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Nested control loop configuration for a three stage biological wastewater treatment process
Abstract
In every urban infrastructure, Wastewater Treatment Plant (WWTP) requires special attention because of its adverse effects on the environment and also for resource recovery. Therefore, there arises a need to treat the wastewater in order to meet the effluent norms prior to discharge. Different control strategies and various scenarios of plant layout can be tested and evaluated through modelling and simulation studies on the benchmark layouts. In this paper, a feedforward nested loop control structure based on ammonia concentration is implemented on Benchmark Simulation Model (BSM1-P) developed based on Activated Sludge Model No. 3 bioP (ASM3bioP) for controlling the dissolved oxygen in aerobic zones and nitrate level in anoxic zones and nutrient removal by adding two anaerobic zones. By using this control strategy, pumping energy, percentage violations of ammonia and nitrogen concentrations in the effluent, and effluent quality are reduced effectively.
... Moreover, its inputs to the environment have been on the rise for the past few decades [1], making the availability of a sustainable source of healthy water increasingly important to many countries because of the increasing population, expansion of industries, and climate change effects. Various methods are available for nitrate removal from water, such as reverse osmosis, ion exchange, electrodialysis, and membrane processes [2][3][4][5]. Additionally, there is a rising interest in biological methods [6]. ...
The nitrate removal efficiency of a 9.5 L packed bed column bioreactor was evaluated using different feeding strategies and initial concentrations. The bioreactor was filled with zeolite mineral particles and initially treated with Thiobacillus denitrificans. Several hydraulic retention times were examined to assess the effectiveness of nitrate removal. The most favorable scenario resulted in an 87% reduction in nitrate concentration from an influent of 400 mg/L within a three-hour period. To determine the optimal length of the bioreactor, a computational fluid dynamics model was developed. By comparing simulations with experimental results, the ideal height of the bioreactor for complete denitrification was determined to be 90 cm, 45 cm, 30 cm, and 20 cm for influents with nitrate concentrations of 400 mg/L, 250 mg/L, 120 mg/L, and 80 mg/L, respectively.
... In the literature, many control applications like fuzzy logic controller (FLC), model predictive controller (MPC), proportional-integral (PI), and ammonia-based aeration control (ABAC) with different hierarchical combinations of PI, MPC, and fuzzy were studied, and it is observed that there is a trade-off between operational cost and effluent quality [18][19][20][21]. Maheswari et al. (2020) designed the nested control loop on three-stage biological treatment for ammonia changes and they observed that Effluent Quality Index (EQI ) is improved with higher operational costs [22]. Shiek et al. (2021) implemented an ammonia-based aeration control (ABAC) with four different combinations of controllers like PI-MPC, MPC-MPC, PI-fuzzy, and MPC-fuzzy, which resulted in a tradeoff between Operational Cost Index (OCI) and EQI. ...
Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality.
Wastewater Treatment Plants (WWTP) are highly nonlinear and complex processes and their control is a challenging task. In literature, the works carried out on WWTPs have considered the sensors and actuators without any noise and delay. This is unrealistic compared with the real-time case study. The BSM1 plant is tested to ensure that it has a realistic sense, considering sensor delay and noise, as well as actuator delay. The conventional Proportional Integral controller is designed at the lower level and advanced controllers such as model predictive control (MPC) and Fuzzy Logic control are designed to achieve enhanced set-point tracking. These controls are developed for both nitrate and dissolved oxygen loops which represent more accurate behavior of the realistic WWTP. The hierarchical control loop for ammonia is designed using Fuzzy to decrease the total ammonia and nitrogen violations in the effluent. The MPC-Fuzzy control strategy resulted in a notable change in the effluent quality because of the substantial decrease observed in the effluent violations for dry weather conditions and 40% reduction in total nitrogen, 6% reduction in ammonia violations.
Wastewater treatment plants (WWTPs) are highly non-linear and complex processes, and their control is a challenging task. In the literature, the works carried out on WWTPs have considered the sensors and actuators without any noise and delay. This is unrealistic compared with the real-time case studies. In this research, controllers are designed and implemented on WWTPs by considering noise and delay in sensors and delay in actuators. The conventional proportional-integral (PI) controller is designed at the lower level and advanced controller such as model predictive control (MPC) is designed at higher level for both Nitrate and Dissolved Oxygen loops. The hierarchical control loop for ammonia decreases the total ammonia violations in the effluent. The MPC–MPC control strategy resulted in a notable change in the effluent quality and reduced 29% of total nitrogen violations and a 5% reduction is observed in ammonia violations as well. Controllers are designed and implemented on WWTPs by considering realistic case of noise in sensors and delay in actuators.The conventional PI controller is designed at the lower level and advanced controller such as model predictive control (MPC) is designed at higher level for both Nitrate and Dissolved Oxygen loops.The MPC–MPC control strategy resulted in a notable change in the effluent quality and reduced 29% of total nitrogen violations and a 5% reduction is observed in ammonia violations. Controllers are designed and implemented on WWTPs by considering realistic case of noise in sensors and delay in actuators. The conventional PI controller is designed at the lower level and advanced controller such as model predictive control (MPC) is designed at higher level for both Nitrate and Dissolved Oxygen loops. The MPC–MPC control strategy resulted in a notable change in the effluent quality and reduced 29% of total nitrogen violations and a 5% reduction is observed in ammonia violations.
In the analysis of wastewater treatment plants (WWTPs), it is very general to use ideal sensors and actuators without noise and delay for the measurement of dissolved oxygen, flow rate, ammonia, and nitrate levels. However, to control WWTPs in practice, non-ideal sensors and actuators only exist, and hence evaluation by considering non-ideal sensors is required. It is important to assess the performance of the advanced control strategies under non-ideal conditions and it is addressed in this research. Biological WWTP model such as benchmark simulation model (BSM1-P) with activated sludge process of ASM3bioP is used in the present study. In this chapter, non-ideal sensors with delay and noise and different combinations of sensors are analyzed. Three control strategies are designed (Proportional integral (PI) controller, Model predictive controller (MPC), and Fuzzy logic controllers (FLC)) to check the effluent quality and operating costs. For the right dissolved oxygen and nitrite monitoring assessment, the significance of delay and noise in sensors are much higher, as for ideal sensors a good control performance is achieved by increasing the controller gain. A total of nine different sensor class combinations are studied. MPC has shown an improved effluent quality of 3.6% in the sensor class combination of DO–BO and NO–BO. Fuzzy is not responding well to the sensor class combinations and there are no improvements. Operational cost is improved by 1% in the sensor class combination of DO–DO and NO–DO in the application of PI controller.KeywordsDissolved oxygenEffluent qualityOperational costAnd Ideal sensor
A novel control strategy is developed for a municipal wastewater treatment plant (WWTP) consisting of anaerobic-anoxic-aerobic reactors. The idea is to generate more organic matter with a reduction of nitrate concentration in the anoxic section so that more biological phosphorus (P) removal happens. For this, the Supervisory and Override Control Approach (SOPCA) is designed based on the benchmark simulation model (BSM1-P) and is evaluated by considering dynamic influent. In the supervisory layer, proportional integral (PI) and fuzzy controllers are designed. Additionally, dissolved oxygen (So) control loops in the aerobic reactors are designed. PI controller is designed for control of nitrate levels in the anoxic reactors and is integrated with override control and supervisory layer. It is found that the novel SOPCA approach gave better nutrient removal with slightly higher operating costs when So control is not put in place. With three So control loops in place, the WWTP showed better effluent quality and lower cost. Here, the improved removal efficiency of 28.5% and 20.5% are obtained when Fuzzy and PI control schemes respectively are used in the supervisory layer. Therefore, the application of SOPCA is recommended for a better P removal rate.
Wastewater treatment plants (WWTP) are highly non-linear operations concerned with huge disturbances in flow rate and concentration of pollutants with uncertainties in the composition of influent wastewater. In this work, the activated sludge process model with seven reactor configuration in the ASM3bioP framework is used to achieve simultaneous removal of nitrogen and phosphorus. A total of 8 control approaches are designed and implemented in the advanced simulation framework for assessment of the performance. The performance of the WWTP (effluent quality index and global plant performance) and the operational costs are also evaluated to compare the control approaches. Additionally, this paper reports a comparison among proportional integral (PI) control, fuzzy logic control, and model-based predictive control (MPC) configurations framework. The simulation outcomes indicated that all three control approaches were able to enhance the performance of WWTP when compared with open loop operation.
Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86%–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.
A general framework for the formulation and analysis of an overall decision support index is discussed. It is indicated that such an index allow evaluation of the combined effects of both design and operation (i) during the planning phase of new WWTPs, as well as (ii) for the evaluation of new operational strategies versus traditional expansions of plants already in operation. Attention is drawn to the problems of incorporating such factors as plant flexibility and robustness against failures in the index. These factors become especially relevant when decisions are to be made that affect the life span of a WWTP. Spatial and temporal separation of the optimisation problem are proposed to make the approach operational. It is not the intention of the authors to provide an exhaustive list of objective criterion functions, but rather to give the reader some examples illustrating the approach.
This paper presents a hierarchical control architecture for operation of biological wastewater treatment plants. The main goal of this control architecture is conceived being that of avoiding violations of effluent pollution limits while, at the same time, keeping reasonable values for the quality of the effluent as well as for the costs of operating the plant. Regarding the working scenario, the benchmark established within the BSM1 platform is adopted as working scenario to simulate a wastewater treatment plant. A hierarchical structure regulates the dissolved oxygen (DO) of the three aerated tanks based on the ammonium and ammonia nitrogen concentration (NH) in the fifth tank (NH5). The proposed architecture is a two layer cascaded control where the lower level deals with the goal of keeping the process variables to the desired set-points that are determined by the upper layer. The main contribution of the paper is to operate the upper layer under the idea of not keeping a fixed set-point but following simple rules dictated by the process reaction equations. While an Model Predictive Control with feedforward compensation is used for the lower level, an affine function is selected for the higher level. To avoid the total nitrogen in the effluent (N tot,e) to go over the establish limits, an affine function, implemented with a sliding window, that determines a supplementary external carbon to be added in the first tank. Also effluent violations of NH, (NH e), limits is tackled by means of a combination of a linear function and an exponential is proposed. As a result, N tot,e violations and NH e violations are avoided.
Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. The demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant is an important research theme recently. Thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological P and N removal Benchmark Simulation Model No.1 (BSM1-P). In addition, according to the results of applying PI controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the PI controllers with fuzzy controllers. In this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal.
This paper presents a proposal with the objective of eliminating violations of ammonia nitrogen in the effluent (NH e) of a wastewater treatment process. Furthermore, improving effluent quality and reducing operating costs and the percentage of violations of total nitrogen in the effluent (N tot,e) are also considered. The evaluation of control strategies are conducted with the Benchmark Simulation Model N o 1 (BSM1). Several controllers have been proposed: Model Predictive Control (MPC) with inlet flow rate feedforward compensation (MPCFF), Fuzzy controller and Exponential, Linear and Affine Functions. MPCFF and Fuzzy controller are applied in a hierarchical structure to improve effluent quality, to reduce operational costs and to decrease NH e peaks. Exponential, Linear and Affine Functions are implemented, along with the hierarchical control structure, to avoid NH e violations. The results are presented and compared with the default control strategy of BSM1 for dry, rain and storm weahter conditions. They show that the NH e violation removal is achieved for the three weather conditions. For dry influent also an improvement of effluent quality and a reduction of operational cost are acquired. For the rain and storm cases, an increment of operational cost is required.
Aeration control at wastewater treatment plants based on ammonia as the controlled variable is applied for one of two reasons: (1) to reduce aeration costs, or (2) to reduce peaks in effluent ammonia. Aeration limitation has proven to result in significant energy savings, may reduce external carbon addition, and can improve denitrification and biological phosphorus (bio-P) performance. Ammonia control for limiting aeration has been based mainly on feedback control to constrain complete nitrification by maintaining approximately one to two milligrams of nitrogen per liter of ammonia in the effluent. Increased attention has been given to feedforward ammonia control, where aeration control is based on monitoring influent ammonia load. Typically, the intent is to anticipate the impact of sudden load changes, and thereby reduce effluent ammonia peaks. This paper evaluates the fundamentals of ammonia control with a primary focus on feedforward control concepts. A case study discussion is presented that reviews different ammonia-based control approaches. In most instances, feedback control meets the objectives for both aeration limitation and containment of effluent ammonia peaks. Feedforward control, applied specifically for switching aeration on or off in swing zones, can be beneficial when the plant encounters particularly unusual influent disturbances.
The quality of simulation results can be significantly affected by errors in the published model (typing, inconsistencies, gaps or conceptual errors) and/or in the underlying numerical model description. Seven of the most commonly used activated sludge models have been investigated to point out the typing errors, inconsistencies and gaps in the model publications: ASM1; ASM2d; ASM3; ASM3 + Bio-P; ASM2d + TUD; New General; UCTPHO+. A systematic approach to verify models by tracking typing errors and inconsistencies in model development and software implementation is proposed. Then, stoichiometry and kinetic rate expressions are checked for each model and the errors found are reported in detail. An attached spreadsheet (see http://www.iwaponline.com/wst/06104/0898.pdf) provides corrected matrices with the calculations of all stoichiometric coefficients for the discussed biokinetic models and gives an example of proper continuity checks.
As the largest single energy-consuming component in most biological wastewater treatment systems, control of aeration is of great interest seen from an energy savings point of view. This paper suggests a simple way of using on-line ammonium measurements to control aeration in a pre-denitrification plant by controlling the dissolved oxygen setpoint. The controller works primarily by feed-forward based on an ammonium sensor located at the head of the aerobic process part. Using online in-situ sensor measurements directly from the process have the important advantage over effluent measurements that there is no or very short time delay for information. The controller has been implemented in a full-scale wastewater treatment plant for a period of 35 days. During the experiment two identical activated sludge lines were used. The controller was implemented in one line, while the other line worked as a reference for comparison. The preliminary results indicate that the described control strategy leads to energy savings for the aeration in the region of 5-15%, while maintaining approximately the same effluent quality as in the reference line. Even higher energy savings can probably be achieved by optimising the controller. An automatic procedure for updating the controller parameters based on dynamic effluent ammonium measurement has been tested.
In the paper three linear aeration controllers that can be easily implemented are presented and evaluated on the activated sludge process pilot plant. Controllers differ according to the information that is used about the process, which can be oxygen in the last aerobic reactor, ammonia in the last aerobic reactor and ammonia in the influent. The aeration controllers that are addressed are: oxygen cascade PI controller, ammonia cascade PI controller and ammonia feedforward-cascade PI controller. Experiments show that, in comparison with the oxygen cascade PI controller, the ammonia cascade PI controller allows better control of effluent ammonia and airflow savings of around 23%, while the ammonia feedforward-cascade PI controller gives the best reduction of ammonia peaks and can save up to 45% of the airflow.
This paper presents a thorough review of control technologies that have been applied to wastewater treatment processes in the environmental engineering regime in the past four decades. It aims to provide a comprehensive technological review for both water engineering professionals and control specialists, giving rise to a suite of up-to-date pathways to impact this field in light of the classified technology hubs. The assessment was conducted with respect to linear control, linearizing control, nonlinear control, and artificial intelligence-based control. The application domain of each technology hub was summarized into a set of comparative tables for a holistic assessment. Challenges and perspectives were offered to these field engineers to help orient their future endeavor.
Data scarcity can be considered as the main limitation for a more widespread utilization of mathematical models in the design, optimization and control of biological nutrient removal activated sludge systems (BNRAS). High cost and demanding workload related to experimental data and sufficient sampling campaigns make the data collection process an unpleasant necessity for managing stakeholders in modelling projects. Complicated use of online-sensors leading to frequent erroneous readings and dynamic nature of wastewater treatment processes can intensify the data scarcity problems. This paper investigates the influence of data scarcity on the development and calibration of wastewater treatment plant (WWTP) models. A straightforward methodology is proposed to address the challenges associated with data quality and quantity problems in modelling of a BNRAS in the largest Italian WWTP located in Castiglione, Italy. The plant operational modes, weather condition and sensor performance during the sampling campaigns were the main sources of the data scarcity. Influent, biokinetic, aeration, hydraulic and transport, clarifier, energy consumption and effluent sub-models were calibrated by use of the proposed extensive step-wise calibration process. The Monte Carlo analysis was performed to quantify the uncertainty of the modelling results. The proposed methodology could be implemented in engineering practice to develop and calibrate the WWTP models while it increases the awareness about modelling robustness and its characterized uncertainty to avoid bad modelling practice.
The Activated Sludge Model No. 2 is introduced as a further development of Activated Sludge Model No. 1. Model No. 2 introduces phosphorus accumulating organisms (PAO) and allows us to simulate the behaviour of biological nutrient removal activated sludge systems. Typical wastewater composition and a set of stoichiometric and kinetic parameters are provided in order to make reasonable predictions. The model has not yet been calibrated or verified in any full-scale application. This report contains a simplified version of the full Model No. 2; the full model is published in the final report of the Task Group (Henze et al., 1994).
Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. And the demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant (WWTP) is an important research theme. This paper presents a comparision between PI and fuzzy logic control applied to the combined phosphorus removal Benchmark Simulation Model 1 (BSM1-P) wastewater treatment process to enhance the P removal. A default control strategy which contains two control loops and is similar to the one given by the original version of BSM1 was tested on BSM1-P. In addtion, another control strategy that contains three dissolved oxygen (DO) controllers for all the aerabic tanks was also applied. For each control strategy, at first the conventional PI controllers were applied to see the performance. Secondly fuzzy logic controllers replaced the PI controllers with the popurse of enhancing P removal. The simulation results showed that both control strategies were able to improve the performance of WWTP and the application of fuzzy controllers was more beneficial for the P removal.
Wastewater treatment plants are large non-linear systems subject to large perturbations in wastewater flow rate, load and composition. Nevertheless these plants have to be operated continuously, meeting stricter and stricter regulations.
Many control strategies have been proposed in the literature for improved and more efficient operation of wastewater treatment plants. Unfortunately, their evaluation and comparison – either practical or based on simulation – is difficult. This is partly due to the variability of the influent, to the complexity of the biological and biochemical phenomena and to the large range of time constants (from a few minutes to several days). The lack of standard evaluation criteria is also a tremendous disadvantage. To really enhance the acceptance of innovative control strategies, such an evaluation needs to be based on a rigorous methodology including a simulation model, plant layout, controllers, sensors, performance criteria and test procedures, i.e. a complete benchmarking protocol.
This book is a Scientific and Technical Report produced by the IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants . The goal of the Task Group includes developing models and simulation tools that encompass the most typical unit processes within a wastewater treatment system (primary treatment, activated sludge, sludge treatment, etc.), as well as tools that will enable the evaluation of long-term control strategies and monitoring tasks (i.e. automatic detection of sensor and process faults).
Work on these extensions has been carried out by the Task Group during the past five years, and the main results are summarized in Benchmarking of Control Strategies for Wastewater Treatment Plants . Besides a description of the final version of the already well-known Benchmark Simulation Model no. 1 (BSM1), the book includes the Benchmark Simulation Model no. 1 Long-Term (BSM1_LT) – with focus on benchmarking of process monitoring tasks – and the plant-wide Benchmark Simulation Model no. 2 (BSM2).
This title belongs to Scientific and Technical Report Series
ISBN: 9781780401171 (eBook)
ISBN: 9781843391463 (Print)
This paper describes the implementation of a simulation benchmark for studying the influence of control strategy implementations on combined nitrogen and phosphorus removal processes in a biological wastewater treatment plant. The presented simulation benchmark plant and its performance criteria are to a large extent based on the already existing nitrogen removal simulation benchmark. The paper illustrates and motivates the selection of the treatment plant lay-out, the selection of the biological process model, the development of realistic influent disturbance scenarios for dry, rain and storm weather conditions respectively, the definition of performance indexes that include the phosphorus removal processes, and the selection of a suitable operating point for the plant. Two control loops were implemented: one for dissolved oxygen control using the oxygen transfer coefficient KLa as manipulated variable, the second one for nitrate control in the anoxic zone using the internal recirculation flow rate as manipulated variable. Dynamic simulations for different dissolved oxygen set points illustrate the complex interactions in this plant, and the necessity for a continuous trade off between supplying sufficient oxygen to promote nitrification on the one hand, and the need for low dissolved oxygen concentrations on the other hand to allow sufficient development of phosphorus accumulating organisms. The potential for aeration energy savings in the plant is highlighted based on the dissolved oxygen profiles resulting from open loop simulations with a dynamic dry weather influent scenario. The influence of the dissolved oxygen set point selection on the nitrate control loop performance observed in the simulations further illustrates the need for a plant-wide optimization approach to reach optimal plant performance.
This paper describes a modelling study where five new benchmark plant design configurations for biological nutrient removal (A(2)/O, UCT, JHB, MUCT and BDP-5 stage) are simulated and evaluated under different model assumptions. The ASM2d including electron dependent decay rates is used as the reference model (A1). The second case (A2) adds nitrite as a new state variable, describing nitrification and denitrification as two-step processes. The third set of models (A3 and A4) considers different reactive settlers types (diffusion-limited/non limited). This study analyses the importance of these new model extensions to correctly describe the nitrification behaviour and the carbon source competition between ordinary heterotrophic organisms (OHO) and polyphosphate accumulating organisms (PAO) under certain operation conditions. The economic and environmental aspects when meeting the P discharge limits by adding an external carbon source are also studied.
Activated sludge wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior, however, the control of the dissolved oxygen level in the reactors plays an important role in the operation of the facility. For this reason a new approach is studied in this paper using simulated case-study approach: model predictive control (MPC) has been applied to control the dissolved oxygen concentration in an aerobic reactor of a wastewater treatment plant. The control strategy is investigated and evaluated on two examples using systematic evaluation criteria: in a simulation benchmark – developed for the evaluation of different control strategies – the oxygen concentration has to be maintained at a given level in an aerobic basin; and a changing oxygen concentration in an alternating activated sludge process is controlled using MPC technique. The effect of some MPC tuning parameters (prediction horizon, input weight, sampling time) are also investigated. The results show that MPC can be effectively used for dissolved oxygen control in wastewater treatment plants.
A dynamic model of the clarification-thickening process is presented. Based on the solids flux concept and on a mass balance around each layer of a one-dimensional settler, this model can simulate the solids profile throughout the settling column, including the underflow and effluent suspended solids concentrations under steady-state and dynamic conditions. The model makes use of a special settling velocity equation designed to simulate the settling velocity of dilute and more concentrated suspensions. The model can be applied to both primary and secondary settlers to simulate dynamic and steady-state conditions. Examples based on full-scale and pilot-scale experimental data taken from the literature serve to illustrate the application of the model to secondary settlers. Results of the analysis confirm that the model can serve to predict the effluent and underflow suspended solids concentrations under a variety of conditions.
As the largest single energy-consuming component in most biological wastewater treatment systems, aeration control is of great interest from the point of view of saving energy and improving wastewater treatment plant efficiency. In this paper, three different strategies, including conventional constant dissolved oxygen (DO) set-point control, cascade DO set-point control, and feedforward-feedback DO set-point control were evaluated using the denitrification layout of the IWA simulation benchmark. Simulation studies showed that the feedforward-feedback DO set-point control strategy was better than the other control strategies at meeting the effluent standards and reducing operational costs. The control strategy works primarily by feedforward control based on an ammonium sensor located at the head of the aerobic process. It has an important advantage over effluent measurements in that there is no (or only a very short) time delay for information; feedforward control was combined with slow feedback control to compensate for model approximations. The feedforward-feedback DO control was implemented in a lab-scale wastewater treatment plant for a period of 60 days. Compared to operation with constant DO concentration, the required airflow could be reduced by up to 8-15% by employing the feedforward-feedback DO-control strategy, and the effluent ammonia concentration could be reduced by up to 15-25%. This control strategy can be expected to be accepted by the operating personnel in wastewater treatment plants.
Activated sludge model no. 3 with bioP module (ASM bioP) implemented within the benchmark simulation model no
- K Solon