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Appl. Sci. 2018, 8(2), 261; doi:10.3390/app8020261

Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm

1
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2
School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia
3
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
4
National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
5
Department of Energy and Power Engineering, North China Electric Power University, Baoding 071003, China
*
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 25 January 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
(This article belongs to the Special Issue Wastewater Treatment and Reuse Technologies)
View Full-Text   |   Download PDF [2711 KB, uploaded 9 February 2018]   |  

Abstract

The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by using a Proportional-Integral-Derivative (PID) controller. Since the traditional PID parameter adjustment is not adaptive, the unknown disturbances make it difficult to adjust the DO concentration rapidly and precisely to maintain at a desired level. A Radial Basis Function (RBF) neural network (NN)-based adaptive PID (RBFNNPID) algorithm is proposed and simulated in this paper for better control of DO in an activated sludge process-based wastewater treatment. The powerful learning and adaptive ability of the RBF neural network makes the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online can be made by RBFNNPID algorithm to improve the performance of the controller. The RBFNNPID algorithm is based on the gradient descent method. Simulation results comparing the performance of traditional PID and RBFNNPID in maintaining the DO concentration show that the RBFNNPID control algorithm can achieve better control performances. The RBFNNPID control algorithm has good tracking, anti-disturbance and strong robustness performances. View Full-Text
Keywords: dissolved oxygen concentration; radial basis function (RBF) neural network; adaptive PID; dynamic simulation dissolved oxygen concentration; radial basis function (RBF) neural network; adaptive PID; dynamic simulation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Du, X.; Wang, J.; Jegatheesan, V.; Shi, G. Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm. Appl. Sci. 2018, 8, 261.

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