Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs

Han, Honggui; Liu, Zheng; Qiao, Junfei

doi:10.1007/s40815-019-00644-8

Cited by 19 publications

(1 citation statement)

References 44 publications

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“…An adaptive fuzzy control strategy for dissolved oxygen concentration was used to control of an activated sludge process Liu et al [7]. In the paper [8] an adaptive fuzzy neural network-based model predictive control (AFNN-MPC) is proposed for the control problem of DO concentration. Piotrowski proposed a supervisory heuristic fuzzy control system applied to a Sequencing Batch Reactor (SBR) in the Wastewater Treatment Plant (WWTP) [9].…”

Section: Introductionmentioning

confidence: 99%

Auto-Control Technique Using Gradient Method Based on Radial Basis Function Neural Networks to Control of an Activated Sludge Process of Wastewater Treatment

Lemita¹,

Boulahbel²,

Kahla³

et al. 2020

JESA

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Dissolved oxygen (DO) concentration is a key variable in the activated sludge wastewater treatment processes. In this paper, an auto control strategy based on Euler method and gradient method with radial basis function (RBF) neural networks (NNs) is proposed to solve the DO concentration control problem in an activated sludge process of wastewater treatment. The control purpose is to maintain the dissolved oxygen concentration in the aerated tank for having the substrate concentration within the standard limits established by legislation of wastewater treatment. For that reason, a new proposed control strategy based on gradient descent method and RBF neural network has been used. Compared with RBF neural network PI control, the obtained results show the effectiveness in terms of both transient and steady performances of proposed control method for dissolved oxygen control in the activated sludge wastewater treatment processes.

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Section: Introductionmentioning

confidence: 99%