Simultaneous Removal of Organic Matter and Nutrients from High Strength Organic Wastewater Using Sequencing Batch Reactor (SBR)

Abstract

Industrial wastewater discharges often contain high levels of organic matter and nutrients, which can lead to eutrophication and constitute a serious hazard to receiving waters and aquatic life. The purpose of this study was to examine the efficacy of using a sequencing batch reactor (SBR) to treat high-strength organic wastewater for the removal of both chemical oxygen demand (COD) and nutrients (nitrogen and phosphorus). At a constant COD concentration of approximately 1000 mg/L, the effects of cycle time (3 and 9 h) and various C:N:P ratios (100:5:2, 100:5:1, 100:10:1, and 100:10:2) were investigated using four identical SBRs (R1, R2, R3, and R4). According to experimental data, a significant high removal, i.e., 90%, 98.5%, and 84.8%, was observed for COD, NH₃-N, and PO₄³⁻-P, respectively, when C:N:P was 100:5:1, at a cycle time of 3 h. Additionally, when cycle time was increased to 9 h, the highest levels of COD removal (95.7%), NH₃-N removal (99.6%), and PO₄³⁻-P removal (90.31%) were accomplished. Also, in order to comprehend the primary impacts and interactions among the various process variables, the data was statistically examined using analysis of variance (ANOVA) at a 95% confidence level, which revealed that the interaction of cycle time and C/N ratio, cycle time and C/P ratio is significant for COD and NH₃-N removal. However, the same interaction was found to be insignificant for PO₄³⁻-P removal. Sludge volume index (SVI₃₀ and SVI₁₀) and sludge settleability were studied, and the best settling was found in R3 with SVI₃₀ of 55 mL/g after 9 h. Further evidence that flocs were present in reactors came from an average ratio of SVI ₃₀/SVI ₁₀ = 0.70 after 9 h and 0.60 after 3 h.

1. Introduction

Wastewater generation due to increased industrialization, urbanization, and rapid population growth has led to a lack of safe water and eutrophication [1]. The scarcity of safe drinking water has become a global issue, and problems related to water contamination are on the rise [2] In developing countries, most of the sewage treatment plants are designed to remove organic matter, and there are no such stringent regulations governing nutrient discharge in surface waters. However, organic matter is not the only pollutant in sewage, and managing nitrogen forms such as ammonia, nitrate, and phosphorus-containing discharge into water bodies has become a worldwide concern in sustaining surface water quality [3,4]. Nitrogen (N) and phosphorus (P) are of vital importance for the growth and development of living organisms, but an excess of these causes adverse environmental impacts such as surface water algal blooms (eutrophication), toxicity toward aquatic organisms, and depletion of dissolved oxygen in water as a result of oxidation of ammonia to nitrate by bacteria [5,6,7].

To date, various methods, such as physical, chemical, biological, and hybrid methods have been developed for the treatment of raw wastewater containing organic matter as well as nutrient discharge. Coagulation/flocculation, chemical precipitation, ion exchange, adsorption/filtration, membrane filtration, and electro-coagulation are among the technologies used for nutrient removal in wastewater treatment [8,9,10,11,12,13]. However, these remediation technologies have some drawbacks, such as a high initial capital cost, skilled manpower for operation and maintenance, the addition of chemicals and adsorbents, and so on [14]. Biological wastewater treatment, on the other hand, has attracted a lot of attention in recent years due to its environmental sustainability, high efficiency, and cost effectiveness. [15]. Sequencing Batch Reactor (SBR) employing simultaneous nitrification and denitrification has proven its capabilities for biological nutrient removal (BNR), hence safeguarding water from eutrophication [16]. SBR is a time-sequenced suspended growth treatment technology in which different treatment operations take place in the same tank or single batch reactor [17,18]. Different phases for operation of SBR include the fill phase, react phase (aerobic, anaerobic, and anoxic), settle phase, decant phase, and idle phase [19]. The advantages of using SBR are: (i) reaction and sedimentation take place in the same reactor, so no extra clarifier is needed for biomass settling [17,20]; (ii) operational flexibility; and (iii) low cost and easy management as a result of simple setup. Furthermore, when compared to traditional biological nitrogen removal procedures, SBRs using SND have the ability to remove nitrogen more effectively in a single reactor while retaining the same overall operating conditions [21]. The typical mechanism of SND is shown in Figure 1.

Recent literature reports a number of studies examining the efficacy of SBRs in treating various types of wastewaters, including municipal sewage, woodchips, dairy, and synthetic wastewater [22,23,24,25,26,27,28,29]. Different modification in SBR reactor has been documented like, integrated fixed film activated sludge (IFAS) reactor combined with SBR was used for municipal wastewater treatment by varying different organic loadings [26]; aerobic granular sequencing batch reactor (AGSBR) was used for the treatment of high strength organic wastewater in SBR (AGSBR) and removal efficiencies of 97% COD, 100% total nitrogen and 98% P removal were achieved at C:N:P of 250:2.77:1 [30]; a moving bed sequencing batch reactor (MBSBR) was used to study the effect of DO, pH, C/N and cycle time on removal [6]. By using the advantages of SBR and continuous flow, a novel system was developed by Li et al., in 2019, which revealed that HRT of 9 h and aeration intensity of 9.74/h proved to be beneficial for nutrient removal [31]. Furthermore, a recent study used a hybrid reactor to treat bulk organic contaminants and disinfect E. coli while simultaneously using hydrodynamic cavitation and plasma discharge with powerful radical, UV radiation, shock wave, and charged particle creation [32].

The effect of different process parameters such as influent COD, cycle time, aeration time, mixed liquor suspended solids (MLSS), C/N ratio, dissolved oxygen (DO), and pH has been extensively studied [6,25,33,34,35]. The direct effect of HRT, air flow rate, and their interactive effects revealed that HRT was the more significant factor for nitrogen and carbon removal from soft drink wastewater and its optimal value was 11 h [35]. The effect of two independent variables, i.e., cycle time and aeration time, revealed that an intermittent aeration strategy with an optimum cycle time of 6.5 h and an aeration time of 40 min/h results in 85.2% COD removal, 71% total nitrogen removal, and 67% phosphorus removal [19]. According to Wang et al., 2019 [36], the C/N ratio was one of the most significant factors influencing aerobic granular sludge for pollutant removal in an aerobic granular sludge sequencing batch reactor [31].

To a great extent, the C/N ratio of wastewater represents the relative proportion of carbon and nitrogen sources to a great extent and affects the growth and competition of heterotrophic and autotrophic nitrifying bacteria [26,28]. The optimal C/N ratio for the SND process reported in the literature ranges from 7 to 20 [34,37,38,39]. So far, a lot has been said about how the C/N ratio affects nutrient and organic matter removal, but less has been understood about how the C/P ratio affects nutrient and organic matter removal individually and in combination with the C/N ratio.

In this study, a detailed review of literature was conducted in order to select the various process variables, and lab-scale SBRs were fabricated to treat the synthetic wastewater. The current study aims to investigate the influence of cycle time and the combination of different C/N and C/P ratios on COD and nutrient removal processes. Experimental conditions were designed and COD, ammonia (NH₃-N), and phosphorus (PO₄³⁻-P) removal were studied as process responses. Also, the influence of cycle time on sludge quality was reported using sludge volume index.

In addition, a broad comparison of the present study with previously reported findings is included in this paper.

2. Materials and Methods

2.1. Reactor Setup and Operation

Four identical cylindrical plexiglass columns (R1, R2, R3, and R4) with an effective working volume of 3.5 L and a height to diameter (H/D) ratio of 6.3 were set up for granule cultivation in a sequencing batch reactor mode. The schematic diagram of the sequencing batch reactor (SBR) is shown in Figure 2 and the lab-scale experimental setup used in this study is shown in Figure S1.

The sequence of SBR operation was controlled manually (feed, react, settle, and decant) and was operated with a 3 and 9 h cycle time as shown in Table 1. Influent was added instantaneously to the reactor while 1.75 L of effluent was discharged at the end of settling through an outlet point of the reactor placed at the middle height of the reactor, resulting in a volumetric exchange ratio (VER) of 50%. Fine air bubbles were distributed at the bottom of the reactor with the help of diffuser stones attached to the aquarium pumps. The experiments were performed at room temperature and pH was maintained 7.6 ± 0.2 using 1 N NaOH and 1 N H₂SO₄.

2.2. Seed Sludge and Wastewater Composition

All the reactors were inoculated with activated seed sludge from an aeration tank at Verka Milk Plant, Amritsar. Reactors were fed with synthetic wastewater prepared in the laboratory, which mainly consisted of glucose as a carbon source, ammonium chloride as a nitrogen source, and potassium dihydrogen orthophosphate as a phosphorus source. Sodium bicarbonate was added as a source of alkalinity. Trace elements were also added to the reactors. The composition of synthetic wastewater is given in Table 2. The COD conc. = 1000 mg/L was fixed, and the conc. of N and P was varied in order to have different C/N and C/P ratios. Different reaction times for different phases were set in accordance with the cycle times. Initially, 1 L of inoculum sludge and 2.5 L of synthetic wastewater were added to each reactor. After initial dilution, MLSS in reactor R1 = 5420 mg/L, R2 = 5750 mg/L, R3 = 5060 mg/L, R4 = 5140 mg/L. Operating conditions for reactors (R1, R2, R3, and R4) are given in Table 3.

2.3. Analytical Methods

COD (mg/L), PO₄³⁻ (mg/L), NH₃-N (mg/L), MLSS (mg/L), Sludge Volume Index (SVI₃₀) were estimated by standard methods [40]. For COD, the closed reflux titrimetric method was used. The ammonia selective electrode method was used for NH₃-N and the stannous chloride method was used after filtration for the calculation of orthophosphate (PO₄³⁻P). MLSS was calculated using the weighing method. SVI₁₀ and SVI₃₀ were measured by dividing settled sludge volume after 10 and 30 min, respectively, by the MLSS of mixed samples taken after the end of the aeration period. The ratio SVI₃₀/SVI₁₀ was also determined.

2.4. Calculations

The removal efficiency of C, N and P was calculated by Equation (1):

(1)$\mathrm{Removal} \mathrm{efficiency}=\frac{\left(Cin-Ce\right)}{Cin}\times 100\%$

where C_in means pollutant conc. in influent and C_e means pollutant conc. in effluent.

3. Results and Discussion

3.1. Effect of Cycle Time and C:N:P Ratios on Reactor Performance

The influent COD conc. was fixed at 1000 mg/L and the influent NH₃-N and PO₄³⁻P conc. were varied to provide different C/N and C/P ratios. Examination of effects of the C/N and C/P ratios on organic matter and nutrient removal was done in two phases, Phase I (cycle time = 3 h) and Phase II (cycle time = 9 h). The time series degradation profiles of COD, NH₃-N, and PO₄³⁻-P during SBR operation are shown in Figure 3.

3.1.1. COD Removal in SBR

For wastewaters with high organic content, carbon removal is favored by the combination of processes, with or without oxygen, in a sequencing batch reactor [41]. The process performance in terms of total COD removal under different conditions is documented in this study. Glucose, solely used as a carbon source as glucose is considered to be a readily biologically degradable organic matter source as compared to starch, which is a complex macromolecule, the former can be consumed by biomass in a short span of time [15]. In the present system, excellent performance in organic carbon removal (COD) was observed. COD removal efficiency greater than 88% was achieved throughout all the experimental runs.

The COD degradation profile (Figure 3), showed a faster trend at a mere 3 h cycle time (Phase 1) when C:N:P was 100:10:2 and reached 95% as the cycle time increased in Phase 2. This may be due to the fact that most of the COD available was readily degraded in the first few hours of reactor operation [29]. C:N:P ratio of 100:5:2 also yields an almost similar COD removal efficiency (95%) in Phase 2. The rate of COD removal decreased to 90% with a C:N:P ratio of 100:5:1 as compared to that of COD removal at a C:N:P ratio of 100:10:1 after 3 h cycle time. Whereas the removal efficiency appears to increase with an increase in cycle time from 3 to 9 h (96% for 100:5:1; 94% for 100:10:1) This might be because longer cycle times often give bacteria more time to interact with the substrate and absorb organic matter. [42]. However, it is clear from Figure 3c,d that the increments in COD removal efficiency were not considerable when cycle time increased from 3 to 9 h in the experiments with C:N:P 100:10:1 and 100:10:2.

3.1.2. N Removal

NH₃-N removal was governed by the SND process in which nitrification is the autotrophic oxidation of ammonia first to nitrite, and then nitrite to nitrate in the presence of oxygen, followed by denitrification, which occurs in the absence of oxygen, releasing gaseous nitrogen compounds [41]. The C/N ratio in the influent must be sufficient for denitrification of all nitrates produced during nitrification [42]. The effect of various C:N:P ratios under two different cycle times on NH₃-N removal is shown in Figure 3. The average efficiency of the four SBRs was 93% and they all achieved high NH₃-N removal in Phase 2 (9 h) under all C:N:P ratios. The concentration of NH₃-N in the effluent was also consistently less than 18 mg/L. However, when the cycle time was 3 h in Phase 1, the highest removal (99%) with effluent NH₃-N concentration reaching less than 1 mg/L was achieved in reactorsR1 and R2 Whereas, as C:N was reduced to 10 (C:P = 100 and C:P = 50), a significant decrease in NH₃-N removal efficiency (60–75%) was observed, as shown in Figure 3c,d. A study by Chen et al., 2018 reported that as the C/N ratio decreases, nitrogen removal is also reduced and the morphology of aerobic granular sludge gets worsened; in order to restore it, the C/N ratio must be increased from 100/10 to 100/5 [43]. As NH₃-N reduction efficiency was higher than 90% in R1 and R2, it could be established that the lack of nitrogen removal was mainly because of inadequate denitrification when the organics were not enough for denitrifiers along with the nitrate and nitrite accumulation [44,45]. For low strength wastewater, with C:N:P of 100:10:2 (C/N = 10, C/P = 50), 90.7% removal of nitrogen in 6 h cycle time was reported [22]. Also, for woodchips wastewater having C/N = 20 and C/P = 100, 96.7% NH₃-N Removal was achieved in 24 h cycle time [46].

3.1.3. P Removal

Phosphorus accumulating organisms, i.e., PAOs, compete with bacteria responsible for denitrification for readily degradable organic matter, and denitrifying bacteria proliferate rapidly due to the high concentration of nitrogen in influent thus out competing phosphorus accumulating organisms and inhibiting P removal [36,47]. In this study, the average effluent PO₄³⁻ conc. and efficiency were 5 mg/L and 70%, respectively. Also, the lowest removal efficiency was detected at a C:N:P ratio of 100:5:2 [Figure 3a] (C/N = 20, C/P = 50) (C/N = 20, C/P = 50), with 43.3% P removal after 3 h and 53.8% P removal after 9 h Figure 3b shows removal percentage at a conventional organics to nutrient ratio i.e., 100:5:1, while dealing with high strength synthetic wastewater, PO₄³⁻P removal was 85% with effluent PO₄³⁻P conc. 1.5 mg/L at 3 h. On the other hand, in Phase 2, the effluent conc. of PO₄³⁻P reached less than 1 mg/L achieving 90% removal, which is the maximum of all the operational runs. For low COD, 97% PO₄³⁻P at a similar C:N:P ratio of 100:4.9:1.36 in 6 h cycle time was observed [36]. When C:N:P was set to 100:10:1 (C/N = 10, C/P = 100), similar results were obtained as with 100:5:1 in 9 h cycle time (Figure 3c), with effluent concentrations reaching 1 mg/L and around 1.9 mg/L at 3 h operation time. This shows that at C/P = 100, the PO₄³⁻P removal rate was stable and maintained at 90% for the 9 h cycle time. At C:N:P ratio 100:10:2 (C/N = 10, C/P = 50), 54% removal at 3 h and 63% removal of PO₄³⁻ at 9 h occurs with effluent concentration of 9 mg/L and 7.4 mg/L respectively (Figure 3d).

3.1.4. Sludge Volume Index

In order to demonstrate bio-sludge settling ability, sludge volume index (SVI) was calculated for all operating conditions. The measurement of SVI in the laboratory is shown in Figure S2. In the settle stage of SBR, it is the best parameter for observing sludge settling property and sludge bulking. In the case of biological treatment operations, low SVI values due to high settling velocity are considered much more favorable [15]. SVI for the activated sludge process typically has values under 100 mL/g [23], while values over 150 mL/g are indicative of sludge thickening [48]. According to Figure 4, SVI₁₀ values vary from 107.8 to 132.8 mL/g in Phase 1 (cycle duration = 3 h), with an average value of 124 mL/g. The average SVI₁₀ was 86.1 mL/g in Phase 2, when the cycle length was raised from 3 to 9 h. In addition, the SVI after 30 min (SVI₃₀) was within the normal range for an activated sludge process, varying from 67–79 mL/g for 3 h and 55–64 mL/g for 9 h. An SVI₃₀ value of 81 mL g1 was attained under ideal circumstances with an MLSS of 3000 mg L1, a cycle period of 24 h, and an initial COD content of 1000 mg L1, according to a study by Azimi et al., 2019 [23]. Gholami et al., 2020 [35] reported that the bioreactor produced SVI₃₀ values between 60.5 and 92.5 mL/g due to the predominance of HRT on SVI₃₀. Furthermore, other studies have found that the SVI₃₀ typically ranges between 50 and 108 mg/L [15,49,50]. Since cycle time and SVI have an inverse relationship, the SVI declined as the cycle time increased from 12 to 24 h, and then increased to 36 h [23]. Similar findings were obtained in the current investigation, where SVI values in reactors decreased as cycle time increased (3 to 9 h) while maintaining a constant COD value of 1000 mg/L. While considering the level of granulation in SBR, the low SVI values alone do not suggest complete granulation, and another key parameter, ratios SVI₃₀/SV_I5, SVI₃₀/SVI₁₀, has been reported by several researchers to measure the degree of granulation. [27,51,52]. Good sludge performance is considered to be achieved if the value of these ratios is close to 1. As shown in Figure 4, the SVI ₃₀/SVI₁₀ ratio in Phase 1 was in the range of 0.54–0.66, and as the cycle length was extended in Phase 2, the SVI ₃₀/SVI ₁₀ values improved to 0.78, 0.69, 0.61, and 0.75 for reactors R1, R2, R3 and R4, respectively. Derlon et al., 2016 reported that these ratios have been observed to indicate the existence of flocs in the reactors and that as the granule fraction grows, so does this ratio [52]. Also, the values of SVI₃₀/SVI₁₀ in the present study were lower than those of the reported studies [51,52]. In reactors R1 and R4, the ratios were high (0.78 and 0.75, respectively), and were close to 1, which indicated more granular sludge formation as compared to R2 and R3.

3.2. Statistical Analysis of Tested Variables

For determining the significance of each variable, the F value and the values of probability >F were used. Statistical analysis of the data was conducted using SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY, USA) and is shown in Table 4. The ANOVA results for response, COD removal are displayed in Table 4. The significance of every single term in the model was considered by testing the null hypothesis and by determining the F values. At 95% confidence level, the F-value of the model (36.284) with a p-value less than 0.05 (<0.001) was obtained, which showed that the model is significant at this level. Also, the correlation coefficient, i.e., R² for each response was computed and it shows that the amount of variation around the mean by the model [53]. For COD removal efficiency, coefficient of determination, R² = 0.841 and adjusted R² = 0.818 were achieved. The test showed that there was a significant individual main effect of C/N, C/P, and cycle time on COD removal efficiency at p < 0.05. Also interaction effects between Cycle Time and C/N, Cycle Time and C/P were found to be significant. On the other hand, interaction s between C/N and C/P were insignificant.

ANOVA results for NH₃-N removal efficiency are shown in Table 4. The model F value for nitrogen removal percentage was 309.480. The greater F values for the variables’ cycle time and C/N ratio, and their interaction with each other, showed a more significant effect on removal at p < 0.05. However, all the model terms were significant, with p < 0.001 at a 95% confidence level. Besides this, a high coefficient of determination, i.e., R² = 0.978 and adjusted R² = 0.975, was achieved.

From the ANOVA table shown (Table 4), the interaction of cycle time and C/N ratio was insignificant on phosphate removal, and the most significant variables were cycle time and C/P ratio, with model F-values of 439.806 and 6844.531, respectively, at a 95% confidence level. Similar findings were reported by Akhbari et al., 2011 [35], indicating that HRT and C/P ratio are the most important factors in PO₄³⁻-P removal. [41]. The F-value for the model was 1089.765, which suggested that the model is significant. The value of coefficient of determination is high (0.994) and is in reasonable agreement with adjusted R² (0.994).

3.3. Comparison of Present Study with Literature

A detailed review of the literature was done, and various studies were compared for analyzing process parameters such as cycle Time, and C:N:P ratio. A graphical presentation with minimum, maximum, and optimum values is plotted in Figure 5A Cycle Time [B] C/N ratio [C] C/P ratio. Earlier studies reported in Tables S1–S3 show that C, N and P removal efficiency was realized over a wide range of cycle times (6.5–41 h), C/N ratio (5–20), and C/P ratio (20–250). From Figure 5, it is clear that the best operating range for cycle time is 3–12 h, C/N is 5–20 and C/P is 25–100. Similarly, complete N removal at a C/N ratio of 20 over a 6 h cycle time was reported for an aerobic granular SBR (AGSBR) in which high dissolved oxygen, i.e., 7–8 mg/L, was primarily responsible for 100% N and 98% P removal [29]. For synthetic wastewater with an influent COD of 1000 mg/L, a cycle time of 6.5 h with an intermittent aeration strategy resulted in COD, N, and P removal efficiencies of 93.5%, 79.2%, and 63.75%, respectively [37]. Also, Jaafari et al., 2017 and Zinatizadeh et al., 2015 reported 10-12 h of cycle time requirement for low strength wastewater (COD~500 mg/L) [54,55]. Extremely long cycle time of around 38 h was observed for high COD wastewater resulting in 84.78 % COD Removal and 82.72 % N removal [56]. Normally, a wastewater having a C/N ratio greater than 10 is considered to have good nitrogen removal performance for the SND process [45]. Low C/N ratios can result in carbon deficits, which can disrupt the SND process [12]. Low C/N ratios can cause carbon deficit leading to a disturbed SND process [12]. In this study, a C:N:P ratio of 100:5:1 (C/N = 20; C/P = 100) was found to be sufficient for COD, N and P cycle at 9 h cycle time. However, a cycle time of 3 h, which is the lowest among the already reported studies, also proves to be sufficient for high COD, NH₃-N, and PO₄³⁻P removal at the same ratio (Figure 3b).

4. Conclusions

In the present study, the effect of cycle time and C:N:P ratio on organic matter and nutrient (N & P) removal from simulated wastewater was explored using a laboratory scale SBR setup.

The main conclusions from the present study can be summarized as follows:

• The C:N:P ratio 100:5:1 (C/N = 20; C/P = 100) proposed for conventional activated sludge process was found to be sufficient for biomass growth and nutrient removal from high strength synthetic wastewater used in the present study;

• Excellent effluent quality with COD conc. < 50 mg/L and PO₄³⁻ P conc. ~1 mg/L was attained at cycle time of 9 h in reactor R2. Almost complete NH₃-N removal was also observed in the same. In addition, when the cycle time was reduced to 3 h, the removal efficiencies were quite encouraging (COD = 90%; NH₃-N = 98.5%; PO₄³⁻P = 84.5%);

• Statistical analysis indicates that cycle time, carbon to nitrogen, and carbon to phosphorus all have significant individual main effects on NH₃-N and PO₄³⁻-P removal at p < 0.05. COD removal, however, was not significantly affected by the C/N ratio. On NH₃-N removal, there were also significant interaction effects between cycle time and C/N, cycle time and C/P, and C/N and C/P. Furthermore, the interaction effects of cycle time and C/N, as well as cycle time and C/P, were found to be insignificant for PO₄³⁻P removal;

• The coefficient of determination (R²) for COD, NH₃-N and PO₄³⁻ P removal was 0.841, 0.978, and 0.994, which suggested that there was very little variation in data that could not be explicated by the fitted model;

• SVI₃₀ and SVI_{10 ratio} were found to decrease with an increase in cycle time from 3 to 9 h;

• Further, the ratios of SVI₃₀ and SVI₁₀ were less than 1, which concluded that granulation was not complete in all the reactors.

Author Contributions

The idea was conceptualized and supervised by M.S.B. and A.S. carried out all the experiments and prepared the original draft. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are available in a Supplementary File.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Mechanism of simultaneous nitrification denitrification (SND) for biological nitrogen and phosphorus removal.

Figure 2. Schematic diagram of Lab scale sequencing batch reactor: (i) fill phase (ii) react phase (iii) settle phase; and (iv) decant phase.

Figure 3. Time series degradation profile of COD, NH3-N and PO43−-P at different C:N:P ratios: (a) 100:5:2; (b) 100:5:1; (c) 100:10:1; and (d) 100:10:2.

Figure 3. Time series degradation profile of COD, NH3-N and PO43−-P at different C:N:P ratios: (a) 100:5:2; (b) 100:5:1; (c) 100:10:1; and (d) 100:10:2.

Figure 3. Time series degradation profile of COD, NH3-N and PO43−-P at different C:N:P ratios: (a) 100:5:2; (b) 100:5:1; (c) 100:10:1; and (d) 100:10:2.

Figure 3. Time series degradation profile of COD, NH3-N and PO43−-P at different C:N:P ratios: (a) 100:5:2; (b) 100:5:1; (c) 100:10:1; and (d) 100:10:2.

Figure 4. Sludge volume index (SVI30 and SVI 10) and SVI 30/SVI 10 ratios at two different phases (Phase 1: cycle time = 3 h; Phase 2: cycle time = 9 h).

Figure 5. Review graphs for operating range along with optimum values for: (A) cycle time; (B) C/N ratio; and (C) C/P ratio. References included are (A) [15,19,23,24,25,28,35,37,44,53,54,55,56]; (B) [6,22,26,28,29,30,34,36,37,44,45,53]; (C) [19,22,23,24,30,35,37,54].

Figure 5. Review graphs for operating range along with optimum values for: (A) cycle time; (B) C/N ratio; and (C) C/P ratio. References included are (A) [15,19,23,24,25,28,35,37,44,53,54,55,56]; (B) [6,22,26,28,29,30,34,36,37,44,45,53]; (C) [19,22,23,24,30,35,37,54].

Table 1

Operational cycle for SBRs.

Cycle Time (h)	Fill	Anaerobic (min)	Aerobic(min)	Settle(min)	Decant (min)
3 h	Instantaneous	30	135	10	5
9 h	Instantaneous	60	455	20	5

Table 2

Synthetic wastewater composition.

S. No.	Chemical Name	Conc. (g/L)
1	Glucose	40
2	NH4Cl	7.638
3	KH2PO4	4.39
4	NaHCO3	13.76
5	Trace Elements(1 mL/L each)
	FeCl3.6H2O	2
	MnSO4.H2O	1
	Boric Acid	2

Table 3

Operational parameters in reactors.

Parameter	R1	R2	R3	R4
NH3-N (mg/L)	50	50	100	100
PO43−P (mg/L)	20	10	10	20
VER (%)	50	50	50	50
MLSS (mg/L)	5420	5750	5060	5140

Table 4

(a) Tests of Between-Subjects Effects (dependent variable: COD removal); (b) tests of Between-Subjects Effects (dependent variable: NH₃-N removal); and (c) tests of Between-Subjects Effects (where dependent variable: PO₄³⁻-P removal).

(a)
Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	175.131 a	7	25.019	36.284	<0.001 *
Intercept	491,512.731	1	491,512.731	712,829.238	<0.001 *
CT	123.611	1	123.611	179.271	<0.001 *
CN	3.703	1	3.703	5.370	0.025 *
CP	11.703	1	11.703	16.972	<0.001 *
CT * CN	22.126	1	22.126	32.088	<0.001 *
CT * CP	10.286	1	10.286	14.917	<0.001 *
CN * CP	0.000	1	0.000	0.000	1.000 *
CT * CN * CP	3.703	1	3.703	5.370	0.025 *
Error	33.097	48	0.690
Total	491,720.960	56
Corrected Total	208.229	55
(b)
Corrected Model	10,443.882 a	7	1491.983	309.480	<0.001 *
Intercept	432,266.631	1	432,266.631	89,664.544	<0.001 *
CT	1407.390	1	1407.390	291.933	<0.001 *
CN	7133.790	1	7133.790	1479.753	<0.001 *
CP	197.934	1	197.934	41.057	<0.001 *
CT * CN	1200.300	1	1200.300	248.977	<0.001 *
CT * CP	170.417	1	170.417	35.349	<0.001 *
CN * CP	188.410	1	188.410	39.082	<0.001 *
CT * CN * CP	145.641	1	145.641	30.210	<0.001 *
Error	231.405	48	4.821
Total	442,941.918	56
Corrected Total	10,675.287	55
(c)
Corrected Model	16,974.379 a	7	2424.911	1089.765	<0.001 *
Intercept	274,623.247	1	274,623.247	123,416.787	<0.001 *
CT	978.643	1	978.643	439.806	<0.001 *
CN	210.908	1	210.908	94.783	<0.001 *
CP	15,230.240	1	15,230.240	6844.531	<0.001 *
CT * CN	2.155	1	2.155	0.969	0.330 *
CT * CP	22.360	1	22.360	10.049	0.003 *
CN * CP	507.572	1	507.572	228.105	<0.001 *
CT * CN * CP	22.501	1	22.501	10.112	0.003 *
Error	106.808	48	2.225
Total	291,704.435	56
Corrected Total	17,081.187	55

(a) ^a R²= 0.841 (Adjusted R² = 0.818); (b) ^a R² = 0.978 (Adjusted R² = 0.975); (c) ^a R² = 0.994 (Adjusted R² = 0.993), * Significant at p ≤ 0.05.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr10101903/s1, Figure S1. Lab scale setup of sequencing batch reactor (SBR). Figure S2. Sludge volume index measurement. Table S1. Comparative results of cycle time effect on removal efficiencies with reported studies. Table S2. Comparative results of C/N ratio effect on removal efficiencies with reported studies. Table S3. Comparative results of C/P ratio effect on removal efficiencies with reported studies.

References

1. Chang, M.; Wang, Y.; Pan, Y.; Zhang, K.; Lyu, L.; Wang, M.; Zhu, T. Nitrogen removal from wastewater via simultaneous nitrification and denitrification using a biological folded non-aerated filter. Bioresour. Technol.; 2019; 289, 121696. [DOI: https://dx.doi.org/10.1016/j.biortech.2019.121696] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31252319]

2. Hojjati-Najafabadi, A.; Mansoorianfar, M.; Liang, T.; Shahin, K.; Wen, Y.; Bahrami, A.; Vasseghian, Y. Magnetic-MXene-based nanocomposites for water and wastewater treatment: A review. J. Water Process Eng.; 2022; 47, 102696. [DOI: https://dx.doi.org/10.1016/j.jwpe.2022.102696]

3. Kartal, B.; Kuenen, J.V.; Van Loosdrecht, M.C.M. Sewage treatment with anammox. Science; 2010; 328, pp. 702-703. [DOI: https://dx.doi.org/10.1126/science.1185941] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20448175]

4. Zuthi, M.F.R.; Guo, W.S.; Ngo, H.H.; Nghiem, L.D.; Hai, F.I. Enhanced biological phosphorus removal and its modeling for the activated sludge and membrane bioreactor processes. Bioresour. Technol.; 2013; 139, pp. 363-374. [DOI: https://dx.doi.org/10.1016/j.biortech.2013.04.038]

5. Bărbulescu, A.; Barbeş, L. Statistical methods for assessing water quality after treatment on a sequencing batch reactor. Sci. Total Environ.; 2021; 752, 141991. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.141991]

6. Wang, J.; Rong, H.; Cao, Y.; Zhang, C. Factors affecting simultaneous nitrification and denitrification (SND) in a moving bed sequencing batch reactor (MBSBR) system as revealed by microbial community structures. Bioprocess Biosyst. Eng.; 2020; 43, pp. 1833-1846. [DOI: https://dx.doi.org/10.1007/s00449-020-02374-w]

7. Zhang, L.; Huang, Y.; Li, S.; He, P.; Wang, D. Optimization of Nitrogen Removal in Solid Carbon Source SND for Treatment of Low-Carbon Municipal Wastewater with RSM Method. Water; 2018; 10, 827. [DOI: https://dx.doi.org/10.3390/w10070827]

8. Jiang, J.Q. The role of coagulation in water treatment. Curr. Opin. Chem. Eng.; 2015; 8, pp. 36-44. [DOI: https://dx.doi.org/10.1016/j.coche.2015.01.008]

9. Wang, X.J.; Xia, S.Q.; Chen, L.; Zhao, J.F.; Renault, N.J.; Chovelon, J.M. Nutrients removal from municipal wastewater by chemical precipitation in a moving bed biofilm reactor. Process Biochem.; 2006; 41, pp. 824-828. [DOI: https://dx.doi.org/10.1016/j.procbio.2005.10.015]

10. Huang, X.; Guida, S.; Jefferson, B.; Soares, A. Economic evaluation of ion-exchange processes for nutrient removal and recovery from municipal wastewater. NPJ Clean Water; 2020; 3, 7. [DOI: https://dx.doi.org/10.1038/s41545-020-0054-x]

11. Bolton, L.; Joseph, S.; Greenway, M.; Donne, S.; Munroe, P.; Marjo, C.E. Phosphorus adsorption onto an enriched biochar substrate in constructed wetlands treating wastewater. Ecol. Eng.; 2019; 142, 100005. [DOI: https://dx.doi.org/10.1016/j.ecoena.2019.100005]

12. Kumari, R.; Ankit, H.; Basu, S. Reclamation of water from dairy wastewater using membrane bioreactor (MBR)–Membrane filtration processes. Mater. Today Proc.; 2021; 47, pp. 1452-1456. [DOI: https://dx.doi.org/10.1016/j.matpr.2021.03.452]

13. Swain, K.; Abbassi, B.; Kinsley, C. Combined electrocoagulation and chemical coagulation in treating brewery wastewater. Water; 2020; 12, 726. [DOI: https://dx.doi.org/10.3390/w12030726]

14. Crini, G.; Lichtfouse, E. Advantages and disadvantages of techniques used for wastewater treatment. Environ. Chem. Lett.; 2019; 17, pp. 145-155. [DOI: https://dx.doi.org/10.1007/s10311-018-0785-9]

15. Zeynali, V.; Masoudi, S.M.A.; Sargolzaei, J.; Dehkordi, F.S. The effect of key factors in aerobic bioreactor and optimization by different strategies. Environ. Technol. Innov.; 2020; 18, 100724. [DOI: https://dx.doi.org/10.1016/j.eti.2020.100724]

16. Akın, B.S.; Ugurlu, A. Monitoring and control of biological nutrient removal in a sequencing batch reactor. Process Biochem.; 2005; 40, pp. 2873-2878. [DOI: https://dx.doi.org/10.1016/j.procbio.2005.01.001]

17. Water Environment FederationEnvironmental and Water Resources Institute (U.S.). Biological Nutrient Removal (BNR) Operation in Wastewater Treatment Plants; McGraw-Hill: New York, NY, USA, 2006.

18. Singh, M.; Srivastava, R.K. Sequencing batch reactor technology for biological wastewater treatment: A review. Asia-Pac. J. Chem. Eng.; 2011; 6, pp. 3-13. [DOI: https://dx.doi.org/10.1002/apj.490]

19. Mansouri, A.M.; Zinatizadeh, A.A.; Irandoust, M.; Akhbari, A. Statistical analysis and optimization of simultaneous biological nutrients removal process in an intermittently aerated SBR. Korean J. Chem. Eng.; 2014; 31, pp. 88-97. [DOI: https://dx.doi.org/10.1007/s11814-013-0183-8]

20. Eddy, M.; Abu-Orf, M.; Bowden, G.; Burton, F.L.; Pfrang, W.; Stensel, H.D. AECOM. Wastewater Engineering: Treatment and Resource Recovery; McGraw Hill Education: New York, NY, USA, 2014.

21. Han, F.; Ye, W.; Wei, D.; Xu, W.; Du, B.; Wei, Q. Simultaneous nitrification-denitrification and membrane fouling alleviation in a submerged biofilm membrane bioreactor with coupling of sponge and biodegradable PBS carrier. Bioresour. Technol.; 2018; 270, pp. 156-165. [DOI: https://dx.doi.org/10.1016/j.biortech.2018.09.026]

22. Khursheed, A.; Gaur, R.Z.; Sharma, M.K.; Tyagi, V.K.; Khan, A.A.; Kazmi, A.A. Dependence of enhanced biological nitrogen removal on carbon to nitrogen and rbCOD to sbCOD ratios during sewage treatment in sequencing batch reactor. J. Clean. Prod.; 2018; 171, pp. 1244-1254. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.10.055]

23. Azimi, S.C.; Shirini, F.; Pendashteh, A. Evaluation of COD and turbidity removal from woodchips wastewater using biologically sequenced batch reactor. Process Saf. Environ. Prot.; 2019; 128, pp. 211-227. [DOI: https://dx.doi.org/10.1016/j.psep.2019.05.043]

24. Asadi, A.; Zinatizadeh, A.A.; Van Loosdrecht, M. High rate simultaneous nutrients removal in a single air lift bioreactor with continuous feed and intermittent discharge regime: Process optimization and effect of feed characteristics. Chem. Eng. J.; 2016; 301, pp. 200-209. [DOI: https://dx.doi.org/10.1016/j.cej.2016.04.144]

25. Abdulgader, M.; Yu, J.; Zinatizadeh, A.A.; Williams, P.; Rahimi, Z. Process analysis and optimization of single stage flexible fibre biofilm reactor treating milk processing industrial wastewater using response surface methodology (RSM). Chem. Eng. Res. Des.; 2019; 149, pp. 169-181. [DOI: https://dx.doi.org/10.1016/j.cherd.2019.07.011]

26. Shao, Y.; Shi, Y.; Mohammed, A.; Liu, Y. Wastewater ammonia removal using an integrated fixed-film activated sludge-sequencing batch biofilm reactor (IFAS-SBR): Comparison of suspended flocs and attached biofilm. Int. Biodeterior. Biodegrad.; 2017; 116, pp. 38-47. [DOI: https://dx.doi.org/10.1016/j.ibiod.2016.09.026]

27. de Sousa Rollemberg, S.L.; Barros, A.R.M.; de Lima, J.P.M.; Santos, A.F.; Firmino, P.I.M.; dos Santos, A.B. Influence of sequencing batch reactor configuration on aerobic granules growth: Engineering and microbiological aspects. J. Clean. Prod.; 2019; 238, 117906. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.117906]

28. Yadu, A.; Sahariah, B.P.; Anandkumar, J. Influence of COD/ammonia ratio on simultaneous removal of NH4+-N and COD in surface water using moving bed batch reactor. J. Water Process Eng.; 2018; 22, pp. 66-72. [DOI: https://dx.doi.org/10.1016/j.jwpe.2018.01.007]

29. Wang, H.; Song, Q.; Wang, J.; Zhang, H.; He, Q.; Zhang, W.; Li, H. Simultaneous nitrification, denitrification and phosphorus removal in an aerobic granular sludge sequencing batch reactor with high dissolved oxygen: Effects of carbon to nitrogen ratios. Sci. Total Environ.; 2018; 642, pp. 1145-1152. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.06.081] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30045496]

30. Hamza, R.A.; Zaghloul, M.S.; Iorhemen, O.T.; Sheng, Z.; Tay, J.H. Optimization of organics to nutrients (COD: N: P) ratio for aerobic granular sludge treating high-strength organic wastewater. Sci. Total Environ.; 2019; 650, pp. 3168-3179. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.10.026]

31. Li, D.; Zhang, S.; Li, S.; Zeng, H.; Zhang, J. Aerobic granular sludge operation and nutrients removal mechanism in a novel configuration reactor combined sequencing batch reactor and continuous-flow reactor. Bioresour. Technol.; 2019; 292, 122024. [DOI: https://dx.doi.org/10.1016/j.biortech.2019.122024]

32. Abramov, V.O.; Abramova, A.V.; Cravotto, G.; Nikonov, R.V.; Fedulov, I.S.; Ivanov, V.K. Flow-mode water treatment under simultaneous hydrodynamic cavitation and plasma. Ultrason. Sonochemistry; 2021; 70, 105323. [DOI: https://dx.doi.org/10.1016/j.ultsonch.2020.105323]

33. Zaman, M.; Kim, M.; Nakhla, G. Simultaneous nitrification-denitrifying phosphorus removal (SNDPR) at low DO for treating carbon-limited municipal wastewater. Sci. Total Environ.; 2021; 760, 143387. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.143387]

34. Meng, Q.; Yang, F.; Liu, L.; Meng, F. Effects of COD/N ratio and DO concentration on simultaneous nitrifcation and denitrifcation in an airlift internal circulation membrane bioreactor. J. Environ. Sci.; 2008; 20, pp. 933-939. [DOI: https://dx.doi.org/10.1016/S1001-0742(08)62189-0]

35. Gholami, F.; Zinatizadeh, A.A.; Zinadini, S.; McKay, T.; Sibali, L. An innovative jet loop-airlift bioreactor for simultaneous removal of carbon and nitrogen from soft drink industrial wastewater: Process performance and kinetic evaluation. Environ. Technol. Innov.; 2020; 19, 100772. [DOI: https://dx.doi.org/10.1016/j.eti.2020.100772]

36. Wang, X.; Chen, Z.; Shen, J.; Zhao, X.; Kang, J. Impact of carbon to nitrogen ratio on the performance of aerobic granular reactor and microbial population dynamics during aerobic sludge granulation. Bioresour. Technol.; 2019; 271, pp. 258-265. [DOI: https://dx.doi.org/10.1016/j.biortech.2018.09.119]

37. Mansouri, A.M.; Zinatizadeh, A.A. A comparative study of an up-flow aerobic/anoxic sludge fixed film bioreactor and sequencing batch reactor with intermittent aeration in simultaneous nutrients (N, P) removal from synthetic wastewater. Water Sci. Technol.; 2017; 76, pp. 1044-1058. [DOI: https://dx.doi.org/10.2166/wst.2017.261]

38. Lackner, S.; Terada, A.; Horn, H.; Henze, M.; Smets, B.F. Nitritation performance in membrane-aerated biofilm reactors differs from conventional biofilm systems. Water Res.; 2010; 44, pp. 6073-6084. [DOI: https://dx.doi.org/10.1016/j.watres.2010.07.074]

39. Guo, W.; Ngo, H.H.; Dharmawan, F.; Palmer, C.G. Roles of polyurethane foam in aerobic moving and fixed bed bioreactors. Bioresour. Technol.; 2010; 101, pp. 1435-1439. [DOI: https://dx.doi.org/10.1016/j.biortech.2009.05.062]

40. American Public Health Association (APHA). Standard Methods for the Examination of Water and Wastewater; 21st ed. APHA: Washington, DC, USA, 2012.

41. Akhbari, A.; Zinatizadeh, A.A.L.; Mohammadi, P.; Irandoust, M.; Mansouri, Y. Process modeling and analysis of biological nutrients removal in an integrated RBC-AS system using response surface methodology. Chem. Eng. J.; 2011; 168, pp. 269-279. [DOI: https://dx.doi.org/10.1016/j.cej.2011.01.003]

42. Jaafari, J.; Mesdaghinia, A.; Nabizadeh, R.; Hoseini, M.; Mahvi, A.H. Influence of upflow velocity on performance and biofilm characteristics of Anaerobic Fluidized Bed Reactor (AFBR) in treating high-strength wastewater. J. Environ. Health Sci. Eng.; 2014; 12, 139. [DOI: https://dx.doi.org/10.1186/s40201-014-0139-x]

43. Chen, C.; Guo, W.S.; Ngo, H.H.; Chang, S.W.; Nguyen, D.D.; Zhang, J.; Zhang, X.B. Effects of C/N ratio on the performance of a hybrid sponge-assisted aerobic moving bed-anaerobic granular membrane bioreactor for municipal wastewater treatment. Bioresour. Technol.; 2018; 247, pp. 340-346. [DOI: https://dx.doi.org/10.1016/j.biortech.2017.09.062]

44. Masoudi, S.M.A.; Hedayati Moghaddam, A.; Sargolzaei, J.; Darroudi, A.; Zeynali, V. Investigation and optimization of the SND–SBR system for organic matter and ammonium nitrogen removal using the central composite design. Environ. Prog. Sustain. Energy; 2018; 37, pp. 1638-1646. [DOI: https://dx.doi.org/10.1002/ep.12847]

45. Lin, J.; Zhang, P.; Li, G.; Yin, J.; Li, J.; Zhao, X. Effect of COD/N ratio on nitrogen removal in a membrane-aerated biofilm reactor. Int. Biodeterior. Biodegrad.; 2016; 113, pp. 74-79. [DOI: https://dx.doi.org/10.1016/j.ibiod.2016.01.009]

46. Ding, S.; Bao, P.; Wang, B.; Zhang, Q.; Peng, Y. Long-term stable simultaneous partial nitrification, anammox and denitrification (SNAD) process treating real domestic sewage using suspended activated sludge. Chem. Eng. J.; 2018; 339, pp. 180-188. [DOI: https://dx.doi.org/10.1016/j.cej.2018.01.128]

47. Asadi, A.; Zinatizadeh, A.A.L.; Isa, M.H. Performance of intermittently aerated up-flow sludge bed reactor and sequencing batch reactor treating industrial estate wastewater: A comparative study. Bioresour. Technol.; 2012; 123, pp. 495-506. [DOI: https://dx.doi.org/10.1016/j.biortech.2012.07.013]

48. Palm, J.; Jenkins, D. Relationship Between Organic Loading, Dissolved Oxygen Concentration and Sludge Settleability in the Completely Mixed Activated Sludge Process. J. WPCF; 1980; 10, 2484.

49. Sathian, S.; Rajasimman, M.; Radha, G.; Shanmugapriya, V.; Karthikeyan, C. Performance of SBR for the treatment of textile dye wastewater: Optimizationand kinetic studies. Alex. Eng. J.; 2014; 53, pp. 417-426. [DOI: https://dx.doi.org/10.1016/j.aej.2014.03.003]

50. Zinatizadeh, A.A.L.; Mansouri, Y.; Akhbari, A.; Pashaei, S. Biological treatmentof a synthetic dairy wastewater in a sequencing batch biofilm reactor: Statisticalmodeling using optimization using response surface methodology. Chem. Ind. Chem. Eng. Quart.; 2011; 17, pp. 485-495. [DOI: https://dx.doi.org/10.2298/CICEQ110524034Z]

51. Ni, B.J.; Xie, W.M.; Liu, S.G.; Yu, H.Q.; Wang, Y.Z.; Wang, G.; Dai, X.L. Granulation of activated sludge in a pilot-scale sequencing batch reactor for the treatment of low-strength municipal wastewater. Water Res.; 2009; 43, pp. 751-761. [DOI: https://dx.doi.org/10.1016/j.watres.2008.11.009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19059624]

52. Derlon, N.; Wagner, J.; da Costa, R.H.R.; Morgenroth, E. Formation of aerobic granules for the treatment of real and low-strength municipal wastewater using a sequencing batch reactor operated at constant volume. Water Res.; 2016; 105, pp. 341-350. [DOI: https://dx.doi.org/10.1016/j.watres.2016.09.007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27639343]

53. Amini, M.; Younesi, H.; Najafpour, G.; Zinatizadeh-Lorestani, A.A. Application of response surface methodology for simultaneous carbon and nitrogen (SND) removal from dairy wastewater in batch systems. Int. J. Environ. Stud.; 2012; 69, pp. 962-986. [DOI: https://dx.doi.org/10.1080/00207233.2012.739428]

54. Jaafari, J.; Seyedsalehi, M.; Safari, G.H.; Ebrahimi Arjestan, M.; Barzanouni, H.; Ghadimi, S.; Haratipour, P. Simultaneous biological organic matter and nutrient removal in an anaerobic/anoxic/oxic (A2O) moving bed biofilm reactor (MBBR) integrated system. Int. J. Environ. Sci. Technol.; 2017; 14, pp. 291-304. [DOI: https://dx.doi.org/10.1007/s13762-016-1206-x]

55. Zinatizadeh, A.A.L.; Ghaytooli, E. Simultaneous nitrogen and carbon removal from wastewater at different operating conditions in a moving bed biofilm reactor (MBBR): Process modeling and optimization. J. Taiwan Inst. Chem. Eng.; 2015; 53, pp. 98-111. [DOI: https://dx.doi.org/10.1016/j.jtice.2015.02.034]

56. Ray, S.; Mohanty, A.; Mohanty, S.S.; Mishra, S.; Chaudhury, G.R. Optimization of biological elimination of ammonia and chemical oxygen demand from wastewater using response surface methodology. CLEAN–Soil Air Water; 2014; 42, pp. 1744-1750. [DOI: https://dx.doi.org/10.1002/clen.201200542]