State estimation for large-scale wastewater treatment plants

Busch, Jan; Elixmann, David; Kühl, Peter; Gerkens, Carine; Schlöder, Johannes P.; Bock, Hans Georg; Marquardt, Wolfgang

doi:10.1016/j.watres.2013.04.007

Cited by 63 publications

(48 citation statements)

References 29 publications

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“…In order to check its observability, we linearize the nonlinear model at different points along typical state trajectories and check the observability of these linearized models. This approximation approach was also used in ref 13. The linear approximation of the process at x(t) can be described as the following form (assuming zero process and measurement noise without loss of generality):…”

Section: ■ Subsystem Configurationmentioning

confidence: 99%

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Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Zeng

Liu

Zou

et al. 2016

Ind. Eng. Chem. Res.

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A wastewater treatment plant is a large-scale nonlinear system including a series of biological reactors and a settler. In this work, we propose a distributed state estimation scheme for wastewater treatment processes in the context of extended Kalman filtering. Specifically, we consider a wastewater treatment process that includes a fivecompartment reactor and an ideal splitter. First, we present a method to design the sensor network for the process and then discuss how the process may be decomposed into subsystems for distributed state estimation. We present a detailed design of the distributed filters and a detailed distributed state estimation algorithm to coordinate the actions of the different filters. Without loss of generality, we consider the entire system as being decomposed into two subsystems. The proposed approach can be extended in a straightforward fashion to include more subsystems. The distributed scheme is compared with the corresponding centralized extended Kalman filtering scheme under different weather conditions. Simulation results show that the distributed scheme can give comparable estimation performance to the centralized scheme or even better performance than the centralized scheme. Also, the distributed estimation scheme is shown to have more stable performance under different noise conditions. ■ INTRODUCTIONWastewater treatment is an important step in water recycling, and it involves complex biological and physical phenomena. A wastewater treatment plant (WWTP) is typically a large-scale nonlinear system including a series of biological reactors and a settler. The effluent quality of a wastewater treatment plant is closely related to the sustainability of the environment and normally is regulated by environmental legislations. The significant variability of the influent flow and the composition of the flow to a WWTP poses significant challenges in the associated control system design.Different process control schemes have been reported for WWTPs. Proportional−integral (PI) control is one of the most commonly used strategies 1,2 because of its simplicity in design and implementation. Feedforward control has also been implemented together with PI control to improve the control performance. 3 While PI control can achieve the stability requirement in the operation, it cannot handle constraints and is not optimal. Model predictive control (MPC) is an online optimization-based approach and can take input and state constraints explicitly into account. 4 MPC as an optimal control method has also been widely used in the control of WWTPs. , an economic MPC algorithm was applied to a WWTP, and the economic MPC performance was compared with PI and regular MPC. It was shown that economic MPC is able to achieve improved effluent quality.While there are many results on the control system design for WWTPs, relatively less attention has been given to the state estimation of WWTPs. State estimation is a process of constructing system states based on output measurements and a system model. Sta...

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Section: ■ Subsystem Configurationmentioning

confidence: 99%

“…In this work, we use the Popov−Belevich−Hautus (PBH) rank test to check the observability of the WWTP. 13 The observability test is to check if the observability matrix O k (k = 1, ..., n) has a full column rank:…”

Section: ■ Subsystem Configurationmentioning

confidence: 99%

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Zeng

Liu

Zou

et al. 2016

Ind. Eng. Chem. Res.

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“…As a consequence of the need for online optimization, MHE requires more computational effort than Kalman filters. However, in process industries, this may be not a major concern due to the relatively slow process dynamics, although it has been questioned whether the improved state estimations justify the increased computational effort [113] . The additional computational burden associated with MHE motivates distributed MHE approaches for large-scale systems.…”

Section: Distributed State Estimationmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

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Abstract:In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

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“…To take into account this fact two formulations of the SEP are usually applied: in the first one involves adding slack/noise variables to each system dynamic and output equation and minimizing these variables while forcing the model outputs to be equal to the measured ones [7], [10], [11]. The second formulation is a direct minimization of the norm of the difference between the model generated outputs and the measured ones [8], [12], [13]. The former appears to be more appealing for theoretical purposes since it explicitly deals with the slack/noise variables.…”

Section: Optimal Control Problemmentioning

confidence: 99%

“…Unlike other classical observer and estimation techniques (e.g., Luenberger, Kalman filter) the MHE strategy can naturally handle system constraints and be applied to linear, nonlinear and hybrid systems. However, very few applications to water systems have been reported, with [12], [13] being the only ones known to the authors. In [13], the MHE strategy is used to estimate flows in a river system, much like in the present document, but based on a model leading to mid-scale smooth nonlinear optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Output-feedback model predictive control of sewer networks through moving horizon estimation

Joseph-Duran

Ocampo‐Martínez

Cembraño

2014

53rd IEEE Conference on Decision and Control

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Abstract-Based on a simplified control-oriented hybrid linear delayed model, model predictive control (MPC) of a sewer network designed to reduce pollution during heavy rain events is presented. The lack of measurements at many parts of the system to update the initial conditions of the optimal control problems (OCPs) leads to the need for estimation techniques. A simple modification of the OCP used in the MPC iterations allows to formulate a state estimation problem (SEP) to reconstruct the full system state from a few measurements. Results comparing the system performance under the MPC controller using full-state measurements and a moving horizon estimation (MHE) strategy solving a finite horizon SEP at each time instant are presented. Closed-loop simulations are performed by using a detailed physically-based model of the network as virtual reality.

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State estimation for large-scale wastewater treatment plants

Cited by 63 publications

References 29 publications

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Distributed control of chemical process networks

Output-feedback model predictive control of sewer networks through moving horizon estimation

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