment in a hotel in Los Angeles, the MBR plant proved to be the most effective solution for the considered location and standards was recommended for usage in hotel buildings. The design and modeling results were verified by hand calculations so this study provided a detailed procedure for designing and modeling a greywater treatment plant for a hotel building ,and then it could be applied for the localities with a similar climate(Kobeyev et al.). The study by Jcab C et al. presented a novel strategy to enhance the TNRE in WWTPs by GPS-X integrated with response surface methodology (RSM) and the accuracy of GPS-X for WWTPs modeling was validated by static and dynamic simulations with actual operational data, so this result exhibited the advantages (quickness and simpleness) and applicability of utilizing the approach of RSM and GPS-X integration for WWTPs treatment processes upgrade (Jcab et al., 2021). In this study, modeling and simulation were carried out for the test which effect of varying HRT of anoxic tank under different C/N on the effect of TNRE and TPRE in A 2 O process. This study was based on the experimental data of the A 2 O process under the condition of C/N=7, changing the volume of the anoxic tank to extend the HRT of the anoxic tank from 1.8h to 3.6h, utilizing GPS-X software to develop a model for parameter calibration, and then applied the experimental data under the condition of C/N=10 for model validation. Finally, the optimal volume (HRT) of the anoxic tank was predicted by the validated model and the results were analyzed, so as to provide a reference for practical engineering in A 2 O. 2 Material and methods 2.1 Reactor set-up and operation The volume of the reactor was 16L, including two anaerobic tanks (0.8L for each tank), two anoxic tanks (1.8L for each tank), and three aerobic tanks (3.6 L for each tank) and the procedure of reactor was shown in Fig. 1. The reactor operation mode was A 2 O and the test temperature was controlled by a constant temperature water bath at 20.0±1.0 °C. The influent flow efficiency was controlled by a peristaltic pump at 24 L/d, with a sludge return ratio of 100% and a mixture return ratio of 200%. The aerobic tank was injected with air by an aeration pump, and the sludge age (SRT) was 15-20 d. The MLSS was maintained at 3.5-4.5 g/L at phase I-IV of the reactor, and the dissolved oxygen (DO) in the aerobic tank was in the range of 0.9-1.2 mg/L. The reactor underwent a 25d start-up phase and then entered a stable operation phase for 60 days (day 1-60), divided into 4 phases (Ⅰ - Ⅳ) and operated as A 2 O process. In phase I and phase II, the process with the same level of C/N, COD, NH 4 + -N and phosphates (PO 4 3- -P) but different HRT. Meanwhile, the phase III and phase Ⅳ with the same level of C/N, COD, NH 4 + -N and PO 4 3- -P but different HRT. During phase Ⅰ and phase Ⅲ, the anoxic tank No. 2 was closed to reduce the volume of the anoxic tank, so HRT of anoxic tank was1.8h. The HRT of anoxic tank in phase II and phase IV was 3.6h. The reactor operating conditions were shown in Table 1. Table 1 The reactor operating conditions Phase Operation Days (d) Anoxic tank HRT (h) COD (mg/L) C/N NH 4 + -N (mg/L) PO 4 3- -P (mg/L) I 1-15 1.8 280 7 40 6 II 16-30 3.6 280 7 40 6 III 31-45 1.8 400 10 40 6 IV 45-60 3.6 400 10 40 6 2.2 Inoculated sludge and synthetic wastewater The 8 L inoculated sludge with the MLSS concentration of 6.368 g/L was taken from the sedimentation tank at the No. 4 WWTPs in Xi’an, China. The main components of the synthetic wastewater were C 6 H 12 O 6 . H 2 O (COD=280mg/L and COD=400mg/L), NH 4 HCO 3 (NH 4 + -N=40mg/L), KH 2 PO 4 (PO 4 3- -P=6mg/L), sodium bicarbonate (NaHCO 3 ), magnesium sulphate heptahydrate (MgSO 4 7H 2 O), anhydrous calcium chloride (CaCl 2 ), the volume fraction of the trace element nutrient solution was 0.1 mL/L. 2.3 Analytical methods The content of NH 4 + -N, NO 2 - -N, NO 3 - -N, PO 4 3- -P, TN and MLSS were determined via standard methods(Rattaapha et al., 1985). The TN content was the sum of NH 4 + -N, NO 2 - -N and NO 3 - -N. pH was measured utilizing a Thermo pH meter and DO content was measured utilizing the Thermo fluorescence DO meter. 2.4 Simulation methods The simulations were carried out with GPS-X 6.4 software, a modular and versatile simulation tool for municipal and industrial WWTPs developed by Hydromantis Environmental Consultants, Canada. The mechanistic models include all the activated sludge mathematical models developed by International Water Quality Association (IWAQ), such as the Activated Sludge Model (ASM) 1 and ASM 3 models for carbon and nitrogen removal, and the ASM 2D model for nitrogen and phosphorus removal, as well as the self-developed Mantis, Newgenerate models and others(Latif et al., 2020, Shuhan Lei 2022). The A 2 O process used in this study was created in a GPS-X simulation and required a comprehensive library of carbon, nitrogen and phosphorus, so the model library CNPLIB was chosen. The codstates model based on COD components was selected for the influent, the ASM 2D model with simultaneous nitrogen and phosphorus removal was selected for each reaction tank, and the simple1d model was selected for the secondary sedimentation tank. The model contains over 60 composite, state variables and several libraries of expressions describing the process, as well as over 35 stoichiometric and 25 kinetic input and output parameters. The steps of A 2 O process model building were as follows: Selected the appropriate unit to build the A 2 O process model and adjust the size correspondingly as Fig. 1. The GPS-X model library (CNPLIB) was chosen, and selected the corresponding model library for each unit. Entered the corresponding water quality (COD, NH 4 + -N, PO 4 3- -P and so on) parameters as Table 1 in the influent unit, and modified the corresponding parameters, such as the soluble inert fraction of total COD (frsi), readily biodegradable fraction of total COD (frss), particulate inert fraction of total COD (frxi), colloidal fraction of slow biodegradation COD (frscol), ammonium fraction of soluble TN (frsnh), VSS/TSS ratio (ivsstotss), nitrogen content of inert particulate material (inxi) and so on. Data from phase I and phase II was utilized to calibrate the GPS-X default kinetics parameters. Calibration was completed when the model predictions were fitted to all corresponding effluent quality parameter data within acceptable limits. 3 Results and Discussion 3.1 Test results and model calibration and validation Table 2 showed the operation performance and simulation results of four phases of the A 2 O process. Table 2 Operation performance and simulation results of A 2 /O processes at different phases Phase COD / (mg/L) NH 4 + -N / (mg/L) TN / (mg/L) TP / (mg/L) Removal efficiency / (%) Influent Effluent Influent Effluent Influent Effluent Influent Effluent COD NH 4 + -N TN PO 4 3- -P Ⅰ Exp 277.8±6.7 12.84±2.72 36.51±1.50 11.42±1.77 36.54±1.50 15.77±1.82 5.68±0.47 0.40±0.10 95.4±1.0 72.2±4.1 57.9±3.7 93.0±2.0 Sim 280±0.0 11.44±1.32 40.00±0.0 9.72±1.47 40.00±0.0 14.09±0.84 6.00±0.0 0.41±0.08 95.9±0.5 75.7±3.7 66.8±2.1 93.8±1.0 Ⅱ Exp 279.±7.9 8.74±1.56 38.37±1.30 7.59±1.22 38.40±1.30 11.08±1.79 5.59±0.72 0.31±0.07 96.9±0.6 79.9±3.3 71.1±4.9 94.4±2.0 Sim 280±0.0 8.40±1.13 40.00±0.0 7.63±0.91 40.00±0.0 11.20±0.69 6.00±0.0 0.29±0.07 97.0±0.4 81.0±2.3 72.0±1.7 95.2±1.0 Ⅲ Exp 390.9±11.5 8.26±3.01 39.19±2.11 0.99±0.25 39.17±2.0 8.93±1.91 5.54±0.41 0.11±0.03 97.9±0.8 97.5±5.7 77.2±5.0 98.0±1.0 Sim 400±0.0 8.20±1.44 40.00±0.0 0.90±0.23 40.00±0.0 8.34±2.18 6.00±0.0 0.10±0.04 98.0±0.4 97.8±5.9 79.2±5.5 98.4±1.0 Ⅳ Exp 385.3±16.0 8.06±3.08 39.0±2.0 0.36±0.25 39.04±2.11 6.98±1.46 5.58±0.39 0.05±0.04 97.9±0.8 99.0±6.2 82.1±3.5 99.0±1.0 Sim 400±0.0 8.27±1.55 40.00±0.0 0.26±0.19 40.00±0.0 5.73±1.57 6.00±0.0 0.05±0.03 98.0±0.4 99.4±4.8 85.7±3.9 99.7±0.0 Under C/N=7, HRT of anoxic tank increased from 1.8h to 3.6h, had little effects on the removal efficiency of COD, but TNRE and TPRE elevated from 57.9±3.7% to 71.1±4.9%, and 93.0±2.0% to 94.4±2.0%, respectively. Under C/N=10, the increase in the volume of the anoxic tank did not necessarily improve the TNRE and TPRE (Table 2). In order to optimize the process design and operating parameters, the test results were simulated utilizing the GPS-X software. A sensitivity analysis was conducted utilizing the GPS-X sensitivity analysis function (SAT) included a time-dynamic model. SAT was applied to determine the most sensitive kinetic parameters to the A 2 O process for change of influent COD, NH 4 + -N, TN and TP concentrations over 30 days including phase I and phase II. More than 60 kinetic parameters, 7 parameters were found to be the most sensitive ones, including aerobic heterotrophic yield (Y H ), aerobic heterotrophic decay rate (b H ), the maximum growth rate for ammonia oxidizer (μ A ), oxygen saturation for ammonia oxidizer (K o ), ammonia oxidizer aerobic decay rate (b A ), ammonification rate (K a ) and maximum growth rate of PAO (μ PAO ). The main purpose of model calibration is to minimize the differences between experimental and simulation results. In the model calibration process carried out in GPS-X software, the major calibrated parameter was the kinetic parameters obtained from the sensitivity analysis. The experimental data of phase I and phase II were utilized for parameter calibration, and multiple calibrations were carried out to achieve the target. Table 3 showed the kinetic parameters of the calibrated model and the reasonable range of each parameter mentioned in the literatures(Afonso and Maria, 2002, Boontian, 2012, ESS, 2017, Jeppsson, 1996, Mulas, 2006, Soliman et al., 2018, Weijers and Vanrolleghem, 1997). The final calibrated kinetic parameters in this study fell within previous reports. Table 3 The calibrated model kinetic parameters and the reported ranges in previous studies Kinetic parameters Default Range References Calibrated values Y H 0.666 0.38 - 0.75 Jeppsson (1996) 0.51 b H 0.62 0.05 - 0.16 Mulas (2006) and Hydromantis (2017) 0.83 μ A 0.90 0.2 - 1.2 Afonso and da Conceição Cunha (2002) and Soliman and Eldyasti (2018) 0.93 K o 0.25 0.1 - 1 Jeppsson (1996), Boontian (2012) and Soliman and Eldyasti (2018) 0.26 b A 0.17 0.05 - 0.3 Jeppsson (1996), Weijers and Vanrolleghem (1997) and Soliman and Eldyasti (2018) 0.16 K a 0.08 0.04 - 0.12 Jeppsson (1996) 0.05 μ PAO 1.00 0.67 - 2.97 Boontian (2012) 1.61 The simulations were validated utilizing the calibrated model for phase III phase IV. Fig. 2 showed a comparison of the experimental results and simulated values for phase I to IV. The results were highly consistent with the experimental results. This model could simulate and predict the effect of varying the volume of the anoxic tank on the pollutant removal effectiveness of the A 2 O process. The COD removal efficiency was basically the same in all four phases and remained above 95% (Fig. 2). TNRE and TPRE showed an increasing trend. Removal efficiency of NH 4 + -N in the four phases was 73%, 80.5%, 97.5% and 99% respectively. TPRE was 60%, 72%, 79% and 83% respectively. TPRE was 93%, 95%, 98% and 99% respectively. The reactor has the best effect on carbon, nitrogen and phosphorus treatment under the working condition of phase Ⅳ. Thus, under low C/N, changing the HRT of anoxic tank to improve the pollutant removal efficiency of A 2 O reactor was more significantly, so this method was more suitable for the case of insufficient carbon source. 3.2 Selection of optimum working conditions Utilizing this model, the anoxic tank volume was simulated from 1.98L to 5.4L (0.08L increase each time) under 19 operating conditions. Fig. 3 showed the treatment effects on the pollutants in optimum working conditions. 3.3 Analysis the effect of changing the volume of the anoxic tank on pollutant removal efficiency TNRE and TPRE in the A 2 O was associated with COD level, DO content of the aerobic tank and the HRT. The HRT of the anoxic tank mainly affected the nitrogen removal efficiency. Insufficient denitrification led to higher NO 3 - -N content in the aerobic tank effluent and thus reducing the TNRE. Fig. 4 showed the simulated concentrations of each pollutant in the phase I, II, III and IV, and Table 4 showed the amount of released phosphorus and the amount of uptake phosphorus in phase Ⅰ, Ⅱ, Ⅲ and Ⅳ. Tab le 4 Phosphorus release and uptake of the reactor at various phase Phase Anaerobic phosphorus release/(mg/L) Anoxic phosphorus uptake/(mg/L) Aerobic phosphorus uptake/mg/L) Tests Simulation Tests Simulation Tests Simulation I 7.19±0.72 8.45±0.41 0.27±0.12 0.33±0.09 6.52±0.75 7.87±0.13 II 7.24±1.51 8.34±0.69 1.11±0.59 1.06±0.13 5.81±1.25 7.52±0.23 III 15.63±1.43 16.71±0.96 0.04±0.08 0.09±0.02 15.48±1.38 16.68±0.84 IV 20.20±2.11 21.33±0.87 0.12±0.14 0.20±0.09 20.03±2.07 23.13±0.76 3.3.1 Influence of changing anoxic tank volume on pollutant removal efficiency under C/N=7 Under C/N=7, the anoxic tank volume increased from 1.8L at phase Ⅰ to 3.6 L at phase Ⅱ, the HRT of the anoxic tank increased from 1.8 h to 3.6 h, the effluent COD and NO 3 - -N decrease and the amount of phosphorus uptake in the anoxic tank was significantly increased from 0.27 mg/L to 1.11 mg/L (the simulated value increased from 0.33 mg/L to 1.06 mg/L) (Fig. 2 and Table 4). The test and simulation results showed that under C/N=7, increasing the volume of the anoxic tank had little effect on the COD and NH 4 + -N removal efficiency, but TNRE and TPRE were largely improved. As the volume of the anoxic tank increased, the removal efficiency of COD had less change in all phases (Fig. 2 and Fig. 4), because the removal of COD was mainly concentrated in the anaerobic and anoxic phases. In the anaerobic tank, the large molecules carbon source was converted into small molecules of volatile fatty acids (VFA), which were synthesized into PHB by the polyphosphate-accumulating organisms (PAOs) and stored in cells, and then the intracellular phosphorus was hydrolyzed to orthophosphate and released outside of the cell(Zhao et al.). In the anoxic tank, the remaining carbon source was utilized by the OHOs to reduce the nitrate returned from the aerobic tank, and DPAOs also underwent phosphorus uptake through denitrification(Xie et al., 2021). In the anoxic tank, as HRT increased, the NO 3 - -N content significantly reduced and the TNRE in phase II was higher than that of phase I. The NO 3 - -N returned to the anoxic tank was fully utilized by denitrifying bacteria, which utilized NO 3 - -N as an electron acceptor for energy metabolism leading to an increase of TNRE and a decrease in NO 3 - -N content(Zhao et al., 2020). The amount of phosphorus released in phase I and phase II in the anaerobic tank was basically the same, but the amount of phosphorus aggregated by phase II in the anoxic tank was much larger than that of phase I (Table 3). When denitrifying bacteria and PAOs were present at the same time, the denitrifying efficiency would be faster than the phosphorus release efficiency, and denitrifying bacteria would preferentially utilize the carbon source in the feed water, especially when the carbon source was insufficient. Meanwhile, the available carbon source to PAOs was decrease, resulting in insufficient phosphorus release and reducing the overall phosphorus removal capacity of the system(Jya et al.). Phase II increased the volume of the anoxic tank, enhanced the usage of dissolved biodegradable COD and endogenous carbon by denitrifying bacteria, reduced the COD flow to the aerobic tank and also provided sufficient electron acceptors for denitrification and phosphorus removal process. Adequate denitrification reduced the NO 3 - -N content of the system and reduced the inhibitory effect on PAOs. 3.3.2 Influence of changing anoxic tank volume on pollutant removal efficiency under C/N=10 Under C/N=10, the HRT of the anoxic tank increased from 1.8h at phase III to 3.6h at phase IV (Fig. 2 and Table 4), the COD and NO 3 - -N of the anoxic tank effluent decreased, the amount of phosphorus released in the anoxic tank increased significantly from 0.04 mg/L to 0.12 mg/L (the simulated value increased from 0.09 mg/L to 0.20 mg/L) in response to change in phases. Compared to the low C/N, less anoxic phosphorus uptake amount, more phosphorus uptake was found in the aerobic tank. The experimental and simulation results showed that under C/N=10, increasing the volume of the anoxic tank had insignificantly improved on NH 4 + -N and COD removal efficiency, while had a greater effect on the TNRE and TPRE. Compared with phase Ⅰ and phase Ⅱ, the released phosphorus value from phase Ⅲ and phase Ⅳ in anaerobic tank was higher than that of previously phase (Fig.4 and Table 4). This was mainly attributed to enhancement of influent COD in phase Ⅲ and phase Ⅳ, thus, a sufficient COD was provided for PAOs, resulting in the increase of the phosphorus release. Phase Ⅳ anaerobic tank phosphorus release was higher than that of phase Ⅲ, because anoxic tank denitrification was sufficient in phase Ⅳ due to increase of anoxic tank volume, thus, NO 3 - -N concentration in the external return stream decreased(Meijiao et al., 2018). This result was due to the reduced inhibition of NO 3 - -N on the anaerobic release of phosphorus by PAO. Phosphorus uptake was mainly concentrated in the aerobic tank, and the amount of phosphorus uptake in the anoxic tank was limited, indicating insufficient denitrification of phosphorus uptake reaction was little. As the influent COD increased, denitrifying bacteria competed for the external carbon source and the denitrification reaction was more complete, therefor DPAOs had insufficient electron acceptors to phosphorus uptake(Wei et al., 2019). 4 Conclusion Under the conditions of influential C/N=7, changing the HRT of the anoxic tank at 1.8h~4.5h had a significant effect on the TNRE and TPRE in the A 2 O process. The optimal solution obtained was that the HRT of the anoxic tank were 3.8-4.0h, and the corresponding volume ratio of anaerobic, anoxic and aerobic was 1:2.4:6.75. The corresponding TNRE and TPRE at this point were 76.13% and 96.5%, respectively. Under C/N=10 conditions, changing the HRT of the anoxic tank from 1.8h to 4.5h exhibited limited effect on nitrogen and phosphorus removal. Declarations Data Availability statement The data that support the findings of this study are available from the corresponding author, Jianqiang Zhao, upon reasonable request. Funding statement This work was supported by the National Natural Science Foundation of China (No. 51778057). The authors have no relevant financial or non-financial interests to disclose. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Sample CRediT author statement Shuhan Lei: Conceptualization, Project administration, Writing – original draft, Writing – review & editing. Jianqiang Zhao: Supervision, Funding acquisition, Writing – review & editing. Junkai Zhao: Investigation, Writing – review & editing. ShutingXie: Investigation, Methodology. Ju Zhang: Investigation. Bingfeng Shi: Methodology. References Abbasi, N., Ahmadi, M., Naseri, M., 2021. Quality and cost analysis of a wastewater treatment plant using GPS-X and CapdetWorks simulation programs. J Environ Manage 284. Afonso, P., Maria, D.C.O.C., 2002. 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Journal of Chemical Technology & Biotechnology. Mu'Azu, N.D., Alagha, O., Anil, I., 2020. Systematic Modeling of Municipal Wastewater Activated Sludge Process and Treatment Plant Capacity Analysis Using GPS-X. Sustainability-Basel 12. Mulas, M., 2006. Modelling and Control of Activated Sludge Processes. Università Degli Studi Di Cagliari. Rattaapha, W., Greenberg, A.E., Clesceri, L.S., Eaton, A.D., Eaton, D.L., Rice, W., Greenberg, A.S., Rice, E.W., Connors, J., Jenkis, D., 1985. Standards methods for the examination of water and wastewater. Health Laboratory Science 4(3), 137. Shuhan Lei , e.a., 2022. Comparisons of nitrogen and phosphorus removal efficiency in A2O process, UCT process, MUCT process, enhanced phosphorus removal process and inverted A2/ O process based on GPS-X simulation. 2022 IOP Conf. Ser.: Earth Environ Sci. 983012114. Soliman, Moomen, Eldyasti, Ahmed, 2018. Ammonia-Oxidizing Bacteria (AOB): opportunities and applications-a review. Reviews in Environmntal Science and Biotechnology 17(2), 285-321. Tabraiz, S., Hassan, S., Abbas, A., Nasreen, S., Zeeshan, M., Fida, S., Shamurad, B.A., Acharya, K., Petropoulos, E., 2018. Effect of effluent and sludge recirculation ratios on integrated fixed films A2O system nutrients removal efficiency treating sewage. Desalination and water treatment 114(MAY), 120-127. Tian, Y.J., Iop, 2019. Selection of Municipal Wastewater Treatment Process Based on Improved Analytic Hierarchy Process, 5th International Conference on Energy Materials and Environment Engineering (ICEMEE). Kuala Lumpur, MALAYSIA. Wang, J., Chon, K., Ren, X., Kou, Y., Chae, K.J., Piao, Y., 2019. Effects of beneficial microorganisms on nutrient removal and excess sludge production in an anaerobic–anoxic/oxic (A2O) process for municipal wastewater treatment. Bioresource Technology. Wei, L.I., Sun, H., Wei, W., Jinxiang, F.U., 2019. Study on the Domestication and Phosphorus Removal Characteristics of Denitrifying Poly-phosphorus Accumulating Organism. Journal of Shenyang Jianzhu University(Natural Science). Wei, Z.Q., Shangguan, H.D., Jun-Lei, Y.E., Zhi-Rong, H.U., 2018. Optimization of Upgrading Design of a Wastewater Treatment Plant Based on GPS-X Simulation. China Water & Wastewater. Weijers, S.R., Vanrolleghem, P.A., 1997. A Procedure for Selecting Best Identifiable Parameters in Calibrating Activated Sludge Model No.1 to Full-Scale Plant Data. Water Science & Technology 36(5), 69-79. Xie, S., Zhao, J., Zhang, Q., Zhao, J., Yan, C., 2021. Improvement of the performance of simultaneous nitrification denitrification and phosphorus removal (SNDPR) system by nitrite stress. Sci Total Environ 788(31), 147825. yang, S., Dianzhan, W., Lixiang, Z., 2020. Effect of Tank Volume Ratio on the Removal of Nitrogen and Phosphorus in Digested Piggery Wastewater by Reversed A2O Process. Technology of Water Treatment(3), 5. Zhao, J., Xie, S., Luo, Y., Li, X., NADH accumulation during DPAO denitrification in a simultaneous nitrification, denitrification and phosphorus removal system. Environmental Science: Water Research & Technology. Zhao, J., Zhao, J., Xie, S., Lei, S., 2020. The role of hydroxylamine in promoting conversion from complete nitrification to partial nitrification: NO toxicity inhibition and its characteristics - ScienceDirect. Bioresource Technology 319. Additional Declarations No competing interests reported. Supplementary Files GA.png Graphical abstract Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2058607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":149906766,"identity":"26d6e2e2-3845-4f24-b301-027edeb53c55","order_by":0,"name":"Shuhan Lei","email":"","orcid":"","institution":"Chang’ an University","correspondingAuthor":false,"prefix":"","firstName":"Shuhan","middleName":"","lastName":"Lei","suffix":""},{"id":149906767,"identity":"bfb7cc79-02b9-498d-85e3-236ba7ba331c","order_by":1,"name":"Jianqiang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACfvbm4z8SDGzk7I83EKlFsudYgsSDijRjhjMHiNRiMCNHQfLBmcOJDTcSiNXCkMNgkNjGbMw48/HGGww1NtEEtZgznD2QkNjGJscsnVZswXAsLbeBkBbLxr6EA4ltPMZs0jlmEowNhwlrMTjMY9iQ2CaR2CN5hlgtx3iMGRLOGCTOkOAhUotkD1saQ0JFgrEBD9AvCcT4hV/+8THGHwb/5QzYD2+88aHGhrAWFEdKJJCiHKKFVB2jYBSMglEwMgAAqiJAxVU6oXAAAAAASUVORK5CYII=","orcid":"","institution":"Chang’ an University","correspondingAuthor":true,"prefix":"","firstName":"Jianqiang","middleName":"","lastName":"Zhao","suffix":""},{"id":149906768,"identity":"05244131-f592-44a9-9a4e-13510583b9a0","order_by":2,"name":"Junkai Zhao","email":"","orcid":"","institution":"Chang’ an University","correspondingAuthor":false,"prefix":"","firstName":"Junkai","middleName":"","lastName":"Zhao","suffix":""},{"id":149906769,"identity":"3095c6c9-7a79-4f9e-9e28-89d595c7bed6","order_by":3,"name":"Shuting Xie","email":"","orcid":"","institution":"Chang’ an University","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Xie","suffix":""},{"id":149906770,"identity":"5a5eb498-2e2d-41ed-afe5-67feda416dd2","order_by":4,"name":"Ju Zhang","email":"","orcid":"","institution":"Chang’ an University","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"","lastName":"Zhang","suffix":""},{"id":149906771,"identity":"205c4bdf-9e32-4458-9d33-c8ed43ab4313","order_by":5,"name":"Bingfeng Shi","email":"","orcid":"","institution":"Chang’ an University","correspondingAuthor":false,"prefix":"","firstName":"Bingfeng","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2022-09-13 02:14:23","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-2058607/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-2058607/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28750920,"identity":"6ca65b0e-b9e2-41ce-8592-19a95cec2d5e","added_by":"auto","created_at":"2022-11-07 14:45:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of the test setup\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/3aa92c593abc506bd8fe6b9e.png"},{"id":28750923,"identity":"e1b5e855-56cd-4021-ad5d-10e88e102150","added_by":"auto","created_at":"2022-11-07 14:45:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Removal performances in A\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO process: (a) Removal performance of COD; (b) Removal performance of NH\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e -N; (c) Removal performance of TN in A\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO process; (d) Removal performance of PO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e3-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e-P in A\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/b8e216ed026c967f22807d44.png"},{"id":28751209,"identity":"cc41ff9b-a430-4d5b-9a94-be9a3efec17a","added_by":"auto","created_at":"2022-11-07 14:53:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1077718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulated pollutant treatment effect at optimal working condition: (a) Volume of Anoxic tank equals 3.96 L, C/N=7; (b) Volume of Anoxic tank equals 4.32 L, C/N=10\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/7de6f0fcc5859f0e3fc9b078.png"},{"id":28750921,"identity":"f7021f54-1673-43ae-9627-975029707cef","added_by":"auto","created_at":"2022-11-07 14:45:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe variations of the pollutants at various phase: (a) Phase Ⅰ; (b) Phase Ⅱ; (c) Phase Ⅲ; (d) Phase Ⅳ.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/deba8b5ba973202cbeef68cd.png"},{"id":32531397,"identity":"a6249abb-4822-414f-a11f-de226a7c0e1b","added_by":"auto","created_at":"2023-02-06 14:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1483243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/7bbb4a04-f208-4054-941d-67360d608763.pdf"},{"id":28751208,"identity":"e4140637-570e-47ad-a2c0-55c1b59108c4","added_by":"auto","created_at":"2022-11-07 14:53:35","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325453,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-2058607/v2/cd8277b69985a71cad5ba5eb.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of A2O Process by Changing Anoxic Tank Volume Based on GPS-X Simulation","fulltext":[{"header":"Highlights","content":"\u003col\u003e\n \u003cli\u003eIncreased anoxic tank volume can improve the pollutant removal efficiency of A\u003csup\u003e2\u003c/sup\u003eO.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSix critical parameters were found with the sensitivity analysis and screening.\u003c/li\u003e\n \u003cli\u003eThe developed GPS-X model predicts the effect of changing A\u003csup\u003e2\u003c/sup\u003eO conditions.\u003c/li\u003e\n \u003cli\u003eThe optimal HRTs of anoxic tank were 4.0 h under C/N=7.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eAnaerobic-anoxic-aerobic process (AAO), also known as A\u003csup\u003e2\u003c/sup\u003eO process, is a traditional biological nitrification, denitrification and phosphorus removal wastewater treatment technology, and was widely applied in wastewater treatment plants (WWTPs)\u0026nbsp;(Jia et al., Tian and Iop, 2019, Wang et al., 2019). However, the current A\u003csup\u003e2\u003c/sup\u003eO process also had certain defects and some improvements still need to be improved. For example, the competition between aerobic phosphate accumulating organisms (PAO) and denitrifying bacteria for carbon source becomes more intense under low ratios of chemical oxygen demand (COD) to nitrogen (C/N), and the anaerobic environment of anaerobic phosphorus release is influenced by nitrate in sludge mixture reflux(Tian and Iop, 2019). These phenomena often lead to the effluent total nitrogen（TN）concentration in some WWTPs beyond the standard level. Therefore, it is urgent to improve the relevant treatment process and corresponding WWTPs structure to enhance the TN removal efficiency (TNRE)\u0026nbsp;(Wang et al., 2019).\u003c/p\u003e\n\u003cp\u003eThe removal efficiency of A\u003csup\u003e2\u003c/sup\u003eO is linked with hydraulic retention time (HRT), C/N, dissolved oxygen (DO) and so on. Recent investigations have shown that extending the HRT in the anoxic tank can improve the efficiency of nitrogen and phosphorus removal. In the anoxic tank, the nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N) in the return stream of the mixture from the aerobic tank, together with the organic matter and intracellular storage of heterotrophic bacteria from the anaerobic tank, undergoes denitrification by exogenous denitrification (e.g. ordinary heterotrophic denitrifying organisms, OHOs), endogenous denitrification (e.g. denitrifying glycogen-accumulating organisms, DGAOs) to nitrogen gas, and then the nitrogen removal was achieved. In the denitrification process, endogenous denitrifying polyphosphorus (e.g. denitrifying polyphosphate-accumulating organisms, DPAOs) utilized nitrate nitrogen (NO\u003csub\u003ex\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N) as electron acceptor and poly-\u0026beta;-hydroxyalkanoate (PHA) as electron donor to phosphorus uptake. Insufficient denitrification process would increase NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N concentration in effluent and thus reduce TNRE\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e(Tabraiz et al., 2018)\u003c/sup\u003e. Extended HRT in anoxic tank could make denitrification process more sufficient. The results by Shen et al. showed that the removal efficiency of the inverted A\u003csup\u003e2\u003c/sup\u003eO was optimized under the anoxic, anaerobic and aerobic tank capacity ratios of 3:2:4, and the average removal efficiency of COD, ammonia nitrogen(NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N), TN and TP up to 88.75%, 80.12%, 65.33% and 62.53%, respectively(yang et al., 2020). Moreover, the results of Brown et al. showed that TNRE increased only from 76% to 89%, while TP removal efficiency (TPRE) significantly increased from 40% to 82% in an anaerobic/anoxic/aerobic membrane bioreactor, under the HRT in anaerobic, anoxic and aerobic tank ranged from 0.5 to 3 h, 1to 5 h and fixed at 8 h, respectively. In order to achieve better TNRE and TPRE simultaneously, it was necessary to balance and optimize the HRT of each reaction tank and reduce the adverse effects of each other. Therefore, the optimized HRT was 2-h anaerobic and 4-h anoxic(Brown et al., 2011). However, the volume of the biological reaction tank was linked with HRT and running cost, so it was necessary to choose suitable HRT.\u003c/p\u003e\n\u003cp\u003ePractical studies identify suitable HRT require much long time for experimental analysis and costly. Instead, it is essential to use simulation to fully utilize the predictive capabilities of the model, which can be utilized to efficiently and easily filter out the best design solution, so as to reduce the laboratory test time and cost. Modeling is the process of representing reality in a simplified way. A series of mathematical procedures and equations consists of time-dependent variables and parameters through which the model was defined(Faris et al., 2022). Current mainstream wastewater treatment software include Biowin, GPS-X, STOAT, WEST, of which Biowin and GPS-X were widely applied(Wei et al., 2018). GPS-X is a mature wastewater treatment simulation software, and numerous recent studies have utilized GPS-X software to simulate and predict wastewater treatment processes which was capable of simulating wastewater treatment processes, such as sequencing batch reactor (SBR), aeration biofilter, oxidation ditch, etc. Its contact growth model includes BAF model and denitrification filter, etc. In addition, the model includes a variety of settlement models, aerobic (anaerobic) digestion models which can be built and simulated in response to the designer\u0026apos;s needs(Abbasi et al., 2021, Hellal and Abou-Elela, 2021, Li et al., 2021, Mu\u0026apos;Azu et al., 2020). Kobeyev, et al. utilized GPS-X software to simulate the application of membrane bioreactor (MBR), SBR and reverse osmosis with upflow anaerobic sludge blanket for wastewater treatment in a hotel in Los Angeles, the MBR plant proved to be the most effective solution for the considered location and standards was recommended for usage in hotel buildings. The design and modeling results were verified by hand calculations so this study provided a detailed procedure for designing and modeling a greywater treatment plant for a hotel building ,and then it could be applied for the localities with a similar climate(Kobeyev et al.). The study by Jcab C et al. presented a novel strategy to enhance the TNRE in WWTPs by GPS-X integrated with response surface methodology (RSM) and the accuracy of GPS-X for WWTPs modeling was validated by static and dynamic simulations with actual operational data, so this result exhibited the advantages (quickness and simpleness) and applicability of utilizing the approach of RSM and GPS-X integration for WWTPs treatment processes upgrade\u0026nbsp;(Jcab et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn this study, modeling and simulation were carried out for the test which effect of varying HRT of anoxic tank under different C/N on the effect of TNRE and TPRE in A\u003csup\u003e2\u003c/sup\u003eO process. This study was based on the experimental data of the A\u003csup\u003e2\u003c/sup\u003eO process under the condition of C/N=7, changing the volume of the anoxic tank to extend the HRT of the anoxic tank from 1.8h to 3.6h, utilizing GPS-X software to develop a model for parameter calibration, and then applied the experimental data under the condition of C/N=10 for model validation. Finally, the optimal volume (HRT) of the anoxic tank was predicted by the validated model and the results were analyzed, so as to provide a reference for practical engineering in A\u003csup\u003e2\u003c/sup\u003eO.\u0026nbsp;\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003ch2\u003e2.1 Reactor set-up and operation\u003c/h2\u003e\n\u003cp\u003eThe volume of the reactor was 16L, including two anaerobic tanks (0.8L for each tank), two anoxic tanks (1.8L for each tank), and three aerobic tanks (3.6 L for each tank) and the procedure of reactor was shown in Fig. 1.\u003c/p\u003e\n\u003cp\u003eThe reactor operation mode was A\u003csup\u003e2\u003c/sup\u003eO and the test temperature was controlled by a constant temperature water bath at 20.0\u0026plusmn;1.0 \u0026deg;C. The influent flow efficiency was controlled by a peristaltic pump at 24 L/d, with a sludge return ratio of 100% and a mixture return ratio of 200%. The aerobic tank was injected with air by an aeration pump, and the sludge age (SRT) was 15-20 d. The MLSS was maintained at 3.5-4.5 g/L at phase I-IV of the reactor, and the dissolved oxygen (DO) in the aerobic tank was in the range of 0.9-1.2 mg/L.\u003c/p\u003e\n\u003cp\u003eThe reactor underwent a 25d start-up phase and then entered a stable operation phase for 60 days (day 1-60), divided into 4 phases (Ⅰ - Ⅳ) and operated as A\u003csup\u003e2\u003c/sup\u003eO process. In phase I and phase II, the process with the same level of C/N, COD, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and phosphates (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P) but different HRT. Meanwhile, the phase III and phase Ⅳ with the same level of C/N, COD, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P but different HRT. During phase Ⅰ and phase Ⅲ, the anoxic tank No. 2 was closed to reduce the volume of the anoxic tank, so HRT of anoxic tank was1.8h. The HRT of anoxic tank in phase II and phase IV was 3.6h. The reactor operating conditions were shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 The reactor operating conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003eOperation Days\u003c/p\u003e\n \u003cp\u003e(d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003eAnoxic tank\u003c/p\u003e\n \u003cp\u003eHRT\u0026nbsp;(h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eCOD\u003c/p\u003e\n \u003cp\u003e(mg/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eC/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e\n \u003cp\u003e(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P\u003c/p\u003e\n \u003cp\u003e(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e1-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e16-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e31-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e45-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e2.2 Inoculated sludge and synthetic wastewater\u003c/h2\u003e\n\u003cp\u003eThe 8 L inoculated sludge with the MLSS concentration of 6.368 g/L was taken from the sedimentation tank at the No. 4 WWTPs in Xi\u0026rsquo;an, China. The main components of the synthetic wastewater were C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003csup\u003e.\u003c/sup\u003eH\u003csub\u003e2\u003c/sub\u003eO (COD=280mg/L and COD=400mg/L), NH\u003csub\u003e4\u003c/sub\u003eHCO\u003csub\u003e3\u003c/sub\u003e (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N=40mg/L), KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P=6mg/L), sodium bicarbonate (NaHCO\u003csub\u003e3\u003c/sub\u003e), magnesium sulphate heptahydrate (MgSO\u003csub\u003e4\u003c/sub\u003e7H\u003csub\u003e2\u003c/sub\u003eO), anhydrous calcium chloride (CaCl\u003csub\u003e2\u003c/sub\u003e), the volume fraction of the trace element nutrient solution was 0.1 mL/L.\u003c/p\u003e\n\u003ch2\u003e2.3 Analytical methods\u003c/h2\u003e\n\u003cp\u003eThe content of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P, TN and MLSS were determined via standard methods(Rattaapha et al., 1985). The TN content was the sum of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N. pH was measured utilizing a Thermo pH meter and DO content was measured utilizing the Thermo fluorescence DO meter.\u003c/p\u003e\n\u003ch2\u003e2.4 Simulation methods\u003c/h2\u003e\n\u003cp\u003eThe simulations were carried out with GPS-X 6.4 software, a modular and versatile simulation tool for municipal and industrial WWTPs developed by Hydromantis Environmental Consultants, Canada. The mechanistic models include all the activated sludge mathematical models developed by International Water Quality Association (IWAQ), such as the Activated Sludge Model (ASM) 1 and ASM 3 models for carbon and nitrogen removal, and the ASM 2D model for nitrogen and phosphorus removal, as well as the self-developed Mantis, Newgenerate models and others(Latif et al., 2020, Shuhan Lei 2022).\u003c/p\u003e\n\u003cp\u003eThe A\u003csup\u003e2\u003c/sup\u003eO process used in this study was created in a GPS-X simulation and required a comprehensive library of carbon, nitrogen and phosphorus, so the model library CNPLIB was chosen. The codstates model based on COD components was selected for the influent, the ASM 2D model with simultaneous nitrogen and phosphorus removal was selected for each reaction tank, and the simple1d model was selected for the secondary sedimentation tank. The model contains over 60 composite, state variables and several libraries of expressions describing the process, as well as over 35 stoichiometric and 25 kinetic input and output parameters.\u003c/p\u003e\n\u003cp\u003eThe steps of A\u003csup\u003e2\u003c/sup\u003eO process model building were as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSelected the appropriate unit to build the A\u003csup\u003e2\u003c/sup\u003eO process model and adjust the size correspondingly as Fig. 1.\u003c/li\u003e\n \u003cli\u003eThe GPS-X model library (CNPLIB) was chosen, and selected the corresponding model library for each unit.\u003c/li\u003e\n \u003cli\u003eEntered the corresponding water quality (COD, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P and so on) parameters as Table 1 in the influent unit, and modified the corresponding parameters, such as the soluble inert fraction of total COD (frsi), readily biodegradable fraction of total COD (frss), particulate inert fraction of total COD (frxi), colloidal fraction of slow biodegradation COD (frscol), ammonium fraction of soluble TN (frsnh), VSS/TSS ratio (ivsstotss), nitrogen content of inert particulate material (inxi) and so on.\u003c/li\u003e\n \u003cli\u003eData from phase I and phase II was utilized to calibrate the GPS-X default kinetics parameters.\u003c/li\u003e\n \u003cli\u003eCalibration was completed when the model predictions were fitted to all corresponding effluent quality parameter data within acceptable limits.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3 Results and Discussion","content":"\u003ch2\u003e3.1 Test results and model calibration and validation\u003c/h2\u003e\n\u003cp\u003eTable 2 showed the operation performance and simulation results of four phases of the A\u003csup\u003e2\u003c/sup\u003eO process.\u003c/p\u003e\n\u003cp\u003eTable 2 Operation performance and simulation results of A\u003csup\u003e2\u003c/sup\u003e/O processes at different phases\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eCOD / (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N / (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eTN / (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003eTP / (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eRemoval efficiency / (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eInfluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eEffluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eInfluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eEffluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eInfluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eEffluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eInfluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.75%\"\u003e\n \u003cp\u003eEffluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.5%\"\u003e\n \u003cp\u003eCOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.5%\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.5%\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.5%\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"4.444444444444445%\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003eExp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e277.8\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e12.84\u0026plusmn;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e36.51\u0026plusmn;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e11.42\u0026plusmn;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e36.54\u0026plusmn;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e15.77\u0026plusmn;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e5.68\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e0.40\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e95.4\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e72.2\u0026plusmn;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e57.9\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e93.0\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003eSim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e280\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e11.44\u0026plusmn;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e9.72\u0026plusmn;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e14.09\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e6.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e95.9\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e75.7\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e66.8\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e93.8\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"4.444444444444445%\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003eExp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e279.\u0026plusmn;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e8.74\u0026plusmn;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e38.37\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e7.59\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e38.40\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n 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\u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e280\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e8.40\u0026plusmn;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e7.63\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e11.20\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e6.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e97.0\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e81.0\u0026plusmn;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e72.0\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e95.2\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"4.444444444444445%\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003eExp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e390.9\u0026plusmn;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e8.26\u0026plusmn;3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e39.19\u0026plusmn;2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e39.17\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e8.93\u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e5.54\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e0.11\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e97.9\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e97.5\u0026plusmn;5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e77.2\u0026plusmn;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e98.0\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003eSim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e400\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e8.20\u0026plusmn;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e8.34\u0026plusmn;2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e6.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.10\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e98.0\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e97.8\u0026plusmn;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e79.2\u0026plusmn;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e98.4\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"4.444444444444445%\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003eExp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e385.3\u0026plusmn;16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e8.06\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e39.0\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e39.04\u0026plusmn;2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e6.98\u0026plusmn;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e5.58\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.777777777777778%\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e97.9\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e99.0\u0026plusmn;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e82.1\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.666666666666667%\"\u003e\n \u003cp\u003e99.0\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003eSim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e400\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e8.27\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.26\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e40.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e5.73\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e6.00\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.13953488372093%\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e98.0\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e99.4\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e85.7\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.976744186046512%\"\u003e\n \u003cp\u003e99.7\u0026plusmn;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnder C/N=7, HRT of anoxic tank increased from 1.8h to 3.6h, had little effects on the removal efficiency of COD, but TNRE and TPRE elevated from 57.9\u0026plusmn;3.7% to 71.1\u0026plusmn;4.9%, and 93.0\u0026plusmn;2.0% to 94.4\u0026plusmn;2.0%, respectively. Under C/N=10, the increase in the volume of\u0026nbsp;the anoxic tank did not necessarily improve the TNRE and TPRE (Table 2).\u003c/p\u003e\n\u003cp\u003eIn order to optimize the process design and operating parameters, the test results were simulated utilizing the GPS-X software. A sensitivity analysis was conducted utilizing the GPS-X sensitivity analysis function (SAT) included a time-dynamic model. SAT was applied to determine the most sensitive kinetic parameters to the A\u003csup\u003e2\u003c/sup\u003eO process for change of influent COD, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, TN and TP concentrations over 30 days including phase I and phase II. More than 60 kinetic parameters, 7 parameters were found to be the most sensitive ones, including aerobic heterotrophic yield (Y\u003csub\u003eH\u003c/sub\u003e), aerobic heterotrophic decay rate (b\u003csub\u003eH\u003c/sub\u003e), the maximum growth rate for ammonia oxidizer (\u0026mu;\u003csub\u003eA\u003c/sub\u003e), oxygen saturation for ammonia oxidizer (K\u003csub\u003eo\u003c/sub\u003e), ammonia oxidizer aerobic decay rate (b\u003csub\u003eA\u003c/sub\u003e), ammonification rate (K\u003csub\u003ea\u003c/sub\u003e) and maximum growth rate of PAO (\u0026mu;\u003csub\u003ePAO\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eThe main purpose of model calibration is to minimize the differences between experimental and simulation results. In the model calibration process carried out in GPS-X software, the major calibrated parameter was the kinetic parameters obtained from the sensitivity analysis. The experimental data of phase I and phase II were utilized for parameter calibration, and multiple calibrations were carried out to achieve the target. Table 3 showed the kinetic parameters of the calibrated model and the reasonable range of each parameter mentioned in the literatures(Afonso and Maria, 2002, Boontian, 2012, ESS, 2017, Jeppsson, 1996, Mulas, 2006, Soliman et al., 2018, Weijers and Vanrolleghem, 1997). The final calibrated kinetic parameters in this study fell within previous reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 The calibrated model kinetic parameters and the reported ranges in previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eKinetic parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003eDefault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003eCalibrated values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eY\u003csub\u003eH\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.38 - 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eJeppsson (1996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eb\u003csub\u003eH\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.05 - 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eMulas (2006) and Hydromantis (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003e\u0026mu;\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.2 - 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eAfonso and da Concei\u0026ccedil;\u0026atilde;o Cunha (2002) and Soliman and Eldyasti (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eK\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.1 - 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eJeppsson (1996), Boontian (2012) and Soliman and Eldyasti (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eb\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.05 - 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eJeppsson (1996), Weijers and Vanrolleghem (1997) and Soliman and Eldyasti (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003eK\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.04 - 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eJeppsson (1996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.275641025641026%\"\u003e\n \u003cp\u003e\u0026mu;\u003csub\u003ePAO\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.057692307692308%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66025641025641%\"\u003e\n \u003cp\u003e0.67 - 2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\"\u003e\n \u003cp\u003eBoontian (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe simulations were validated utilizing the calibrated model for phase III phase IV. Fig. 2 showed a comparison of the experimental results and simulated values for phase I to IV. The results were highly consistent with the experimental results. This model could simulate and predict the effect of varying the volume of the anoxic tank on the pollutant removal effectiveness of the A\u003csup\u003e2\u003c/sup\u003eO process.\u003c/p\u003e\n\u003cp\u003eThe COD removal efficiency was basically the same in all four phases and remained above 95% (Fig. 2). TNRE and TPRE showed an increasing trend. Removal efficiency of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N in the four phases was 73%, 80.5%, 97.5% and 99% respectively. TPRE was 60%, 72%, 79% and 83% respectively. TPRE was 93%, 95%, 98% and 99% respectively. The reactor has the best effect on carbon, nitrogen and phosphorus treatment under the working condition of phase Ⅳ. Thus, under low C/N, changing the HRT of anoxic tank to improve the pollutant removal efficiency of A\u003csup\u003e2\u003c/sup\u003eO reactor was more significantly, so this method was more suitable for the case of insufficient carbon source.\u003c/p\u003e\n\u003ch3\u003e3.2 Selection of optimum working conditions\u003c/h3\u003e\n\u003cp\u003eUtilizing this model, the anoxic tank volume was simulated from 1.98L to 5.4L (0.08L increase each time) under 19 operating conditions. Fig. 3 showed the treatment effects on the pollutants in optimum working conditions.\u003c/p\u003e\n\u003ch3\u003e3.3 Analysis the effect of changing the volume of the anoxic tank on pollutant removal efficiency\u003c/h3\u003e\n\u003cp\u003eTNRE and TPRE in the A\u003csup\u003e2\u003c/sup\u003eO was associated with COD level, DO content of the aerobic tank and the HRT. The HRT of the anoxic tank mainly affected the nitrogen removal efficiency. Insufficient denitrification led to higher NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N content in the aerobic tank effluent and thus reducing the TNRE.\u003c/p\u003e\n\u003cp\u003eFig. 4 showed the simulated concentrations of each pollutant in the phase I, II, III and IV, and Table 4 showed the amount of released phosphorus and the amount of uptake phosphorus in phase Ⅰ, Ⅱ, Ⅲ and Ⅳ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab\u003c/strong\u003e\u003cstrong\u003ele\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4 Phosphorus release and uptake of the reactor at various phase\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8.6777%;\" width=\"10.989010989010989%\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.2369%;\" valign=\"top\" width=\"30.76923076923077%\"\u003e\n \u003cp\u003eAnaerobic phosphorus release/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26.0331%;\" valign=\"top\" width=\"28.414442700156986%\"\u003e\n \u003cp\u003eAnoxic phosphorus uptake/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6915%;\" valign=\"top\" width=\"29.356357927786497%\"\u003e\n \u003cp\u003eAerobic phosphorus uptake/mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.7741%;\" valign=\"top\" width=\"16.75485008818342%\"\u003e\n \u003cp\u003eTests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6006%;\" valign=\"top\" width=\"17.98941798941799%\"\u003e\n \u003cp\u003eSimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8099%;\" valign=\"top\" width=\"15.696649029982364%\"\u003e\n \u003cp\u003eTests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2231%;\" valign=\"top\" width=\"16.225749559082892%\"\u003e\n \u003cp\u003eSimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6364%;\" valign=\"top\" width=\"16.57848324514991%\"\u003e\n \u003cp\u003eTests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0193%;\" valign=\"top\" width=\"16.75485008818342%\"\u003e\n \u003cp\u003eSimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.6777%;\" valign=\"top\" width=\"10.989010989010989%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7741%;\" valign=\"top\" width=\"14.913657770800627%\"\u003e\n \u003cp\u003e7.19\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6006%;\" valign=\"top\" width=\"16.012558869701728%\"\u003e\n \u003cp\u003e8.45\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8099%;\" valign=\"top\" width=\"13.971742543171114%\"\u003e\n \u003cp\u003e0.27\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2231%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e0.33\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6364%;\" valign=\"top\" width=\"14.756671899529042%\"\u003e\n \u003cp\u003e6.52\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1928%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e7.87\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.6777%;\" valign=\"top\" width=\"10.989010989010989%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7741%;\" valign=\"top\" width=\"14.913657770800627%\"\u003e\n \u003cp\u003e7.24\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6006%;\" valign=\"top\" width=\"16.012558869701728%\"\u003e\n \u003cp\u003e8.34\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8099%;\" valign=\"top\" width=\"13.971742543171114%\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2231%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6364%;\" valign=\"top\" width=\"14.756671899529042%\"\u003e\n \u003cp\u003e5.81\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1928%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e7.52\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.6777%;\" valign=\"top\" width=\"10.989010989010989%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7741%;\" valign=\"top\" width=\"14.913657770800627%\"\u003e\n \u003cp\u003e15.63\u0026plusmn;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6006%;\" valign=\"top\" width=\"16.012558869701728%\"\u003e\n \u003cp\u003e16.71\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8099%;\" valign=\"top\" width=\"13.971742543171114%\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2231%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6364%;\" valign=\"top\" width=\"14.756671899529042%\"\u003e\n \u003cp\u003e15.48\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1928%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e16.68\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.6777%;\" valign=\"top\" width=\"10.989010989010989%\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7741%;\" valign=\"top\" width=\"14.913657770800627%\"\u003e\n \u003cp\u003e20.20\u0026plusmn;2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6006%;\" valign=\"top\" width=\"16.012558869701728%\"\u003e\n \u003cp\u003e21.33\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8099%;\" valign=\"top\" width=\"13.971742543171114%\"\u003e\n \u003cp\u003e0.12\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2231%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e0.20\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6364%;\" valign=\"top\" width=\"14.756671899529042%\"\u003e\n \u003cp\u003e20.03\u0026plusmn;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1928%;\" valign=\"top\" width=\"14.442700156985872%\"\u003e\n \u003cp\u003e23.13\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e3.3.1 Influence of changing anoxic tank volume on pollutant removal efficiency under C/N=7\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eUnder C/N=7, the anoxic tank volume increased from 1.8L at phase Ⅰ to 3.6 L at phase Ⅱ, the HRT of the anoxic tank increased from 1.8 h to 3.6 h, the effluent COD and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N decrease and the amount of phosphorus uptake in the anoxic tank was significantly increased from 0.27 mg/L to 1.11 mg/L (the simulated value increased from 0.33 mg/L to 1.06 mg/L) (Fig. 2 and Table 4). The test and simulation results showed that under C/N=7, increasing the volume of the anoxic tank had little effect on the COD and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N removal efficiency, but TNRE and TPRE were largely improved.\u003c/p\u003e\n\u003cp\u003eAs the volume of the anoxic tank increased, the removal efficiency of COD had less change in all phases (Fig. 2 and Fig. 4), because the removal of COD was mainly concentrated in the anaerobic and anoxic phases. In the anaerobic tank, the large molecules carbon source was converted into small molecules of volatile fatty acids (VFA), which were synthesized into PHB by the polyphosphate-accumulating organisms (PAOs) and stored in cells, and then the intracellular phosphorus was hydrolyzed to orthophosphate and released outside of the cell(Zhao et al.). In the anoxic tank, the remaining carbon source was utilized by the OHOs to reduce the nitrate returned from the aerobic tank, and DPAOs also underwent phosphorus uptake through denitrification(Xie et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn the anoxic tank, as HRT increased, the NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N content significantly reduced and the TNRE in phase II was higher than that of phase I. The NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N returned to the anoxic tank was fully utilized by denitrifying bacteria, which utilized NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N as an electron acceptor for energy metabolism leading to an increase of TNRE and a decrease in NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N content(Zhao et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe amount of phosphorus released in phase I and phase II in the anaerobic tank was basically the same, but the amount of phosphorus aggregated by phase II in the anoxic tank was much larger than that of phase I (Table 3). When denitrifying bacteria and PAOs were present at the same time, the denitrifying efficiency would be faster than the phosphorus release efficiency, and denitrifying bacteria would preferentially utilize the carbon source in the feed water, especially when the carbon source was insufficient. Meanwhile, the available carbon source to PAOs was decrease, resulting in insufficient phosphorus release and reducing the overall phosphorus removal capacity of the system(Jya et al.). Phase II increased the volume of the anoxic tank, enhanced the usage of dissolved biodegradable COD and endogenous carbon by denitrifying bacteria, reduced the COD flow to the aerobic tank and also provided sufficient electron acceptors for denitrification and phosphorus removal process. Adequate denitrification reduced the NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N content of the system and reduced the inhibitory effect on PAOs.\u003c/p\u003e\n\u003ch3\u003e3.3.2 Influence of changing anoxic tank volume on pollutant removal efficiency under C/N=10\u003c/h3\u003e\n\u003cp\u003eUnder C/N=10, the HRT of the anoxic tank increased from 1.8h at phase III to 3.6h at phase IV (Fig. 2 and Table 4), the COD and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N of the anoxic tank effluent decreased, the amount of phosphorus released in the anoxic tank increased significantly from 0.04 mg/L to 0.12 mg/L (the simulated value increased from 0.09 mg/L to 0.20 mg/L) in response to change in phases. Compared to the low C/N, less anoxic phosphorus uptake amount, more phosphorus uptake was found in the aerobic tank. The experimental and simulation results showed that under C/N=10, increasing the volume of the anoxic tank had insignificantly improved on NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and COD removal efficiency, while had a greater effect on the TNRE and TPRE.\u003c/p\u003e\n\u003cp\u003eCompared with phase Ⅰ and phase Ⅱ, the released phosphorus value from phase Ⅲ and phase Ⅳ in anaerobic tank was higher than that of previously phase (Fig.4 and Table 4). This was mainly attributed to enhancement of influent COD in phase Ⅲ and phase Ⅳ, thus, a sufficient COD was provided for PAOs, resulting in the increase of the phosphorus release. Phase Ⅳ anaerobic tank phosphorus release was higher than that of phase Ⅲ, because anoxic tank denitrification was sufficient in phase Ⅳ due to increase of anoxic tank volume, thus, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N concentration in the external return stream decreased(Meijiao et al., 2018). This result was due to the reduced inhibition of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N on the anaerobic release of phosphorus by PAO.\u003c/p\u003e\n\u003cp\u003ePhosphorus uptake was mainly concentrated in the aerobic tank, and the amount of phosphorus uptake in the anoxic tank was limited, indicating insufficient denitrification of phosphorus uptake reaction was little. As the influent COD increased, denitrifying bacteria competed for the external carbon source and the denitrification reaction was more complete, therefor DPAOs had insufficient electron acceptors to phosphorus uptake(Wei et al., 2019).\u0026nbsp;\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eUnder the conditions of influential C/N=7, changing the HRT of the anoxic tank at 1.8h~4.5h had a significant effect on the TNRE and TPRE in the A\u003csup\u003e2\u003c/sup\u003eO process. The optimal solution obtained was that the HRT of the anoxic tank were 3.8-4.0h, and the corresponding volume ratio of anaerobic, anoxic and aerobic was 1:2.4:6.75. The corresponding TNRE and TPRE at this point were 76.13% and 96.5%, respectively. Under C/N=10 conditions, changing the HRT of the anoxic tank from 1.8h to 4.5h exhibited limited effect on nitrogen and phosphorus removal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability statement\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, Jianqiang Zhao, upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 51778057).\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eSample CRediT author statement\u003c/h2\u003e\n\u003cp\u003eShuhan Lei: Conceptualization, Project administration, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Jianqiang Zhao: Supervision, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing. Junkai Zhao: Investigation, Writing \u0026ndash; review \u0026amp; editing. ShutingXie: Investigation, Methodology. Ju Zhang: Investigation. Bingfeng Shi: Methodology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, N., Ahmadi, M., Naseri, M., 2021. Quality and cost analysis of a wastewater treatment plant using GPS-X and CapdetWorks simulation programs. J Environ Manage 284.\u003c/li\u003e\n\u003cli\u003eAfonso, P., Maria, D.C.O.C., 2002. 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Bioresource Technology 319.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"A2O process, anoxic tank, HRT, GPS-X, optimal HRTs","lastPublishedDoi":"10.21203/rs.3.rs-2058607/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2058607/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In order to improve the nitrogen removal efficiency of the anaerobic-anoxic-aerobic process (A2O), the effect of anoxic tank volume on the total nitrogen (TN) removal efficiency and total phosphorus (TP) removal efficiency were investigated under different ratios of chemical oxygen demand (COD) to nitrogen (C/N). The results showed that under C/N=7, the HRT of anoxic tank increased from 1.8 h to 3.6 h had little effects on the removal efficiency of COD, but largely improving the removal efficiency of TN and TP which increased from 66.8% and 93.8% to72.0% and 95.2%, respectively. Under C/N=10, increasing the volume of the anoxic tank did not significantly improve the removal efficiency of COD, TN and TP. The optimal HRTs of anoxic tank and the corresponding volume ratios of anaerobic, anoxic and aerobic were 4.0 h, 4.3 h and 1:2.5:6.75, 1:2.69:6.75 simulated by GPS-X software under C/N=7 and C/N=10, respectively. The results proposed that the appropriate elevated the volume of the anoxic tank could help A2O process to meet the TN discharge concentration lower than 15 mg/L. Under low C/N, changing the HRT of anoxic tank to improve the pollutant removal efficiency of A2O reactor was more significantly, so this method was more suitable for the case of insufficient carbon source.","manuscriptTitle":"Optimization of A2O Process by Changing Anoxic Tank Volume Based on GPS-X Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-11-07 14:45:30","doi":"10.21203/rs.3.rs-2058607/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2022-09-15 20:53:18","doi":"10.21203/rs.3.rs-2058607/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e2f2ab8-b405-4438-aedd-b15ea27f4f18","owner":[],"postedDate":"November 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-06T14:29:25+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-07 14:45:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-2058607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2058607","identity":"rs-2058607","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}