Monitoring and control of biological nutrient removal in a Sequencing Batch Reactor

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

Anaerobic starvation realizes partial nitrification and starts anammox bacteria self-enrichment in mainstream municipal sewage treatment in a low filling ratio sequencing batch reactor

The initiating and stable preservation of partial nitrification (PN) and achievement of anammox bacteria self-enrichment in domestic sewage is a purposeful subject. In this article, an originality tactics of anaerobic starvation for 100 days was adopted for rapidly achieving PN in actual wastewater, the nitrite accumulation rate (NAR) improved from 4.95% to 81.73% in 18 days. After anaerobic starvation was stopped, the stable PN effect furnished enough stroma for the growth of anammox bacteria. The abundance of Candidatus Brocadia grew from 0% to 0.42% in floc sludge and 0.43% in blank biofilm, which promoted nitrogen removal effect. Anaerobic starvation continuing 74 days generated further decrease in the abundance of Nitrobacter and Nitrospira of nitrite-oxidizing bacteria (NOB), indicating that anaerobic starvation can restore the destroyed partial nitrification. In conclusion, this article furnished a low-cost method for achieving anammox bacteria self-enrichment in mainstream municipal wastewater in 10% filling ratio without chemicals addition.

Real-time monitoring control of sequencing batch anammox process

Rapid recognition and timely management of the emergent situation in wastewater treatment are crucial to maintaining the stable operation of anammox process. In this study, the feasibility of pH, conductivity (Cond) and oxidation reduction potential (ORP) profiles for monitoring and controlling anammox process for synthetic wastewater treatment was evaluated, and the practicability of the method was further verified by using real wastewater. The results showed that the characteristic values of these parameter profiles exhibited high accuracy and reproducibility in indicating the endpoint of the anammox reaction. Moreover, the positive correlations between TN removal and ΔpH, ΔCond and ΔORP were found. Nevertheless, only the slope of the Cond curve was found to be significantly linearly correlated with the specific anammox activity, which was further validated by the Haldane inhibition kinetic model, suggesting that the Cond curve can be used as an immediate feedback signal on whether anammox activity was inhibited. Overall, this study presents a fast, convenient and accurate strategy based on online real-time monitoring of instrument parameters, which was conducive to tracking the nitrogen removal process dynamics and performing the necessary operations in a timely manner, and to improving the stability of anammox process in wastewater treatment.

Anoxic-Aerobic-Anoxic sequencing batch reactor for enhanced nitrogen removal

An anoxic-aerobic-anoxic process was established to achieve simultaneous removal of organic carbon and nitrogen from wastewater in a sequencing batch reactor. The optimum conditions were attained at a DO of 1.5 mg/L with 1 h pre-anoxic and post-anoxic periods. TOC, NH₄⁺-N, and TN removal efficiencies were 98.76 ± 0.95 %, 98.52 ± 0.48 %, and 88.23 ± 0.62 %, respectively, at optimum conditions. Breakpoints in the pH, DO, and ORP curves provided a clear understanding of biochemical reactions happening in the reactor. Inhibition studies showed that 27.69 % of NH₄⁺-N was removed through nitrogen removal pathways such as heterotrophic nitrification or direct conversion, and 20.55 % of TN was removed through aerobic denitrification. Microbial community analysis confirmed the presence of heterotrophic nitrifiers and aerobic denitrifiers. This study highlighted that the varied redox conditions offered by limited aeration would be beneficial for nitrogen removal, thereby reducing the energy usage and operating costs.

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

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.

The effect of anaerobic pig slurry redox potentials on the degradation of veterinary medicines

Veterinary medicines are frequently used within intensive livestock husbandry and there has been a growing interest regarding their fate in the environment. However, research has seldom assessed the influence of pig slurry properties on the fate of veterinary medicines even though such an understanding is essential for a more robust environmental risk assessment. Changes within manure degradation rates have the potential to alter the concentration of antibiotics applied to land, and the outcome of the risk assessment. The aim of this work was to investigate whether commonly reported redox potentials affect the degradation rates of acetyl-salicylic acid, ceftiofur, florfenicol, oxytetracycline, sulfamethoxazole, and tylosin. The employed redox potentials were -100 mV (reduced), -250 mV (anaerobic) and -400 mV (very anaerobic). A compound specific relationship was observed where the degradation of ceftiofur, florfenicol, oxytetracycline and sulfamethoxazole was inhibited under reduced conditions over that of very anaerobic; the respective DT50 values were 0.7-1.84 h, 1.35-3.61 h, 22.2-49.8 h, 131-211 h and 35.4-94 h. In contrast, tylosin was found to degrade faster at reduced conditions over very anaerobic (DT50 6.88-19.4 h). The presented research demonstrates the importance of redox potential on degradation rates and suggests we need stringent and harmonized redox control to improve the environmental risk assessment of veterinary medicines. Environmental relevance and significance: Given the significant effect of anaerobic redox potentials on veterinary medicine fate tighter regulation is required in manure degradation trials.

Influence of HRT and carbon source on the enhancement of nutrient removal in an Anaerobic-Oxic-Anoxic (AOA) system

The eutrophication and increase in toxicity promoted by the continuous or abundant supply of nutrients in water bodies threaten the safety of drinking water and human health. In this regard, this study proposes the investigation of wastewater treatment focusing on the simultaneous removal of nitrogen and phosphorus in the anaerobic-oxic-anoxic (AOA) system. The AOA system was operated in three different stages to verify the influence of the external carbon source addition in the anoxic reactor and the reduction of hydraulic retention time (HRT) in the anaerobic and oxic reactors for nutrient removal optimization. Results showed that the best performance of the AOA system on nutrient removal was obtained in Stage 3, with the reduction of the HRT in the anaerobic and oxic reactors (HRT = 4 h) while maintaining HRT of 6.4 h in the anoxic reactor with no addition of the external carbon source. Under these conditions, the average removal efficiencies reached 98% for Chemical Oxygen Demand (COD), 88% for Total Ammonia Nitrogen (TAN), 81% for Total Kjeldahl Nitrogen (TKN), and 70% for Total Phosphorus (TP). The results also demonstrate that the highest phosphorus removal efficiency was achieved in the anoxic reactor, thus indicating the occurrence of denitrifying phosphorous removal by Denitrifying Phosphate Accumulating Organisms (DNPAOs). This configuration was efficient regarding the simultaneous removal of nitrogen and phosphorus; besides, the advantages of this system include robust configuration and excellent performance on the nutrient removal.

Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.

The effect of fermented superphosphate pretreatment and step-feed mode on biological denitrification of piggery wastewater

Chemical pretreatment can reduce NH₃-N levels in piggery wastewater to a certain extent, but the lack of a carbon source for subsequent biological treatment leads to a low denitrification efficiency and poor total nitrogen (TN) removal percentage. Taking superphosphate (SP) pretreatment (SP/Pretreatment) as the control, this research studies the influence of fermented superphosphate (FSP; SP plus a carbon source) pretreatment (FSP/Pretreatment) on biological denitrification in a subsequent biological treatment step. Furthermore, the removal of pollutants under different influent modes is also evaluated. The experimental results show that with the addition of the SP pretreatment, the removal percentage of NH₃-N was 52%, and the chemical oxygen demand (COD)/TN ratio increased from 0.36 to 0.71. However, with the addition of the FSP pretreatment, the removal percentage of NH₃-N reached 64%, and the COD/TN ratio increased to 2.28. The combination of the FSP pretreatment and a subsequent sequencing batch reactor (SBR) step in the step-feed influent mode resulted in the best denitrification, with a TN removal percentage of 57%. This result was 51% higher than that of the SP/Pretreatment-SBR system, indicating that the addition of the FSP pretreatment improves the biological denitrification of piggery wastewater. After the full treatment process for piggery wastewater, the effluent COD was 57.33 mg·L^-1, the NH₃-N was 66.32 mg·L^-1, and the total phosphorus (TP) was 1.17 mg·L^-1, all of which meet the emission standards of the "Fouling Standards for Pollutants in the Livestock Breeding Industry" (GB 18596-2001) (400 mg·L^-1 COD, 80 mg·L^-1 NH₃-N, 8 mg·L^-1 TP).

Advanced strategies to improve nitrification process in sequencing batch reactors - A review

The optimization of biological nitrogen removal (BNR) in sequencing batch reactors has become the aim of researchers worldwide in order to increase efficiency and reduce energy and operating costs. This research has focused on the nitrification phase as the limiting reaction rate of BNR. This paper analyzes different strategies and discusses different tools such as: factors for achieving partial nitrification, real-time control and monitoring for detecting characteristic patterns of nitrification/denitrification as end-points, use of modeling based on activated sludge models, and the use of data-driven modeling for estimating variables that cannot be easily measured experimentally or online. The discussion of this paper highlight the properties and scope of each of these strategies, as well as their advantages and disadvantages, which can be integrated into future works using these strategies according to legal and economic restrictions for a more stable and efficient BNR process in the long-term.

Bioclogging in porous media: influence in reduction of hydraulic conductivity and organic contaminants during synthetic leachate permeation

In this study the concept of biofilm accumulation in the sand column was promoted to assess the changes in hydraulic conductivity and concentration of organic contaminants of the synthetic leachate. Four different combinations of column study were carried out using synthetic leachate as a substrate solution. Mixed and stratified mode of experiments with two different sizes (0.3 mm and 0.6 mm) of sand grains were used for column filling. Two columns were acting as a blank, the remaining two columns amended with mixed microbial cultures which were isolated from leachate. The column was operated with continuous synthetic leachate supply for 45 days. The results indicated that the highest hydraulic conductivity reduction occurred in the mixed sand microbial column with 98.8% when compared to stratified sand microbial column. The analysis of organic contaminants of the effluent leachate was also clearly shown that the mixed sand amended with microbes poses a suitable remedial measure when compared to natural and synthetic liners for controlling the leachate migration in the subsurface environment.

Detoxifying of high strength textile effluent through chemical and bio-oxidation processes

Small-scale textile industries (SSTIs) in India struggled for the economic and environmental race. A full-scale common treatment plant (CETP) working on the principle of destabilising negative charge colloidal particles and bio-oxidation of dissolved organic failed to comply with Inland Surface Waters (ISW) standards. Thus, presence of intense colour and organics with elevated temperature inhibited the process stability. Bench scale treatability studies were conducted on chemical and biological processes for its full-scale apps to detoxify a high strength textile process effluent. Colour, SS and COD removals from the optimised chemical process were 88%, 70% and 40%, respectively. Heterotrophic bacteria oxidised COD and BOD more than 84% and 90% at a loading rate 0.0108kgm(-3)d(-1) at 3h HRT. The combined chemical and bio-oxidation processes showed a great promise for detoxifying the toxic process effluent, and implemented in full-scale CETP. The post-assessment of the CETP resulted in detoxify the toxic effluent.

Feasibility of bioengineered two-stages sequential batch reactor and filtration-adsorption process for complex agrochemical effluent

In the present study, the feasibility of a bioengineered two-stages sequential batch reactor (BTSSBR) followed by filtration-adsorption process was investigated to treat the agrochemical effluent by overcoming factor affecting process stability such as microbial imbalance and substrate sensitivity. An air stripper stripped 90% of toxic ammonia, and combined with other streams for bio-oxidation and filtration-adsorption. The BTSSBR system achieved bio-oxidation at 6 days hydraulic retention time by fending off microbial imbalance and substrate sensitivity. The maximum reduction in COD and BOD by heterotrophic bacteria in the first reactor was 87% and 90%, respectively. Removal of toxic ammoniacal-nitrogen by autotrophic bacteria in a post-second stage bio-oxidation was 97%. The optimum filtration and adsorption of pollutants were achieved at a filtration rate of 10 and 9 m(3)m(-2)h(-1), respectively. The treatment scheme comprising air stripper, BTSSBR and filtration-adsorption process showed a great promise for treating the agrochemical effluent.

Model based optimization of the intermittent aeration profile for SBRs under partial nitrification

In this paper, a fast and accurate optimization framework is proposed to compute optimal aeration policies in SBR processes under partial nitrification. The optimization framework aims to determine an optimal intermittent aeration profile which minimizes both the operation time of the SBR cycle and the energy required for aeration. Special consideration is given to the fact that the results not only need to be accurate but also to converge within a short time. Moreover, methods to avoid nitrate formation are analyzed and implemented. It is demonstrated that the implementation of a nonlinear model "5-state" and the reduction of the optimization problem to three control variables are the keystones to an efficient solution strategy which achieves fast, robust, and accurate computation of the optimal intermittent aeration profile for any given conditions of the process. The optimization approach is so efficient that it can also be implemented with more complex models such as the ASM3 extended for a two-step nitrification-denitrification process.

Impacts of operating parameters on oxidation-reduction potential and pretreatment efficacy in the pretreatment of printing and dyeing wastewater by Fenton process

An experiment was conducted in a batch reactor for a real printing and dyeing wastewater pretreatment using Fenton process in this study. The results showed that original pH, hydrogen peroxide concentration and ferrous sulfate concentration affected ORP value and pretreatment efficacy greatly. Under experimental conditions, the optimal original pH was 6.61, and the optimal hydrogen peroxide and ferrous sulfate concentrations were 1.50 and 0.75 gL(-1), respectively. The relationship among ORP, original pH, hydrogen peroxide concentration, ferrous sulfate concentration, and color (COD or BOD(5)/COD) was established, which would be instructive in on-line monitoring and control of Fenton process using ORP. In addition, the effects of wastewater temperature and oxidation time on pretreatment efficacy were also investigated. With an increase of temperature, color and COD removal efficiencies and BOD(5)/COD ratio increased, and they were in proportion to the exponent of temperature reciprocal. Similarly, color and COD removal efficiencies increased with increasing oxidation time, and both color and COD removal obeyed the first-order kinetics. The BOD(5)/COD ratio could be expressed by a second-degree polynomial with respect to oxidation time, and the best biodegradability of wastewater was present at the oxidation time of 6.10h.

Evaluating operation strategies and process stability of a single stage nitritation-anammox SBR by use of the oxidation-reduction potential (ORP)

A single stage nitritation-anammox SBR was operated for 300 days to investigate the impact of cycle operation strategies on process performance and the oxidation-reduction potential (ORP) as process monitoring parameter. Different combinations of feeding (interval, continuous, one-time) and aeration (interval, continuous) strategies were tested revealing that interval feeding and interval aeration was the most suitable case in terms of process performance (ammonium removal, nitrate production and pH stability) and use of the ORP value as indicator parameter. Further investigations into the use of the ORP value showed clear correlations of the ORP slope with the air flow rate and the maximum ORP peak with the ammonium loading under varying operation conditions. Depletion of the main substrates (ammonium and oxygen) was also detectable fastest following the ORP value proofing its worth for process control.

Effect of feeding strategy on the performance of sequencing batch reactor with dual anoxic feedings for swine wastewater treatment

Two feeding ratios, 3:1 (RUN I) and 2:1 (RUN II), were experimented to investigate their effect on removal of nitrogen, phosphorus, organic matter, and on the trends of oxidization-reduction potential (ORP) and pH in sequencing batch reactors (SBR) with dual anoxic feedings of swine wastewater. Results showed for both runs, the feeding ratio had a more significant effect on the removal of phosphorus and nitrogen than on COD in the operating cycle. As compared to RUN I, the removal of nitrogen and phosphorus was enhanced in RUN II. In addition, the optimal feeding ratio greater than 1:1 for SBR with dual anoxic feedings can be determined with reference to the decreasing range of ORP following the primary feeding, and the denitrification-associated ORP bending point in the second non-aeration phase.

Biological nitrogen removal with a real-time control strategy using moving slope changes of pH(mV)- and ORP-time profiles

A new real-time control strategy using moving slope changes of oxidation-reduction potential (ORP)- and pH(mV)-time profiles was designed. Its effectiveness was evaluated by operating a farm-scale sequencing batch reactor (SBR) process using the strategy. The working volume of the SBR was 18 m(3), and the volumetric loading rate of influent was 1 m(3) cycle(-1). The SBR process comprised six phases: feeding → anoxic → anaerobic → aerobic → settle → discharge. The anoxic and aerobic phases were controlled by the developed real-time control strategy. The nitrogen break point (NBP) in the pH(mV)-time profile and the nitrate knee point (NKP) in the ORP-time profile were designated as real-time control points for the aerobic and anoxic phases, respectively. Through successful real-time control, the duration of the aerobic and anoxic phases could be optimized and this resulted in very high N removal and a flexible hydraulic retention time. Despite the large variation in the loading rate (0.5-1.8 kg NH(4)-N m(-3) cycle(-1)) due to influent strength fluctuation, complete removal of NH(4)-N (100%) was always achieved. The removal efficiencies of soluble nitrogen (NH(4)-N plus NO(x)-N), soluble total organic carbon, and soluble chemical oxygen demand were 98%, 90%, and 82%, respectively. Monitoring the ORP and pH(mV) revealed that pH(mV) is a more reliable control parameter than ORP for the real-time control of the oxic phase. In some cases, a false NBP momentarily appeared in the ORP-time profile but was not observed in the pH(mV)-time profile. In contrast, ORP was more the reliable control parameter for NKP detection in the anoxic phase, since the appearance of NKP in the pH(mV)-time profile was sometimes vague.

A fast predicting neural fuzzy model for on-line estimation of nutrient dynamics in an anoxic/oxic process

In this paper a software sensor based on a fuzzy neural network approach was proposed for real-time estimation of nutrient concentrations. In order to improve the network performance, fuzzy subtractive clustering was used to identify model architecture, extract and optimize fuzzy rule of the model. A split network structure was applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as autoregressive with exogenous inputs and multi-way principal component analysis (MPCA). The proposed methodology was applied to a bench-scale anoxic/oxic process for biological nitrogen removal. The simulative results indicate that the learning ability and generalization of the model performed well and also worked well for normal batch operations corresponding to three data points inside the confidence limit determined by MPCA. Real-time estimation of NO(3)(-), NH(4)(+) and PO(4)(3-) concentration based on fuzzy neural network analysis were successfully carried out with the simple on-line information regarding the anoxic/oxic system.

Recognition of Relevant ORP, pH, and DO Bending Points in Ammonia Removal from Drinking Water through Online BAF System

This study was undertaken to identify the relevant bending points in ORP, pH, and DO profiles in ammonia removal through online monitoring. A novelty BAF system as newl application for drinking water treatment that equipped with ORP, pH, DONH4 +andNO3 −sensors was used. Two types of polluted drinking water strength (low and high strength) with variousNH4 +concentrations and aeration flow were treated at a fixed-time reaction of 24 h. Experiments were conducted at four track studies (TS) of TS1 (NH4 +=50 mg/L, aeration=0.3 L/min), TS2 (NH4 += 100 mg/L, aeration=2.0 L/min), TS3 (NH4 +=100 mg/L, no aeration) and TS4 (NH4 +=10 mg/L, aeration=0.1 L/min). The results showed that the removal ofNH4 +was more than 95% for TS1, TS2, and TS4. From the online monitoring performances, DO elbow and ammonia valley appeared in ORP and pH profiles, respectively. Similarly, new positive plateaus were observed in DO, indicating that the nitrifiers stopped to consume the DO afterNH4 +was completely removed. Hence, based on the bending points, the aeration system is possible to be automatically stopped just after DO elbow and ammonia valley appears in order to save the energy consumption and to shorten the time demands for the drinking water treatment process.

Real-time control of oxic phase using pH (mV)-time profile in swine wastewater treatment

The feasibility of real-time control of the oxic phase using the pH (mV)-time profile in a sequencing batch reactor for swine wastewater treatment was evaluated, and the characteristics of the novel real-time control strategies were analyzed in two different concentrated wastewaters. The nitrogen break point (NBP) on the moving slope change (MSC) of the pH (mV) was designated as a real-time control point, and a pilot-scale sequencing batch reactor (18 m(3)) was designed to fulfill the objectives of the study. Successful real-time control using the developed control strategy was achieved despite the large variations in the influent strength and the loading rate per cycle. Indeed, complete and consistent removal of NH4-N (100% removal) was achieved. There was a strong positive correlation (r(2)=0.9789) between the loading rate and soluble total organic carbon (TOCs) removal, and a loading rate of 100 g/m(3)/cycle was found to be optimum for TOCs removal. Experimental data showed that the real-time control strategy using the MSC of the pH (mV)-time profile could be utilized successfully for the removal of nitrogen from swine wastewater. Furthermore, the pH (mV) was a more reliable real-time control parameter than the oxidation-reduction potential (ORP) for the control of the oxic phase. However, the nitrate knee point (NKP) appeared more consistently upon the completion of denitrification on the ORP-time profile than on the pH (mV)-time profile.

Monitoring of anoxic/oxic process for nitrogen and chemical oxygen demand removal using fuzzy neural networks

In this paper, a software sensor based on a fuzzy neural network (FNN) approach was proposed for the real-time estimation of nutrient concentrations and overcoming the problem of delayed measurements. To improve the FNN performance, fuzzy subtractive clustering was used to identify the model's architecture and optimize the fuzzy rule; meanwhile, a split network structure, applied separately for anaerobic and aerobic conditions, was used with dynamic modeling methods, such as an auto-regressive model with exogenous inputs. The proposed methodology was applied to a bench-scale anoxic/oxic process for biological nitrogen removal. It was possible to partially overcome the extrapolation problem of FNNs with the aid of multi-way principal component analysis, because it has the ability to detect abnormal situations, which could generate extrapolation. Real-time estimation of chemical oxygen demand, nitrate, and ammonium concentrations based on the model was successfully carried out with the simple online information of the anoxic/oxic system.

Optimization of main factors associated with nitrogen removal in hybrid sludge sequencing batch reactor with step-feeding of swine wastewater

To attain a high nitrogen removal efficiency and good sludge settleability in a step-fed sequencing batch reactor (SFSBR) treating swine wastewater, L(9)(3(4)) orthogonal experiments were carried out to optimize main factors associated with nitrogen removal, namely, the influent C/N ratio, feeding volume ratio, nitrogen loading rate and aeration intensity. Results showed that nitrogen loading rate contributed most for the build-up of NO(2)(-)-N, NO(3)(-)-N and NH(4)(+)-N in the effluent, while aeration intensity was the most important factor for net nitrogen removal efficiency based on the initial and final nitrogen concentrations in the SFSBR cycle. Additionally, the periodic starvation created by stepwise feeding was the major inducing force for granulation in the SFBSR process and the influent C/N ratio had a profound influence on sludge settleability and granular sludge stability in terms of sludge volume index (SVI) and the fraction of granular sludge with diameter over 0.5 mm (f(0.5 mm)), respectively. Considering the most and secondary important control factor for individual response index, the optimal operating condition for nitrogen removal of SFSBR treating swine wastewater was determined as A(3)B(3)C(1)D(2), i.e., influent C/N ratio 7.0 mg COD/mg NH(4)(+)-N, feeding volume ratio 3:1, nitrogen loading rate 0.026 g NH(4)(+)-N/gVSS . d and aeration intensity 4.2 L/m(3) . s, respectively. Under the optimal operating conditions, inorganic nitrogen concentration in the effluent, net nitrogen removal efficiency, SVI and f(0.5 mm) reached 21 mg/L, 72 %, 40.7 mL/g and 4.3 %, respectively.

Intercross real-time control strategy in alternating activated sludge process for short-cut biological nitrogen removal treating domestic wastewater

To develop technically feasible and economically favorable dynamic process control (DPC) strategies for an alternating activated sludge (AAS) system, a bench-scale continuous-flow alternating aerobic and anoxic reactor, performing short-cut nitrogen removal from real domestic wastewater was operated under different control strategies for more than five months. A fixed-time control (FTC) study showed that bending-points on pH and oxidation-reduction potential (ORP) profiles accurately coincided with the major biological activities. "Ammonia valley" on the pH profile represented the end of nitrification, whereas, the "nitrite knee" on the ORP profile and "nitrite apex" on the pH profile both indicated the end of denitrification. Therefore, a new reliable and effective real-time control strategy was developed using pH and ORP as control parameters, to improve the performance of the AAS process. The online control strategy could achieve up to 87% of the total nitrogen (TN) removal efficiency on an average, and saving approximately 20% aeration energy, as compared to the conventional steady-state control systems. Moreover, stable short-cut nitrification and denitrification were successfully achieved with an average nitrite accumulation ratio of above 95%.

Control of SBR switching by fuzzy pattern recognition

The sequencing batch reactor (SBR) is a widely used process for biological removal of nutrients (nitrogen and phosphorus) from wastewater. It is based on the metabolism of specialised bacteria, which under alternate anaerobic/aerobic conditions uptake phosphorus and perform denitrification. Intermittent operation is normally operated on a fixed switching schedule with ample margin for possible inaccuracies, with the result that the process operation is highly inefficient. This paper proposes a switching strategy based on the indirect observation of process state through simple physico-chemical measurements and the use of an inferential engine to determine the most appropriate switching schedule. In this way the duration of each phase is limited to the time strictly necessary for the actual loading conditions. Experimental results show that the treatment cycle can be significantly shortened, with the results that more wastewater can be treated. The switching strategy is based on innovative data-processing techniques applied to simple process signals including pH, oxido-reduction potential (ORP) and dissolved oxygen (DO). They include wavelet filtering for signal denoising and fuzzy clustering for features extraction and decision-making. The formation of a knowledge-base and its adaptation during the operation are also discussed.