Control Of Wastewater Treatment Process Research Articles

Intelligent aeration amount prediction control for wastewater treatment process based on recurrent neural network

The use of machine learning in artificial intelligence to solve industrial problems has been a current trend. Predictions can be made by machine learning algorithms. This greatly improves the efficiency and also the accuracy. With the development of industrialization, the pollution of water resources is becoming more and more serious. How to treat wastewater more cost-effectively to meet the discharge standards has become one of the most urgent problems in the world. In the Anaerobic-Anoxic-Aerobic (AAO) process, the key to reach the standard of sewage treatment with low cost and high efficiency lies in the aeration link. In this study, multiple machine learning methods are used for modeling, and proposed a method employing the long short-term memory (LSTM) neural network model for regression prediction addresses the need for a more accurate prediction approach. By utilizing data from the preceding 100 days, this model replaces manual, experience-based prediction methods, thereby mitigating energy consumption. This model is optimized by training and testing with real wastewater treatment plant data and continuously adjusting the parameters through multiple sets of experiments. This can make the real recorded aeration amount and the actual aeration amount infinitely close. Through the experimental comparison, it is found that the performance of the LSTM neural network model is better, with about 14% higher accuracy than the benchmark model.

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

Adaptive type-2 fuzzy-neural switching control for wastewater treatment process under several operating conditions

The effluent ammonia and total nitrogen variation lead to multiple operating conditions in wastewater treatment process (WWTP), which challenges the control strategies based on a single control model. Therefore, it is essential to design suitable control strategies for WWTP to enhance the control performance under different operating conditions. In this paper, an adaptive type-2 fuzzy-neural switching control (AT2FSC) strategy is proposed to solve this problem. First, a soft-sensing model is developed based on fuzzy neural network and influent data in WWTP to estimate the effluent ammonia nitrogen and total nitrogen status. Then, the online operating conditions are determined by the critical values of effluent ammonia nitrogen and total nitrogen. Second, the corresponding fuzzy rules are devised for different operating conditions and a type 2 fuzzy switching model (AT2FSM) is constructed to capture the operating status changes of WWTP. Then, the proposed AT2FSC can design the switching control strategy according to AT2FSM to adapt to the current operating condition. Third, the Lyapunov theorem ensures the stability of the proposed AT2FSC method. Then, it facilitates the further application of AT2FSC in WWTP. Finally, the performance of AT2FSC under various operating conditions is validated on the benchmark simulation model No.1 (BSM1). Simulation results under four different operating conditions demonstrate that the proposed AT2FSC strategy can reduce the excess peak of effluent ammonia and total nitrogen while maintaining the operating performance.

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.

Robust Model Free Adaptive Predictive Control for Wastewater Treatment Process With Packet Dropouts.

External disturbances and packet dropouts will lead to poor control performance for the wastewater treatment process (WWTP). To address this issue, a robust model-free adaptive predictive control (RMFAPC) strategy with a packet dropout compensation mechanism (PDCM) is proposed for WWTP. First, a dynamic linearization approach (DLA), relying only on perturbed process data, is employed to approximate the system dynamics. Second, a predictive control strategy is introduced to avoid a short-sighted control decision, and an extended state observer (ESO) is used to attenuate the disturbance effectively. Furthermore, a PDCM strategy is designed to handle the packet dropout problem, and the stability of RMFAPC is rigorously analyzed. Finally, the correctness and effectiveness of RMFAPC are verified through extensive simulations. The simulation results indicate that RMFAPC can significantly reduce IAE by 0.0223 and 0.1976 in two scenarios, regardless of whether the expected value remains constant or varies. This comparison to MFAPC demonstrates the superior robustness of RMFAPC against disturbances. The ablation experiment on PDCM further confirms its capability in handling the packet dropout problem.

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.