Control of SBR switching by fuzzy pattern recognition

Full dynamic control of dairy wastewater treatment by aerobic granular sludge using electric conductivity and oxygen uptake rate

The objective of the current study was to determine the applicability of a sensor-based dynamic control strategy for the treatment of real variable dairy wastewater by aerobic granular sludge (AGS) performing enhanced biological phosphorus removal (EBPR). Two parallel sequencing batch reactors (SBRs) were set up that used only an anaerobic feast/aerobic famine microbial selection strategy to successfully obtain sludge granulation. SBR-STA used a fixed cycle length, while the duration of the reaction steps in SBR-DYN was variable. The control strategy was based solely on (derived) signals from low-cost and common sensors. The profile of the electric conductivity during the anaerobic reaction step was related to the microbial release of phosphate (PO4-P) and the associated uptake of dissolved organic carbon (DOC) by polyphosphate-accumulating organisms (PAOs). Control of the aerobic reaction step was based on the oxygen uptake rate (OUR). This resulted in a dynamic reactor operation with significant efficiency gains, such as 32% shorter cycle times and 42% higher sludge loading rates without impairing the effluent quality. These results extend the existing potential of indirect control strategies to full biological nutrient removal processes, which may be of great assistance to the operators and designers of industrial installations.

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.

Dynamic control of nutrient-removal from industrial wastewater in a sequencing batch reactor, using common and low-cost online sensors

On-line control of the biological treatment process is an innovative tool to cope with variable concentrations of chemical oxygen demand and nutrients in industrial wastewater. In the present study we implemented a simple dynamic control strategy for nutrient-removal in a sequencing batch reactor (SBR) treating variable tank truck cleaning wastewater. The control system was based on derived signals from two low-cost and robust sensors that are very common in activated sludge plants, i.e. oxidation reduction potential (ORP) and dissolved oxygen. The amount of wastewater fed during anoxic filling phases, and the number of filling phases in the SBR cycle, were determined by the appearance of the 'nitrate knee' in the profile of the ORP. The phase length of the subsequent aerobic phases was controlled by the oxygen uptake rate measured online in the reactor. As a result, the sludge loading rate (F/M ratio), the volume exchange rate and the SBR cycle length adapted dynamically to the activity of the activated sludge and the actual characteristics of the wastewater, without affecting the final effluent quality.

Sequencing batch-reactor control using Gaussian-process models

This paper presents a Gaussian-process (GP) model for the design of sequencing batch-reactor (SBR) control for wastewater treatment. The GP model is a probabilistic, nonparametric model with uncertainty predictions. In the case of SBR control, it is used for the on-line optimisation of the batch-phases duration. The control algorithm follows the course of the indirect process variables (pH, redox potential and dissolved oxygen concentration) and recognises the characteristic patterns in their time profile. The control algorithm uses GP-based regression to smooth the signals and GP-based classification for the pattern recognition. When tested on the signals from an SBR laboratory pilot plant, the control algorithm provided a satisfactory agreement between the proposed completion times and the actual termination times of the biodegradation processes. In a set of tested batches the final ammonia and nitrate concentrations were below 1 and 0.5 mg L(-1), respectively, while the aeration time was shortened considerably.

Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns. The expert functional PCA approach enabled linking the two major trends in the data to reversible fouling and irreversible fouling. The B-splines approach provided a more objective way for functional representation of the data set but its complexity did not appear justified by better results. The fuzzy clustering algorithm, applied after PCA, was successful in recognizing the data trends and placing the cluster centres in meaningful positions, as such supporting data analysis. However, the algorithm did not allow a correct classification of all data. Factor analysis was used instead, exploiting the linearity of the observed two dimensional trends, to completely split the reversible and irreversible fouling effects and classify the data in a more pragmatic approach. Overall, the tested techniques appeared useful and can serve as the basis for automatic membrane fouling monitoring and control.

Modeling and monitoring of dynamic processes

In this paper, a new online monitoring approach is proposed for handling the dynamic problem in industrial batch processes. Compared to conventional methods, its contributions are as follows: (1) multimodes are separated correctly since the cross-mode correlations are considered and the common information is extracted; (2) the expensive computing load is avoided since only the specific information is calculated when a mode is monitored online; and (3) after that, two different subspaces are separated, and the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the subspace. The corresponding confidence regions are constructed according to their respective models.

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.

Treatment of municipal wastewater using a contact oxidation filtration separation integrated bioreactor

A new contact oxidation filtration separation integrated bioreactor (CFBR) was used to treat municipal wastewater. The CFBR was made up of a biofilm reactor (the upper part of the CFBR) and a gravitational filtration bed (the lower part of the CFBR). Polyacrylonitrile balls (50mm diameter, 237 m(2)/m(3) specific surface, 90% porosity, and 50.2% packing rate) were filled into the biofilm reactor as biofilm attaching materials and anthracite coal (particle size 1-2mm, packing density 0.947 g/cm(3), non-uniform coefficient (K(80)=d(80)/d(10))<2.0) was placed into the gravitational filtration bed as filter media. At an organic volumetric loading rate of 2.4 kg COD/(m(3)d) and an initial filtration velocity of 5m/h in the CFBR, the average removal efficiencies of COD, ammonia nitrogen, total nitrogen and turbidity were 90.6%, 81.4%, 64.6% and 96.7% respectively, but the treatment process seemed not to be effective in phosphorus removal. The average removal efficiency of total phosphorus was 60.1%. Additionally, the power consumption of the CFBR was less than 0.15 kWh/m(3) of wastewater treated, and less than 1.5 kWh/kg BOD(5) removal.

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.

Artificial intelligence control of a sequencing batch reactor for nitrogen removal via nitrite from landfill leachate

Leachate generated in old landfills is a high-strength wastewater, which is particularly difficult to treat owing to its low biochemical oxygen demand/total Kjeldahl nitrogen ratio. This paper seeks to demonstrate that reliable leachate treatment by means of sequencing batch reactors (SBRs) is indeed possible by means of the application of a smart control system. This study assesses the results of a computer-controlled bench-scale SBR treating raw sanitary landfill leachate to achieve nitrogen removal through the nitrite shortcut. Significant improvements have been obtained by introducing a fuzzy inferential system based on simple process measurements (i.e. dissolved oxygen, oxidation-reduction potential and pH). The paper analyzes the results of a test period of over 280 consecutive days of operation, during which the fuzzy control system correctly recognized over 97% of the SBR phase transitions and provided smart adjustments of the process operating conditions in terms of phase length and external COD addition. In spite of time-varying process conditions, the application of fuzzy logic provided stable nitrogen removal via nitrite through continuous adjustments of the main process parameters and resulted in a decreased hydraulic retention time, an increased loading rate, a saving in the external COD addition and considerable aeration energy conservation.

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.

Nitrogen removal via nitrite in a sequencing batch reactor treating sanitary landfill leachate

The present paper reports the results of the application of a control system, based on artificial intelligence concepts, for the automation of a bench-scale SBR treating leachate generated in old landfills. Attention was given to the nitritation and denitritation processes in order to enhance the nitrogen removal efficiency. Nitrification and nitrogen removal were usually higher than 98% and 95%, respectively, whereas COD removal was approximately 20-30% due to the low biodegradability of organic matter in the leachate from old landfills; therefore, external COD was added to accomplish the denitrification process. Adjusting the length of the oxic phase, almost complete inhibition of the nitrite oxidizing organisms was observed. The results confirm the effectiveness of the nitrite route for nitrogen removal optimisation in leachate treatment. A significant saving of approximately 35% in external COD addition was achieved.