Control Of Wastewater Treatment Plants Research Articles

The effect of direct addition of iron(III) on anaerobic digestion efficiency and odor causing compounds

The role of iron addition to sewage sludge prior to anaerobic digestion was evaluated to determine the effect of iron on digestion performance and generation of odor-causing compounds. Hydrogen sulfide (H2S) and volatile organic sulfur compounds (VOSCs) were the odorous gases evaluated in this study. Samples were obtained from seven municipal wastewater treatment plants (WWTPs), and batch anaerobic digestion tests were conducted using primary and secondary sludges at 30 day solids retention time (SRT) under mesophilic conditions. Volatile solid removal (VSR) was highly predictable with background iron concentrations measured in the combined sludge. They were likely to increase as influent iron content increased. 1.25% w/w ferric chloride (FeCl3) was added to the anaerobic digester feed in order to simulate iron addition for sulfide control in full-scale WWTPs. The results showed that it had a positive impact on digestion performance with higher VSR and odor control with reduced H2S and TVOSCs in the headspace gas of dewatered biosolids considered in the tests. Ferric chloride is considered a beneficial additive as a strategy for an odor mitigation, not to mention more efficient digestion under anaerobic conditions.

Autonomous Adaptive and Active Tuning Up of the Dissolved Oxygen Setpoint in a Wastewater Treatment Plant Using Reinforcement Learning

The aim of this paper is to face one of the main problems in the control of wastewater treatment plants (WWTPs). It appears that the control system does not respond as it should because of changes on influent load or flow. In that case, it is required that a plant operator tunes up the parameters of the plant. The dissolved oxygen setpoint is one of those parameters. In this paper, we present a model-free reinforcement learning agent that autonomously learns to actively tune up the oxygen setpoint by itself. By active, we mean continuous, minute after minute, tuning up. By autonomous and adaptive, we mean that the agent learns just by itself from its direct interaction with the WWTP. This agent has been tested with data from the well-known public benchamark simulation model no. 1, and the results that are obtained allow us to conclude that it is possible to build agents that actively and autonomously adapt to each new scenario to control a WWTP.

Bulking control by low-dose ozonation of returned activated sludge in a full-scale wastewater treatment plant

Sludge bulking is still a problem in the operation of state-of-the-art wastewater treatment plants (WWTPs). The ozonation of returned activated sludge (RAS) is an innovative option as a non-specific measure for the control of filament growth. The applicability of sludge ozonation for bulking control of a large wastewater treatment plant was investigated. At a full-scale WWTP one lane was equipped with a sludge ozonation plant for RAS. The implemented sludge ozonation of RAS was tested against the two identical references lanes of the same WWTP. The positive effect on settleability could be clearly proven. Low-dose sludge ozonation could be a technical alternative in comparison with the established chemical measures for bulking control.

Application of multivariate virtual reference feedback tuning for wastewater treatment plant control

The objective of this paper is to apply the Virtual Reference Feedback Tuning (VRFT) to Multiple-Input Multiple-Output (MIMO) control strategies in wastewater treatment plants (WWTPs). Using the Benchmark Simulation Model No. 1 (BSM1) as a case study, the proposed data-driven approach provides reduced-order controllers only using a batch of input–output data points (i.e. manipulated variable—controlled variable), obtained from an open-loop experiment. The methodology also includes a pre-processing step that subtracts the impact of influent variability and sensor noise from the output signals. The possible interactions amongst different control loops are handled using a decoupling approach where each control signal is computed depending on the error signal of all the loops at the same time. In order to test the methodology, several control strategies are evaluated via simulation. The results show that substantial improvements in the plant performance can be obtained when controllers are implemented.

Uncertainty analysis of WWTP control strategies made feasible

The control of wastewater treatment plants can help to achieve good effluent quality, in a complex, highly non-linear environment. A key but time-demanding component of such modelling studies is uncertainty analysis (UA). The general aims of this paper are (a) to evaluate methods for reduction of the time necessary to conduct an UA, and (b) to evaluate the sensitivity of parameters and model subsystems. Two UA studies on the Benchmark Simulation Model no. 2 (BSM2) are used to illustrate how the above mentioned aims can be achieved: (1) robustness of performance evaluations against changing operation and design conditions; and (2) uncertainty of performance evaluations for a given plant layout and operation. The main conclusions are: (1) solver settings have a large impact on simulation speed and require proper attention; (2) to reach convergence in Monte Carlo simulations with Latin Hypercube Sampling, the number of simulations should be at least 50 times the number of sampled parameters, which is more than what is reported in similar studies; and (3) the number of uncertain parameters that needs to be considered to make a proper uncertainty assessment of a model can be reduced significantly by omitting parameters that have little influence.

Estimation of effluent quality using PLS-based extreme learning machines

The accurate and reliable measurement of effluent quality indices is essential for the implementation of successful control and optimization of wastewater treatment plants. In order to enhance the estimate performance in terms of accuracy and reliability, we present a partial least-squares-based extreme learning machine (called PLS-ELM) in this paper. The partial least squares (PLS) regression is applied to the ELM framework to improve the algebraic property of the hidden output matrix, which can be ill-conditional due to the high multicollinearity of the hidden layer output. The main idea behind our proposed PLS-ELM is to achieve a robust generalization performance by extracting a reduced number of latent variables from the hidden layer and using orthogonal projection operations. The results from a case study of a municipal wastewater treatment plant show that the PLS-ELM can effectively capture the input–output relationship with favorable performance against the conventional ELM.

Rheology - Key to Process Control in Conventional and Advanced Wastewater Treatment Plants

Rheology - Key to Process Control in Conventional and Advanced Wastewater Treatment Plants

Simulation and Optimization of Denitrifying Phosphorus Removal in A<sup>2</sup>/O

The GPS-X was used to simulate the denitrifying phosphorus removal in the A2/O.The improvement actions were obtained due to the simulation result.Moreover, the effect of denitrifying phosphorus removal was forecasted after the application of optimization and improvement. The prediction shows that: by adjusting the operating parameters such as SRT was adjusted to 15ds, the internal reflux ratio was adjusted to 300% and the external reflux ratio was adjusted to 100%,the denitrifying phosphorus removal in the A2/O was obviously increased. Therefore, the employment of GPS-X software in simulation and optimization can provide guidance for the operation and control in wastewater treatment plants.

An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques

One of the main problems in the automation of the control of wastewater treatment plants (WWTPs) appears when the control system does not respond as it should because of changes on influent load or flow. To tackle this difficult task, the application of Artificial Intelligence is not new, and in fact, currently Expert Systems may supervise the plant 24h/day assisting the plant operators in their daily work. However, the knowledge of the Expert System must be elicited previously from interviews to plant operators and/or extracted from data previously stored in databases. Although this approach still has a place in the control of wastewater treatment plants, it should aim to develop autonomous systems that learn from the direct interaction with the WWTP and that can operate taking into account changing environmental circumstances. In this paper we present an approach based on an agent with learning capabilities. In this approach, the agent’s knowledge emerges from the interaction with the plant. In order to show the validity of our assertions, we have implemented such an emergent approach for the N-Ammonia removal process in a well established simulated WWTP known as Benchmark Simulation Model No.1 (BSM1).

Correlation of Community Dynamics and Process Parameters As a Tool for the Prediction of the Stability of Wastewater Treatment

Wastewater treatment often suffers from instabilities and the failure of specific functions such as biological phosphorus removal by polyphosphate accumulating organisms. Since most of the microorganisms involved in water clarification are unknown it is challenging to operate the process accounting for the permanent varying abiotic parameters and the complex composition and unrevealed metabolic capacity of a wastewater microbial community. Fulfilling the demands for water quality irrespective of substrate inflow conditions may emit severe problems if the limited management resources of municipal wastewater treatment plants are regarded. We used flow cytometric analyses of cellular DNA and polyphosphate to create patterns mirroring dynamics in community structure. These patterns were resolved in up to 15 subclusters, the presence and abundances of which correlated with abiotic data. The study used biostatistics to determine the kind and strength of the correlation. Samples investigated were obtained from a primary clarifier and two activated sludge basins. The stability of microbial community structure was found to be high in the basins and low in the primary clarifier. Despite major abiotic changes certain subcommunities were dominantly present (up to 80% stability), whereas others emerged only sporadically (down to 3% stability, both according to equivalence testing). Additionally, subcommunities of diagnostic value were detected showing positive correlation with substrate influxes. For instance blackwater (r(s) = 0.5) and brewery inflow (both r(s) = 0.6) were mirrored by increases in cell abundances in subclusters 1 and 6 as well as 4 and 8, respectively. Phosphate accumulation was obviously positively correlated with nitrate (r(s) = 0.4) and the presence of denitrifying organisms (Rhodacyclaceae). Various other correlations between community structure and abiotic parameters were apparent. The bacterial composition of certain subcommunities was determined by cell sorting and phylogenetic tools like T-RFLP. In essence, we developed a monitoring tool which is quick, cheap and causal in its interpretation. It will make laborious PCR based technique less obligatory as it allows reliable process monitoring and control in wastewater treatment plants.

Combined Titrimetric Respirometer as a Real-Time Sensor to Monitor the Aerobic Biodegradation Process of Different Substrates

Off-line analysis of liquid phase samples have traditionally been used for the determination of organics and nitrogen load for process control or optimization in full-scale wastewater treatment plants. However, these sampling and analysis methods are labor-intensive and time-consuming. Besides, the results are not generally available on real-time to the operators who struggle on daily basis to meet the constant discharge license conditions of the effluent while the influent conditions change diurnally with varying loads. Combined respirometric titrimetric sensor is an on-line method that has been developed recently, indicating the oxygen demand exerted by organic and nitrogen loadings. This sensor monitors both the oxygen uptake as well as the proton production/consumption resulting from the biological reactions in activated sludge system. The basic-data can then be interpreted to find out the short-term BOD, which can be used effectively for process control and optimization. Data interpretation and model development are still in progress as researchers keep on unraveling the mystery of how the substrates are biodegraded by activated sludge. Hence, in this study, it is aimed to develop an in-depth understanding about the aerobic biodegradation process using real-time biosensors through addition of different substrates such as acetate, surfactant (SDS) and ammonium to batch cultures of activated sludge by maintaining the pH of 7.8. Basic data interpretation was carried out to calculate oxygen uptake rate (OUR) as well as proton production/consumption rate from the raw data. Besides, the improved bio-kinetic model calibration was presented in this paper explaining the oxidation of different substrates in activated sludge system.

Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies

The main objective of this paper is to demonstrate how greenhouse gas (GHG) emissions can be quantified during the evaluation of control strategies in wastewater treatment plants (WWTP). A modified version of the IWA Benchmark Simulation Model No 2 (BSM2G) is hereby used as a simulation case study. Thus, the traditional effluent quality index (EQI), operational cost index (OCI) and time in violation (TIV) used to evaluate control strategies in WWTP are complemented with a new dimension dealing with GHG emissions. The proposed approach is based on a set of comprehensive models that estimate all potential on-site and off-site sources of GHG emissions. The case study investigates the overall performance of several control strategies and demonstrates that substantial reductions in effluent pollution, operating costs and GHG emissions can be achieved when automatic control is implemented. Furthermore, the study is complemented with a scenario analysis that examines the role of i) the dissolved oxygen (DO) set-point, ii) the sludge retention time (SRT) and iii) the organic carbon/nitrogen ratio (COD/N) as promoters of GHG emissions. The results of this study show the potential mechanisms that promote the formation of CO 2, CH 4 and N 2O when different operational strategies are implemented, the existing synergies and trade-offs amongst the EQI, the OCI and TIV criteria and finally the need to reach a compromise solution to achieve an optimal plant performance.

ADD CONTROL: advanced control solutions for waste water treatment

Instrumentation, control, and automation (ICA) in wastewater treatment enables the improvement of treatment plant performance without structural modifications of the plant. Even for wastewater treatment plants (WWTPs) meeting all criteria with respect to effluent concentrations and sludge disposal, ICA can be of interest as it can help to reduce energy consumption and operating costs of the plant. Simulations are a useful and cost-effective tool for designing and evaluating different control strategies. Simulation strategies developed with existing WWTP-specific simulation packages are based on ideal sensor and actuator behavior because signal noise and potential sensor and actuator failures are not considered. Real sensor and actuator behavior including failures, however, needs to be accounted for to ensure robust controller performance despite disturbances in sensor and actuator behavior. The ADD CONTROL project aims to design, implement, and validate a new simulation tool that allows for designing and testing “practical” control solutions. A multi-layer modeling architecture is proposed for the simulation tool to represent the hierarchical architecture for automation and control in full-scale WWTPs, and to separate mathematical modeling of components related to the treatment process from components describing instrumentation and actuation devices, and components related to automation and control. The developed simulation tool is implemented based on the TORNADO framework for modeling and virtual experimentation and the WEST® product suite.

Three degrees of freedom Virtual Reference Feedback Tuning design and its application to wastewater treatment plant control

AbstractIn this paper, a three degrees of freedom (two degrees of freedom plus disturbance feedforward action) controller, tuned with the Virtual Reference Feedback Tuning is presented and applied to a wastewater treatment plant. The chosen framework is the one established in the Benchmark Simulation Model 1. Two loops were applied: control of nitrate concentration at the last anoxic section of the plant by manipulating the internal recycle flow rate using a two-degrees of freedom PI and a cascade plus feedforward approach to control ammonia at the last aeration section by manipulating the setpoint of the dissolved oxygen controller at the last aeration section using ammonia measurements from the influent. The results are very close to other more advanced approaches applied to the plant in the same conditions, therefore showing the usability of model-free approach for wastewater treatment plant control.

Artificial neural network modelling of a large-scale wastewater treatment plant operation

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41mg/L and 5.03% for COD; 1.59, 1.21mg/L and 17.10% for SS; 52.51, 44.91mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Multiple-objective evaluation of wastewater treatment plant control alternatives

Besides the evaluation of the environmental issues, the correct assessment of wastewater treatment plants (WWTP) should take into account several objectives such as: economic e.g. operation costs; technical e.g. risk of suffering microbiology-related TSS separation problems; or legal e.g. accomplishment with the effluent standards in terms of the different pollution loads. For this reason, the main objective of this paper is to show the benefits of complementing the environmental assessment carried out by life cycle assessment with economical, technical and legal criteria. Using a preliminary version of the BSM2 as a case study, different combinations of controllers are implemented, simulated and evaluated. In the following step, the resulting multi-criteria matrix is mined using multivariate statistical techniques. The results showed that the presence of an external carbon source addition, the type of aeration system and the TSS controller are the key elements creating the differences amongst the alternatives. Also, it was possible to characterize the different control strategies according to a set of aggregated criteria. Additionally, the existing synergies amongst different objectives and their consequent trade-offs were identified. Finally, it was discovered that from the initial extensive list of evaluation criteria, only a small set of five are really discriminant, being useful to differentiate within the generated alternatives.

Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty

Wastewater treatment plant control and monitoring can help to achieve good effluent quality, in a complex, highly non-linear process. The Benchmark Simulation Model no. 2 (BSM2) is a useful tool to competitively evaluate plant-wide control on a long-term basis. A method to conduct scenario analysis of process designs by means of Monte Carlo (MC) simulations and multi-criteria evaluation is presented. It is applied to the open loop version of BSM2 and to two closed loop versions, one with a simple oxygen controller and the other one with an ammonium controller regulating the set-point of the oxygen controller (cascade controller). The results show a much greater benefit of the cascade controller compared to the simple controller, both in environmental and economic terms. From an optimal process design point of view, the results show that the volume of the primary clarifier and the anoxic fraction of the reactor volume have an important impact on process performance. The uncertainty analysis of the optimal designs, also performed with MC simulations, highlights the improved and more stable effluent under closed loop control.

Applicability of the Sludge Biotic Index (SBI) for MBR activated sludge control

In 1994, Madoni created the Sludge Biotic Index (SBI) based on the presence and abundance of certain key protozoan groups. The use of this index has become increasingly common in wastewater treatment plant control and its applicability has been demonstrated. In this study, we applied the SBI index to a pilot-scale membrane bioreactor equipped with polyvinylidenefluoride hollow fibre membranes (0.04 μm pore size). Two different sludge retention times (SRT) – 25 and 35 days – were assayed, with a constant hydraulic retention time of 30 h. Operational conditions for MBR cause the fragmentation of flocs which are broken down into small dissipated elements with abundant dispersed bacteria. This process determines protozoan composition and colonization of the activated sludge. Results of the experiment showed a constant predominance of small flagellates, carnivorous ciliated protozoa and rotifers, above all in assays with 35-day SRT, independently of effluent quality. However, continuous changes in microbiota were observed, with a clear tendency for the SBI to increase over time as the sludge became more stable. Therefore, although it was not possible to establish an association between SBI and effluent quality for MBR activated sludge, the stabilisation of the process may be related to SBI.

Qualitative representation of trends (QRT) as a tool for automated data-driven monitoring of on-line sensors

The application of on-line sensors for monitoring or real-time control of wastewater treatment plants remains a challenging problem. The primary cause for this is the unknown quality of the data obtained by such on-line instruments. In the presented work sensor-internal meta-data is used for self-diagnosis. It is verified whether the complete measurement profiles obtained during regular measurement cycles of a phosphate analyzer can help to assess the sensor's status and even indicate potential faults causing faulty behaviour. A qualitative analysis approach is taken and the obtained results indicate that measurement profiles are indeed affected by the implied fault scenarios and may thus serve for proper fault detection and diagnosis.

Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty

The evaluation of activated sludge control strategies in wastewater treatment plants (WWTP) via mathematical modelling is a complex activity because several objectives; e.g. economic, environmental, technical and legal; must be taken into account at the same time, i.e. the evaluation of the alternatives is a multi-criteria problem. Activated sludge models are not well characterized and some of the parameters can present uncertainty, e.g. the influent fractions arriving to the facility and the effect of either temperature or toxic compounds on the kinetic parameters, having a strong influence in the model predictions used during the evaluation of the alternatives and affecting the resulting rank of preferences. Using a simplified version of the IWA Benchmark Simulation Model No. 2 as a case study, this article shows the variations in the decision making when the uncertainty in activated sludge model (ASM) parameters is either included or not during the evaluation of WWTP control strategies. This paper comprises two main sections. Firstly, there is the evaluation of six WWTP control strategies using multi-criteria decision analysis setting the ASM parameters at their default value. In the following section, the uncertainty is introduced, i.e. input uncertainty, which is characterized by probability distribution functions based on the available process knowledge. Next, Monte Carlo simulations are run to propagate input through the model and affect the different outcomes. Thus (i) the variation in the overall degree of satisfaction of the control objectives for the generated WWTP control strategies is quantified, (ii) the contributions of environmental, legal, technical and economic objectives to the existing variance are identified and finally (iii) the influence of the relative importance of the control objectives during the selection of alternatives is analyzed. The results show that the control strategies with an external carbon source reduce the output uncertainty in the criteria used to quantify the degree of satisfaction of environmental, technical and legal objectives, but increasing the economical costs and their variability as a trade-off. Also, it is shown how a preliminary selected alternative with cascade ammonium controller becomes less desirable when input uncertainty is included, having simpler alternatives more chance of success.