Search

Abstract: Despite extensive research that has been done on sludge bulking, it remains a widespread problem in the operation of activated sludge processes, which brings severe economic and environmental consequences. In this study, a self-organizing radial basis function (SORBF) neural network method is utilized to predict the evolution of the sludge volume index (SVI). The hidden nodes in the SORBF neural network can be grown or pruned based on the node activity (NA) and mutual information (MI) to achieve the appropriate network complexity and maintain overall computational efficiency. The growing and pruning criteria of the SORBF can vary its structure dynamically with the objective to enhance its performance. Moreover, the input–output selection to calculate the SVI values is also discussed. The variables with key relations to the sludge bulking are used as the inputs for the SVI. Finally, the SORBF neural network is applied to the activated sludge wastewater treatment processes (WWTPs) for predicting the SVI, and then for predicting the sludge bulking. Experimental results show the excellent performance of the SORBF method. The performance comparison demonstrates the effectiveness of the proposed SORBF. [Copyright &y& Elsevier]

Abstract: The dissolved oxygen (DO) concentration in activated sludge wastewater treatment processes (WWTPs) is difficult to control because of the complex nonlinear behavior involved. In this paper, a self-organizing radial basis function (RBF) neural network model predictive control (SORBF-MPC) method is proposed for controlling the DO concentration in a WWTP. The proposed SORBF can vary its structure dynamically to maintain prediction accuracy. The hidden nodes in the RBF neural network can be added or removed on-line based on node activity and mutual information (MI) to achieve the appropriate network complexity and the necessary dynamism. Moreover, the convergence of the SORBF is analyzed in both the dynamic process phase and the phase following the modification of the structure. Finally, the SORBF-MPC is applied to the Benchmark Simulation Model 1 (BSM1) WWTP to maintain the DO concentration. The results show that SORBF-MPC effectively provides process control. The performance comparison also indicates that the proposed model''s predictive control strategy yields the most accurate for DO concentration, better effluent qualities, and lower average aeration energy (AE) consumption. [Copyright &y& Elsevier]

This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems. [ABSTRACT FROM AUTHOR]

Sludge bulking is very common in wastewater treatment process (WWTP), which will degrade the operation performance or even destroy the process. In order to diagnose sludge bulking accurately, a data-knowledge-driven diagnosis (DKD) method is proposed to identify the occurrence and cause variable in this paper. This proposed DKD method contains the following advantages. First, a data-driven detection model, using a recursive kernel principal component analysis (RKPCA) algorithm, is designed to capture the intrinsic nonlinear and time-varying characteristic of sludge bulking. Then, the occurrence of sludge bulking can be detected with high accuracy. Second, a DKD model, based on the Bayesian network (BN), is developed to extract the causality among process variables to identify the root cause variables of sludge bulking. Then, the root cause variables of sludge bulking can be diagnosed to improve the operation performance of WWTP. Finally, the proposed DKD method was tested on the measured data from a real WWTP. Experimental results confirmed the effectiveness of the proposed DKD method. • Detect the occurrence of sludge bulking from the water qualities by using RKPCA detection model. • Diagnose the root cause variable of sludge bulking via the BN. • This DKD method has been successfully used in a real wastewater treatment plant. [ABSTRACT FROM AUTHOR]

It is challenging to introduce an optimization strategy for enhancing the operational performance of wastewater treatment process (WWTP) on account of its multi-task characteristics. To cope with this problem, an adaptive multi-task optimization (AMTO) strategy is proposed to realize the optimal operation of WWTP. First, a multi-task optimization (MTO) framework is developed to depict the multi-task characteristics of WWTP. Then, it is conducive to achieve the optimization of nitrogen removal and phosphorus removal processes simultaneously. Second, a data-driven MTO model, based on the relevant process data, is established to describe the characteristics of nitrogen removal task and phosphorus removal task. Then, a MTO problem is modeled for WWTP. Third, a multi-task particle swarm optimization algorithm, based on the adaptive knowledge transfer method, is developed to cope with the above MTO problem. Then, the optimal set-points can be obtained in WWTP. Finally, the effectiveness of the proposed AMTO strategy is verified by comparing with other optimization strategies. The results demonstrate that the proposed AMTO strategy can achieve multiple tasks optimization in parallel and the optimal operation of WWTP. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Identify dynamic behavior of WWTPs by using IT2FNN. • Reduce the influence of disturbances on control performance and stability via the DRMPC algorithm. • The proposed controller ensures constraint satisfaction and input-state stable (ISS). • The feasibility of DRMPC strategy is verified on the benchmark simulation model No. 1 (BSM1). [ABSTRACT FROM AUTHOR]

Highlights: [•] NMMPC has two loops: the first loop deals with the control of DO and the second one is taking care of the nitrate. [•] The multivariate analysis of the process behavior can be obtained to avoid undesired interactions. [•] The errors between the set-point values and the control output remain within the range of ±0.05mg/L (±2.5%) – . [•] The errors of the set-point values and the control output can be kept within the range of ±5% – . [ABSTRACT FROM AUTHOR]

Different time scales are inevitable for the controlled variables of wastewater treatment process (WWTP), which may degrade the operation performance or even destroy the stability of the closed-loop system. In this paper, a hierarchical nonlinear model predictive control (HNMPC) strategy is developed to deal with the different time scales of controlled variables to improve the operation performance of WWTP. The main merits of HNMPC are three-fold. First, a hierarchical control structure is developed to obtain reasonable control laws. Then, the controlled variables with different time scales can be tracked by different level controllers. Second, a gradient method is developed to solve the hierarchical optimization problem of HNMPC to reduce the computational cost. Then, the fast response of HNMPC can be achieved to improve the operation performance. Third, the stability of HNMPC is proved in theory. Then, the corresponding stability conditions are given to guide the practical application. Finally, the testing results on the benchmark simulation model verify that the proposed HNMPC can achieve suitable operation performance in terms of control accuracy. • In this work, a hierarchical nonlinear model predictive control (HNMPC) strategy is developed to deal with the different time scales of control variables to improve the operation performance of wastewater treatment process. • Firstly, a hierarchical control structure is developed to obtain reasonable control laws. Then, the control variables with different time scales can be tracked by different level controllers. • Secondly, a gradient method is developed to solve the hierarchical optimization problem of HNMPC to reduce the computational cost. Then, the fast response of HNMPC can be achieved to improve the operation performance. • Thirdly, the stability of HNMPC is proved in theory. Then, the corresponding stability conditions are given to guide the practical application. [ABSTRACT FROM AUTHOR]

An optimal control method based on continuous-time continuous-state Hopfield neural network (CTCSHNN) is proposed for time-varying multivariable systems. The equivalence is built theoretically between receding-horizon linear quadratic (LQ) performance index and energy function of CTCSHNN, and the CTCSHNN is constructed to solve the above LQ optimization control problems. Moreover, the rolling optimization strategy is adopted to form closed-loop control structure that includes CTCSHNN so, the dynamic infinite-horizon optimization control is realized for multivariable time-varying systems. As an example, a second order time-varying system is simulated. Simulation results show the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Fault detection is important in the operation of wastewater treatment process (WWTP). In this paper, to ensure the process safety and effluent qualities, an intelligent fault detection (IFD) method, based on self-organizing type-2 fuzzy-neural-network (SOT2FNN) and intelligent identification method, was developed to detect and identify different types of sludge bulking. The main advantages of IFD are as follows. First, a data-driven framework, based on a data-driven model and an intelligent identification algorithm, was developed to facilitate the fault diagnosis. Second, a SOT2FNN, based on the intensity of information transmission algorithm and adaptive second-order algorithm, was designed to predict the sludge volume index (SVI) with high accuracy to provide necessary information for process monitoring. Third, an intelligent identification method, using the target-related identification algorithm (TRIA), was proposed to extract the correlation information to identify the types of sludge bulking. Finally, simulations and experimental examples were provided to confirm the effectiveness of the proposed IFD method. [ABSTRACT FROM AUTHOR]

Prediction of bulking sludge is a matter of growing importance around the world. In this study, to detect bulking sludge of wastewater treatment process (WWTP), an intelligent detection method, using a self-organizing recurrent radial basis function neural network (SORRBFNN) and a cause variables identification (CVI) algorithm, was developed to detect the fault points and the fault variables of bulking sludge. For this intelligent detection method, first, the structure and parameters of SORRBFNN were updated by an information-oriented algorithm (IOA) and an improved Levenberg-Marquardt (LM) algorithm to improve the prediction accuracy of the sludge volume index (SVI) from the water qualities. Second, the CVI algorithm was designed to allow a quick revealing of the cause variables of bulking sludge with high accuracy. And the intelligent detection method was tested on the measured data from a real WWTP. Experimental results confirmed the attractiveness and effectiveness of the proposed intelligent detection method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data-driven models (DDMs) are widely developed for monitoring wastewater treatment processes (WWTPs). However, DDMs, derived from invalid or noisy datasets, may fail to capture the dominant features of WWTPs and further result in inferior monitoring results. To solve this issue, a dynamic–static model is designed to monitor WWTPs. Primarily, the operational status of WWTPs is divided by a receding condition partition strategy, which can prevent the mutual interference of fluctuations among different operational conditions. As to the operational conditions without invalid datasets, the dynamic features of WWTPs are extracted by a dynamic intelligent model (DIM). DIM is built using an interval type-2 fuzzy neural network to mimic the dynamic relationship between process variables accurately. Meanwhile, the unreliable results caused by invalid datasets are compensated by a static statistical model. This model is developed to describe static properties using historical datasets, which are copied to execute the monitoring of WWTPs with a similarity discrimination mechanism. Finally, the proposed dynamic–static model is validated by several experiments in terms of total nitrogen removal under multiple operational conditions. The experimental results illustrate that the proposed model can ensure continuous and reliable monitoring of WWTPs. • In this work, a dynamic–static model is designed to monitor WWTPs. • The operational status of WWTPs is divided by a receding condition partition strategy. • As to the operational conditions without invalid datasets, the dynamic features of WWTPs are extracted by a dynamic intelligent model. • The unreliable results caused by invalid datasets are compensated by a static statistical model. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Proposed a multi-objective integrated optimal control strategy for wastewater treatment process. • Designed an integrated optimal control structure for a wastewater treatment process with the multi-objective model predictive control framework. • Developed a comprehensive cost function and a multi-gradient algorithm. • The integrated optimal control strategy can improve the control performance and operation performance of wastewater treatment process. [ABSTRACT FROM AUTHOR]

Efficient and accurate decision is essential to avoid sludge bulking and ensure the stable operation of wastewater treatment process (WWTP). However, due to the strong nonlinear characteristic, time-varying characteristic and changeable triggering causes of sludge bulking, the decision performance is not optimistic. In this paper, an intelligent decision method combining recursive kernel principal component analysis (RKPCA) and Bayesian network (BN) is proposed to accurately detect the occurrence, identify the root causes and severity level, and provide the restraining measures for sludge bulking. The proposed intelligent decision method consists of three main parts. First, a RKPCA model based on the sliding window mechanism is proposed to extract the nonlinear and time-varying characteristics of WWTP. Then, the proposed RKPCA can assist the decision method to detect the sludge bulking with high accuracy. Second, a mean reconstruction contribution of RKPCA (MRC-RKPCA) method is designed to preliminarily isolate all candidate faulty relevant variables of sludge bulking. Then, the MRC-RKPCA method is able to provide the relationship between the sludge bulking and the corresponding faulty variables for the decision method. Third, a decision model using BN is developed to determine the root causes and the severity of sludge bulking through reverse and forward inference, and provide corresponding restraining measures. Then, the proposed decision method can obtain accurate strategies to suppress the sludge bulking. Finally, the proposed intelligent decision method was applied for a real WWTP. The results demonstrate the proposed intelligent decision method can obtain better decision performance than some existing methods. [ABSTRACT FROM AUTHOR]