Control Of Wastewater Treatment Plants Research Articles

Characteristics and key driving factors of nitrous oxide emissions from a full-scale landfill leachate treatment system

The characteristics of N2O emission from a full-scale landfill leachate treatment system were investigated by in-situ monitoring over 1.4 years and driving factors responsible for these emissions were identified by statistical analysis of multidimensional environmental variables. The results showed that the maximum N2O emission flux of 2.21 × 107 mg N·h−1 occurred in the nitrification tanks, where 98.5 % of the total N2O was released, with only 1.5 % of the total N2O emitted from the denitrification tanks. Limited oxygen in nitrification tank was responsible for N2O hotspot. The N2O emissions from the parallel lines A and B (both comprising the primary biochemical system) accounted for 52.6 % and 46.6 %, respectively, while the secondary biochemical system contributed only 0.8 % to the total emissions. Higher nitrite concentration in line A and lower nitrogen loading in the secondary biochemical system caused these discrepancies. We found that during the steady state of leachate treatment, intensive N2O emissions of 253.4–1270.5 kg N·d−1 were measured. The corresponding N2O emission factor (EF) ranged from 8.86 to 49.6 %, much higher than those of municipal wastewater treatment. But N2O EF was inconceivably as low as 0.42 % averagely after system maintenance. Influent with low salinity was the key reason, followed by the high MLSS and varying microbial community after maintenance. The dominant genus shifted from Lentimicrobium and Thauera to Norank-F-Anaerolineaceae and Unclassified-F-Rhodocyclaceae. This study underscores the significance of landfill leachate treatment in urban nitrogen management and provides valuable insights into the characteristics and driving factors of N2O emissions from such systems. The findings offer important references for greenhouse gas emission inventories and strategies for N2O control in full-scale wastewater treatment plants.

Release characteristics and risk assessment of volatile sulfur compounds in municipal wastewater treatment plants

In recent years, the malodorous gases generated by sewage treatment plants have gradually received widespread attention due to their sensory stimulation and health hazards. The emission concentration, sensory evaluation and health risk assessment of volatile sulfur compounds (VSCs) were all explored in two municipal wastewater treatment plants (WWTPs) with oxidation ditch and anaerobic/oxic treatment process, respectively. The VSCs concentration showed the highest amount in the primary treatment unit in both the two WWTPs (73.3% in Plant A and 93.0% in Plant B), while the H2S took the main role in the composition of VSCs. However, H2S took a larger percentage in Plant A (84.5% ∼ 87.0%) rather than Plant B (61.2% ∼ 83.5%), which may be due to the different operating conditions and sludge properties in different treatment process. Besides, H2S also gained the first rank in the sensory evaluation and health risk assessment, which may cause considerable sensory irritation and health risk to workers and surrounding residents. Furthermore, the influencing factor analyses of VSCs emission showed that the temperature of water and air, ORP of sludge made the greatest effect on VSCs release. This study provides theoretical and data support for the research of VSCs emission control in WWTPs.

Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants

With the deepening of modernization and industrialization, the issues of water pollution and scarcity have become more pressing. To address these issues, many wastewater treatment factories have been built to improve the reuse of water resources. However, the control of the wastewater treatment process (WWTP) is a complex task due to the highly nonlinear and strongly coupled nature. It is challenging to develop the accurate mechanism models of the wastewater treatment system. The improvement of the efficiency for the WWTP is crucial to safeguard the urban ecological environment. In this paper, adaptive critic with weight allocation (ACWA) is developed to address the optimal control problem in the WWTP. Different from the previous methods of the WWTP, system modeling is not adopted in this paper, which meets the actual physical background of the wastewater treatment system to a great extent. In addition, the actor-critic algorithm in reinforcement learning is used as the basic structure in the ACWA. It is worth noting that a novel weighted action-value function and the advantage function are introduced in the weight updating process of the action network and the critic network. The experimental results show that the control accuracy of the ACWA is greatly improved compared with the previous control methods.

Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Global population growth drives demand for clean water, energy, and wastewater production in urban areas. Most existing wastewater treatment plants (WWTPs) are energy- and cost-intensive. To achieve the UN Sustainable Development Goals (SDGs) by 2030, many regions have implemented strict policies for energy consumption and contaminated water discharge, promoting environmental sustainability. This paper attempts to enhance the achievement of SDGs in the wastewater treatment industry by proposing a novel method for hierarchical control of wastewater treatment plants to ensure energy and cost efficiency with less knowledge of the process model, fewer measurements, and fewer control loops. This controller is the combination of a machine learning control technique with a bio-inspired computational algorithm. To evaluate and validate the performance of the proposed controller on the WWTP, the benchmark simulation model no. 1 (BSM1) has been used. The most significant contributions of this paper show that 488.96 KWh/d, 3620.08 Kg/d, and 3454.55 KWh/d of aeration energy cost index, sludge production cost index, and operational cost index are respectively saved when compared to the default control strategy (DCS). Also, 893.69 Kg pollution units per day of effluent fine-related costs have been successfully recovered when compared to the DCS. The experimental results illustrate that the proposed control yields significant amelioration of the energy and cost profile of the WWTP with no effluent violation in dry, rain, and storm weather conditions.

Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study

Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.

Hybrid supervised hierarchical control of a biological wastewater treatment plant.

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Abstract The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. Key to the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with increasing forecasting horizons for prolonged anomalous events.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.

Model-Free Adaptive Nonsingular Fast Integral Terminal Sliding Mode Control for Wastewater Treatment Plants

The regulation of wastewater treatment plants (WWTPs) is a challenge due to their complex biological and chemical characteristics and their accurate mathematical model is generally not accessible because of the limitation of available measurements. To overcome such challenges, in this paper, a novel model-free adaptive nonsingular fast integral terminal sliding mode control (MFA-NFITSMC) is proposed. Firstly, based on the concept of dynamic linearization, a compact format dynamic linearized (CFDL) data model for the WWTP is established. Secondly, a novel fast integral terminal sliding mode surface is proposed to accelerate the convergence of tracking error and a discrete-time MFA-NFITSMC is created using the CFDL model as a basis; then, its stability is proved by theoretical analysis. Finally, the experimental verification is conducted based on the Benchmark Simulation Model No. 1 and the results show that the proposed method has a higher tracking accuracy and stronger robustness than other methods in the control of WWTPs.

Ecological and functional differences of abundant and rare sub-communities in wastewater treatment plants across China

The microbial community in activated sludge is composed of a small number of abundant sub-community with high abundance and a large number of rare sub-community with limited abundance. Our knowledge regarding the ecological properties of both abundant and rare sub-communities in activated sludge is limited. This article presented an analysis of functional prediction, assembly mechanisms, and biogeographic distribution characteristics of abundant and rare sub-communities in 211 activated sludge samples from 60 wastewater treatment plants across China. Moreover, this study investigated the dominant factors influencing the community structure of these two microbial groups. The results showed that the functions associated with carbon and nitrogen cycling were primarily detected in abundant sub-community, while rare sub-community were primarily involved in sulfur cycling. Both microbial groups were mainly influenced by dispersal limitation, which, to some extent, resulted in a distance-decay relationship in their biogeographic distribution. Moreover, a higher spatial turnover rate of rare sub-communities (0.0887) suggested that spatial differences in microbial community structure among different WWTPs may mainly result from rare sub-community. Moreover, SEM showed that geographic locations affected rare sub-communities greatly, which agreed with their higher dispersal limitation and turnover rate. In contrast, influent characteristics showed stronger correlations with abundant sub-communities, suggesting that abundant sub-community may contribute more to the removal of pollutants. This study enhanced our understanding of abundant and rare microorganisms in activated sludge especially the role of rare species and provided scientific evidence for precise regulation and control of wastewater treatment plants.

Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

Multi-source and multimodal data fusion for improved management of a wastewater treatment plant

Wastewater treatment plants (WWTPs) are complex systems presenting stochastic, non-linear, and non-stationary behavior, which makes their operational management very challenging. In this context, data collected from distributed sources across the plant play a central role in the optimized operation and control of WWTPs. However, even when available, the use of collected data is far from trivial due to the coexistence of asynchronous measurements, data with different granularity, measurements of different quality (precision, accuracy), multimodal sources (sensors, spectra, images, hyphenated instrumentation), among other aspects related to the data life cycle. Such heterogeneity in process data characteristics hinders the application of most off-the-shelf data analytics methods. Flexible solutions able to cope with the complexity of systems and of the data they generate are therefore necessary to overcome these limitations and enable an effective analysis and operation of WWTPs. In this article, data-fusion approaches for handling multiple heterogeneous sources of process data are developed and comparatively tested. Priority is given to solutions that can flexibly be adapted to different specific operational contexts. The methodologies are tested on an industrial case study (WWTP), where the concentration of a toxin in the effluent stream is to be predicted from available heterogeneous data. Single- and multi-source modeling approaches are contemplated and a nested cross-validation method was developed to handle the time-series nature of the models. Bayesian fusion synergistically combines data from different sources considering their uncertainty, standing out among the methodologies tested as offering a good balance in terms of accuracy (RMSEP = 1.34), stability (prequential IQR = 0.034), and flexibility (to accommodate missing and new sources).

Re-evaluation and modification of dehydrogenase activity tests in assessing microbial activity for wastewater treatment plant operation

Reliable and cost-effective methods for monitoring microbial activity are critical for process control in wastewater treatment plants. The dehydrogenase activity (DHA) test has been recognized as an efficient measure of biological activity due to its simplicity and broad applicability. Nevertheless, the existing DHA test methods suffer from imperfections and are difficult to implement as routine monitoring techniques. In this work, an accurate and cost-effective modified DHA approach was developed and the procedure for the DHA test was critically evaluated with respect to the standard construction, sample pretreatment, incubation and extraction conditions. The feasibility of the modified DHA test was demonstrated by comparison with the oxygen uptake rate and adenosine triphosphate in a sequencing batch reactor. The sensitivities of the two typical tetrazolium salts to toxicant inhibition by heavy metals and antibiotics were compared, revealing that 2,3,5-triphenyltetrazolium chloride (TTC) exhibited a higher sensitivity. Furthermore, the sensitivity mechanism of the two DHA tests was elucidated through electrochemical experiments, theoretical analysis and molecular simulations. Both tetrazolium salts were found to be effective artificial electron acceptors due to their low redox potentials. Molecular docking simulations revealed that TTC could outperform other tetrazolium salts in accepting electrons and hydrogens from dehydrogenase. Overall, the modified DHA approach presents an accurate and cost-effective way to measure microbial activity, making it a practical tool for wastewater treatment plants.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.

Ensemble water quality forecasting based on decomposition, sub-model selection, and adaptive interval

The prediction of effluent quality for wastewater treatment plants (WWTPs) has caused widespread concern due to its essential role in ensuring water quality standards and reducing energy consumption. However, the complex nonlinearity of WWTPs leads to difficulties in forecasting and less attention to forecast uncertainty. A novel ensemble water quality forecasting (EWQF) system is proposed that incorporates data preprocessing, point prediction and interval prediction. The system provides an accurate prediction of effluent quality and analyses this uncertainty, for enabling feed-forward control of WWTPs. Specifically, the original water quality data is decomposed into subsequences containing more information and less noise based on improved variational modal decomposition (IVMD). The optimal sub-model for each sub-series is selected from six prediction models based on the sub-model selection strategy, and the point prediction results for water quality are obtained by combining the prediction results of the sub-models. Robust and reliable prediction interval construction based on adaptive kernel density estimation. The results demonstrate that the EWQF achieves optimal point prediction results (R2 = 0.955). The EWQF interval prediction achieves the optimal coverage width criterion (CWC) for different confidence intervals and decision objectives. These results demonstrate that EWQF systems can perform excellent point and interval prediction.

Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning

In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and climatic disturbances, among others. The dynamics of complex real processes are difficult to accurately approximate by mathematical models due to the complexity of the process itself. Consequently, model-based control can fail in practical application due to the mismatch between the mathematical model and the real process. Control based on the model-free reinforcement deep learning (RL) methodology emerges as an advantageous method to arrive at suboptimal solutions without the need for mathematical models of the real process. However, convergence of the RL method to a reasonable control for complex processes is data-intensive and time-consuming. For this reason, the RL method can use the transfer learning approach to cope with this inefficient and slow data-driven learning. In fact, the transfer learning method takes advantage of what has been learned so far so that the learning process to solve a new objective does not require so much data and time. The results demonstrate that cumulatively achieving conflicting objectives can efficiently be used to approach the control of complex real processes without relying on mathematical models.

Activity of Ammonium-Oxidizing Bacteria—An Essential Parameter for Model-Based N2O Mitigation Control Strategies for Biofilms

The reduction in N2O emissions is an important task in the control of wastewater treatment plants. Since local operating conditions, especially inside biofilms, are usually not known, models are an important tool in the development and implementation of control strategies. For a pilot-scale nitrifying biofilm reactor and an SBR, different operational strategies to reduce autotrophic nitrous oxide (N2O) formation were developed and tested by applying a combination of modeling and measurement. Both approaches highlighted the relevance of addressing the actual AOB activity as a sensitive control variable. The investigated strategies, therefore, focused on decreasing the AOB-related NH4 conversion rate, as autotrophic N2O formation is directly linked to AOB activity. The results showed that the biofilm system was more advantageous compared with suspended sludge systems. A higher AOB content resulted in a decrease in AOB activity, leading to fewer N2O emissions at the same reactor performance. The highest reduction in autotrophic N2O formation (SBR: 25%; Biofilm: 27%) was obtained by maximizing the aerated time per day and minimizing the number of aeration cycles (the suppression of nitrite-oxidizing bacteria still needed to be ensured). A higher biofilm thickness or a higher sludge mass in the SBR, however, did not have a noteworthy positive effect since no additional biomass could be kept in the system in the long term due to limited substrate availability. Besides nitritation, denitrification was also identified as a relevant source of N2O in both systems (biofilm: main source) due to the inhibition of N2O reduction by nitrous acid (elevated nitrite concentrations in combination with pH values < 7).