Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Shen, Wenhao; Chen, Xiaoquan; Corriou, Jean‐Pierre

doi:10.1016/j.compchemeng.2008.01.009

Cited by 104 publications

(48 citation statements)

References 10 publications

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“…When implementing an advanced control system, such as MPC, on a process with regulatory PID loops, one is faced with the following two options [18]: the first option is called direct MPC control, which means breaking the regulatory loops and have the MPC controller to manipulate the process directly. In recent years, direct MPC control has been applied to the BSM1 [19,20]. The second option is called cascade MPC-PID control, which means leaving the loops and have the MPC controller to manipulate the set-points of these loops.…”

Section: Proposed Cascade Mpc-pid Control Strategymentioning

confidence: 99%

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

Liu

2011

Korean J. Chem. Eng.

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A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.

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Section: Proposed Cascade Mpc-pid Control Strategymentioning

confidence: 99%

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

Liu

2011

Korean J. Chem. Eng.

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“…Similarly, in the context of effluent/waste management, efforts have been directed towards modeling and estimation [129,130], monitoring [131,132], fault diagnosis [133,134], hybrid PI control [135], cascade control [136], data-driven control [137,138], fuzzy control [139], adaptive and robust control [140], nonlinear model-based feedback control [141], optimal control [142], linear MPC [143,144] and nonlinear MPC [145,146]. A hierarchical control strategy, motivated by the presence of multi-time-scale dynamics has also been implemented for a climate control [147] system and an integrated wastewater system [148].…”

Section: Emission and Effluent Managementmentioning

confidence: 99%

Sustainability and process control: A survey and perspective

Daoutidis

Zachar

Jogwar

2016

Journal of Process Control

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“…It is difficult to control wastewater treatment process (WWTP) because of the large perturbations in influent flow rate, pollutant load and the different physical and biological phenomena at play. In addition, the reactors exhibit common features of industrial systems, such as nonlinear dynamics and coupling effects among the variables [1][2]. Many control strategies have been proposed to solve such problems in the literature, Traore, et al [3] designed a fuzzy proportional-integral-derivative (PID) controller to control the dissolved oxygen (DO) concentration very well by adjusting the PID parameters.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Control for Wastewater Treatment Process

Qiao

Huang

Han

2012

Advances in Neural Networks – ISNN 2012

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Abstract. Wastewater treatment process (WWTP) is difficult to be controlled because of the complex dynamic behavior. In this paper, a multi-variable control system based on recurrent neural network (RNN) is proposed for controlling the dissolved oxygen (DO) concentration, nitrate nitrogen (S NO ) concentration and mixed liquor suspended solids (MLSS) concentration in a WWTP. The proposed RNN can be self-adaptive to achieve control accuracy, hence the RNN-based controller is applied to the Benchmark Simulation Model No.1 (BSM1) WWTP to maintain the DO, S NO and MLSS concentrations in the expected value. The simulation results show that the proposed controller provides process control effectively. The performance, compared with PID and BP neural network, indicates that this control strategy yields the most accurate for DO, S NO , and MLSS concentrations and has lower integral of the absolute error (IAE), integral of the square error (ISE) and mean square error (MSE).

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Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Cited by 104 publications

References 10 publications

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

Sustainability and process control: A survey and perspective

Recurrent Neural Network-Based Control for Wastewater Treatment Process

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