Control Of Wastewater Treatment Process Research Articles

En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest

With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.

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.

Supplementary heuristic dynamic programming for wastewater treatment process control

With the rapid development of industry, the amount of wastewater discharge is increasing. In order to improve the efficiency of the wastewater treatment process (WWTP), we often desire that the dissolved oxygen (DO) concentration and the nitrate nitrogen (NO) concentration can be controlled to track set values. However, the wastewater treatment system is a type of unknown nonlinear plant with time-varying dynamics and strong disturbances. Some traditional control methods are difficult to achieve this goal. To overcome these challenges, a supplementary heuristic dynamic programming (SUP-HDP) control scheme is established by combining the traditional control method and heuristic dynamic programming (HDP). A parallel control structure is constructed in the SUP-HDP control scheme, which not only complements the shortcomings of traditional control schemes in learning and adaptive abilities but also improves the convergence speed and the stability of the learning process of HDP. Besides, the convergence proof of the designed control scheme is provided. The SUP-HDP control scheme is implemented utilizing neural networks. Finally, we validate the effectiveness of the SUP-HDP control method through a benchmark simulation platform for the WWTP. Compared with other control methods, SUP-HDP has better control performance.

A Deep Learning-Based Data Management Scheme for Intelligent Control of Wastewater Treatment Processes Under Resource-Constrained IoT Systems

A Deep Learning-Based Data Management Scheme for Intelligent Control of Wastewater Treatment Processes Under Resource-Constrained IoT Systems

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review

As the pursuit of “carbon neutrality” gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.

Self-Healing Control for Wastewater Treatment Process Based on Variable-Gain State Observer

This paper proposes a variable-gain state observer-based sliding mode self-healing control (VSO-based SMSHC) method for a wastewater treatment process (WWTP) with changeable external disturbances and sensor failures. To reconstruct the disturbances and unmeasured system states, a novel VSO is developed, in which the VSO gains are adjusted by following an automatic variable-gain mechanism for adapting to the variation of external disturbances. An adaptive compensation coefficient of failure factor is designed to counteract the failure effects on observation and tracking performance. By utilizing the cubic absolute-value Lyapunov stability criterion, it is shown that system stability and tracking performance of WWTP are guaranteed. Experimental studies are carried out on a standardized platform of WWTP, and the results on dissolved oxygen and nitrate nitrogen regulation indicate that the proposed control strategy can ensure excellent control performance and reduce both failure and disturbance impacts.

A Dimensionality-Reducible Operational Optimal Control for Wastewater Treatment Process.

Operational optimal control (OOC) is an essential component of wastewater treatment process (WWTP). The control variables usually are high-dimensional, nonlinear, and strongly coupled, which can easily fail traditional optimization control methods. Mathematically, these operational variables usually are in the unknown low-dimensional space embedded in the high-dimensional space. Therefore, the OOC problem of WWTP can be resolved as an optimization challenge involving low-dimensional space, and the unknown low-dimensional space is presented in the form of a set of controlled variables in a high-dimensional space, which is normal in real-world industries. Here, a dimension-reducible data-driven optimization control framework for WWTP is proposed. Considering the difficulty in elucidating the whole space of set points, a neural network is designed to approximate the constraint relationship between control variables. The search process is based on optimization methods in low-dimensional space embedded into Euclidean spaces. Furthermore, the convergence of the process is ensured via mathematical analysis. Finally, the experimental simulation of wastewater treatment revealed that this approach is effective for an optimal solution in control systems.

Dynamic Optimal Control for Wastewater Treatment Process Under Multiple Operating Conditions

Wastewater treatment process (WWTP) is operated with multiple conditions. Accurately identify these conditions and satisfy the different requirements are the keys to guarantee the optimal operation of WWTP. To solve this problem, a dynamic optimal control (DOC) strategy is designed in this paper. First, with the aid of the predicting models by adaptive fuzzy neural network for effluent nitrate and total nitrogen, different operating conditions can be identified. Corresponding to each condition, changing number of operating objectives are formulated to match the different requirements. Second, due to the changeable operating objectives can cause the expanded/contracted dimension of Pareto-optimal set, a dynamic multi-objective particle swarm optimization algorithm is developed. In this algorithm, self-adjusting mechanism of population size and global optimal solution are designed to derive the optimal solutions of control variables. Third, fuzzy controllers, matched with the different conditions, are designed to trace these optimal solutions. Finally, the effectiveness of DOC strategy is tested on a real WWTP. The results demonstrate that this proposed DOC strategy enables to achieve promising operating performance. <i>Note to Practitioners</i>&#x2014;This article aims to develop an optimal control strategy to match the multiple conditions and improve the operating performance. To achieve this goal, a dynamic optimal control (DOC) strategy, with the consideration of different operating conditions, is designed. Two key points are contained, the identification of multiple operating conditions and the satisfaction of different requirements. Due to each condition confronts with different requirements, it is important to accurately identify the conditions and construct the corresponding operating objectives. Based on the obtained operating data, changeable operating objectives are established to describe the different conditions. In addition, to satisfy the operating requirements, it is necessary to optimize the changeable objectives, but the changes in the number of objectives can cause the expanded/ contracted of Pareto-optimal set. A dynamic multi-objective particle swarm optimization algorithm, with self-adjusting mechanism of population size and global optimal solution, is designed to deal with this kind of optimization problem. The effectiveness and applicability of the proposed DOC strategy are evaluated on a pilot platform of a real WWTP, showing the improvement on the operating performance. These advantages can be valuable for transplanting this strategy to other real wastewater treatment plants to realize the optimal operation.

Residual-triggered threshold decision and performance self-healing control for wastewater treatment process

The delicate control of effluent quality in wastewater engineering becomes imperative for fulfilling its role in protecting the ecological environment and promoting the water recycling. In this paper, an adaptive multivariable self-healing control strategy for the wastewater treatment process (WWTP) operating under complicated environments and facing abnormal conditions. The control goal is to meet the strict effluent standards of the WWTP and to optimize the abnormal conditions by analyzing the process operating status in the event of sudden sensor failures. Therein, a new residual-triggered threshold decision method is designed and integrated into a dynamical detection mechanism to identify such sensor failures. After locating the failures, a multivariable self-healing control scheme incorporating failure compensation information is activated to guarantee the process output tracking and improve the self-recovery capability of faulty performance in the WWTP. Extensive experiments are carried out on a standardized wastewater treatment platform, and the regulation results on the key component concentrations indicate that the proposed control scheme can ensure excellent control performance and reduce the impact of sensor failures.

Robust Optimal Control for Wastewater Treatment Process With Uncertain Time Delays

To achieve the excellent operational performance of the wastewater treatment process, optimal control has been considered a reliable method. However, there is a time-delay response of the operation performances to the process variables, leading to uncertainties of operational optimal objectives. It is difficult to obtain the optimal set-points due to the uncertain operational optimal objectives. Therefore, a kernel-density-estimation-based robust optimal control (KDE-ROC) method is proposed. First, a data-driven prediction strategy is developed to construct the uncertain operational optimal objectives. Based on the time-delay intervals, the uncertainties between process variables and operational optimal objectives are expressed. Second, a kernel-density-estimation-based robust optimization algorithm is designed to solve the uncertain operational optimal objectives. Then, the optimal set-points of process variables are obtained depending on the robustness index to reduce the influence of uncertainties. Third, an adaptive neural network controller is developed to track the optimal set-points of process variables. Finally, the proposed KDE-ROC is applied in benchmark simulation model No.1. In the experimental results, the optimal control performance of KDE-ROC is compared with some effective optimal control strategies to demonstrate its effectiveness.

Modeling and control of wastewater treatment process with time delay based on event-triggered recursive least squares

Accurate control of dissolved oxygen (DO) concentration is the key to ensuring the quality of effluent from the wastewater treatment process (WWTP). However, the delay disturbance caused by the hydraulic retention time (HRT) hinders the accurate control of DO concentration. To solve this problem, an event-triggered recursive least squares-based sliding mode control (ETRLS-SMC) is proposed to overcome the obstacles caused by the delay disturbance for accurate control of DO concentration. First, a differential equation with a delay disturbance term is established to describe the dynamic process of the system. Then, the delay disturbance is explicitly depicted to lay the foundation for the controller design considering delay disturbance variables. Second, an RLS identification algorithm is used to online obtain the model parameters of the system. In particular, an event-triggered mechanism is designed to reduce the number of model updates with guaranteed accuracy. Third, combined with the ETRLS identification algorithm, an SMC method is introduced to control the DO concentration with delay disturbance variables. Then, the control law is computed to achieve stable control of the system. Finally, the performance of ETRLS-SMC is verified on the benchmark simulation model 1 (BSM1). The results show that the proposed method can reduce ISE by 0.0014 and 0.0128 when the threshold is 2.5% in two cases, including the reference value being fixed and changed, compared with the case without considering the delay disturbance. The results of trigger mechanism test further demonstrate the effectiveness of event-triggered mechanism.

Multi-objective pity beetle algorithm based optimal control of wastewater treatment process

Water is a critically scarce resource for industrial production and social life. The discharge of untreated wastewater into natural waters can pose a serious risk to human health. Most of urban wastewater treatment plants use biochemical methods, the most common of which is the biological reaction through activated sludge to degrade pollutants. Considering the public environmental protection and socio-economic needs, this paper establishes a wastewater treatment process (WWTP) optimization model considering the trade-off between effluent quality (EQ) and energy consumption (EC), and designs an dynamic optimal control framework based on a novel multi-objective evolutionary algorithm (MOEA) to track and control the key variables in the WWTP. The numerical experiments of multi-objective test functions show that the proposed MOEA has good convergence and distributivity. Simulation results based on the BSM1 platform show that the constructed framework can accurately adjust the set-points of controllers in time to improve the performance, which has broad application prospects in practical applications.

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

Data-based robust model predictive control for wastewater treatment process

Data-driven methods are widely used in wastewater treatment processes (WWTPs). However, disturbances are not fully considered during the control process, which may affect the stable operation of WWTPs. To address this issue, a data-driven robust model predictive control (DRMPC) method is proposed for WWTPs with disturbances. First, an interval type-2 fuzzy neural network (IT2FNN), which has high robust performance to disturbances, is used to identify dynamic behavior of WWTPs. Second, based on IT2FNN, a robust stabilizing controller is developed to weaken the influence of disturbances on control performance and stability. Third, the proposed controller ensures constraint satisfaction and input-to-state stability (ISS). Finally, the feasibility of DRMPC strategy is verified on the benchmark simulation model No. 1 (BSM1). The experimental studies indicate that the proposed DRMPC is capable of running stably and tracking accurately.

Multi-objective integrated optimal control for a wastewater treatment process

Multi-objective optimal control is often used to deal with conflicting objectives of the wastewater treatment process (WWTP) to ensure its operational safety and reliability. However, the existing multi-objective optimal control methods mainly analyze the optimization process and control process independently, which may make the set-points fluctuate largely and hard to track, thereby degrading the control and operation performance of WWTP. To solve this problem, a multi-objective integrated optimal control (MIOC) strategy is proposed for WWTP in this paper. The integrated optimal control structure is established by the multi-objective model predictive control framework to achieve the coordinated optimization and control solution. In the integrated optimal control structure, first, a comprehensive cost function with operation indices and tracking errors is designed to deal with the conflicting objectives. Second, a multi-gradient algorithm is employed to evaluate the dynamic set-points and control laws. Third, the stability analysis of MIOC is given to prove the proposed method can stabilize the system. Meanwhile, the proposed MIOC method is tested in the Benchmark Simulation Model No. 1 to demonstrate that it can achieve suitable operation performance with satisfactory control accuracy.

Automating process design by coupling genetic algorithms with commercial simulators: a case study for hybrid MABR processes.

The development of commercial software and simulators has progressed to assist engineers to optimize design, operation, and control of wastewater treatment processes. Commonly, manual trial-and-error approaches combined with engineering experience or exhaustive searches are used to find candidate solutions. These approaches are becoming less favorable because of the increasingly elaborate process models, especially for new and innovative processes whose process knowledge is not fully established. This study coupled genetic algorithms (GAs), a subfield of artificial intelligence (AI), with a commercial simulator (SUMO) to automatically complete a design task. The design objective was the upgrade of a conventional Modified Ludzack-Ettinger (MLE) process to a hybrid membrane aerated biofilm reactor (hybrid MABR). Results demonstrated that GAs can (1) accurately estimate five influent wastewater fractions using eleven typical measurements - 3 out of 5 estimated fractions were nearly the same and the other two were within 7% relative errors and (2) propose reasonable designs for the hybrid MABR process that reduce footprint by 17%, aeration by 57%, and pumping by 57% with significantly improved effluent nitrogen quality (TN<3 mg-N/L). This study demonstrated that tools from AI promote efficiency in wastewater treatment process design, optimization and control by searching candidate solutions both smartly and automatically in replacement of manual trial-and-error methods. The methodology in this study contributes to accumulating process knowledge, understanding trade-offs between decisions, and finally accelerates the learning pace for new processes.

Policy Gradient Adaptive Critic Design With Dynamic Prioritized Experience Replay for Wastewater Treatment Process Control

With the industrialization of modern society, the pollution of water resources becomes more and more serious. Although purifying urban sewage through the wastewater treatment plants eases the burden of fragile ecosystems, the nonlinearities and uncertainties of biochemical reactions are difficult to address. In this article, a dynamic prioritized policy gradient adaptive dynamic programming (ADP) method is developed to solve the optimal control problem of nonaffine nonlinear discrete-time systems, along with convergence analysis of the algorithm. To the best of our knowledge, it is indispensable to conduct system modeling during the previous ADP research on wastewater treatment process control. By introducing the dynamic prioritized replay buffer and neural networks, the proposed ADP controller can track the setpoints of the wastewater treatment plant and alleviate the effects of disturbance without system modeling. The test results verify that the devised control method outperforms the proportional-integral-derivative strategy with less oscillation when unknown interference occurred.

Machine learning classification algorithms for inadequate wastewater treatment risk mitigation

Continuous monitoring of wastewater treatment processes is key to mitigate the risk of inadequately treated wastewater on the environment and public health. However, effective control of wastewater treatment processes is challenging because of the numerous relevant variables and their complex physio-chemical-biological interdependence. Most published related studies focused on correlating the effluent concentration of chemical oxygen demand and/or suspended solids using only a limited number of wastewater influent variables. In addition, recent machine learning- (ML) based studies in wastewater treatment systems considered some individual classification algorithms rather than providing a comparison between different algorithm performances. In the current study, different algorithms were developed to categorize a range of wastewater treatment effluent characteristics based on multiple influent variables. To demonstrate their application, 23 ML classification algorithms were deployed on a wastewater treatment reactor-generated dataset and their performances were evaluated considering two different group of metrics related to the removal efficiency and effluent quality. The analysis results showed that, among all considered algorithms, the ensemble bagged trees algorithm had the most superior performance in terms of its overall classification accuracy. An interpretability analysis was further performed on the treatment process variables to detect the correlation between the input and output variables and to assess variable importance. In practice, the developed algorithms can facilitate optimal operation and effective management of wastewater treatment plants. ML algorithms also present efficient tools for rapidly classifying the effluent characteristics in lieu of typical sampling and laboratory analysis processes.

Sliding mode control of wastewater treatment process with activated sludge under extreme weather events

The present climate change engender an increase of extreme weather events such as intensive rainfall, flash floods, droughts, prolonged periods of deviations from established average daily temperatures. These extreme weather events negative impact on sustainability of wastewater treatment process with activated sludge. The paper aim is to propose a sliding mode control of wastewater treatment process with activated sludge under extreme weather events is proposed. The principles of the binary control theory are used. The control design is carried out with direct use of the nonlinear model, described by three ordinary differential equations with uncertain parameters. The parameters are varied in the known intervals. These uncertainties are predetermined by the nature and intensity of the extreme weather events. The model of the sliding mode control is developed with respect to an auxiliary input variable in order to obtain the smooth signal of the dilution rate, which is need in the biodegradation processes. The good robustness of closed loop system with the designed sliding mode control in regard to various disturbances is proved through simulation investigations in MATLAB using Simulink.

Design of Syncretic Fuzzy-Neural Control for WWTP

Owing to the possible existence of system failures and packet dropouts in the wastewater treatment process (WWTP), it is difficult to obtain sufficient data, which will result in data shortage. Therefore, it is a challenge to design an effective data-driven controller with the above data shortage issue for WWTP. To solve this problem, a syncretic fuzzy-neural controller (SFNC) was developed and analyzed in this article. First, the knowledge obtained from the operation conditions was made full use by a knowledge reconstruction mechanism to construct the initial condition of SFNC. Then, the proposed SFNC was able to obtain the accurate parameters and compact structure in the initialization phase. Second, a syncretic-form strategy (SFS) was designed to syncretize the knowledge from the fuzzy rules and the data from the operation process to optimize the structure of SFNC. Then, the adaptability of SFNC can be improved to achieve good control performance in the presence of insufficient data. Third, the stability of the developed SFNC was proved by using Lyapunov stability theorem. Then, the stability of SFNC was given to guarantee its successful application. Finally, the proposed controller was tested on the Benchmark Simulation Model No.1 to confirm its effectiveness. The results demonstrated that the proposed SFNC can achieve superior control performance than some other existing controllers.