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

doi:10.3390/app8020261

	Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm
	Du, Xianjun 1,2,4,5; Wang, Junlu 1,2,4; Jegatheesan, Veeriah 5; Shi, Guohua 3
刊名	APPLIED SCIENCES-BASEL
	2018-02
卷号	8 期号:2
关键词	dissolved oxygen concentration radial basis function (RBF) neural network adaptive PID dynamic simulation
ISSN号	2076-3417
DOI	10.3390/app8020261
英文摘要	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.
资助项目	Excellent Young Teacher Project of Lanzhou University of Technology[Q201408]
WOS研究方向	Chemistry ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000427510300113
状态	已发表
内容类型	期刊论文
源URL	[http://119.78.100.223/handle/2XXMBERH/32813]
专题	电气工程与信息工程学院
通讯作者	Jegatheesan, Veeriah
作者单位	1.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Gansu, Peoples R China 2.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Edu, Lanzhou 730050, Gansu, Peoples R China 3.North China Elect Power Univ, Dept Energy & Power Engn, Baoding 071003, Peoples R China 4.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China 5.Royal Melbourne Inst Technol RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia
推荐引用方式 GB/T 7714	Du, Xianjun,Wang, Junlu,Jegatheesan, Veeriah,et al. Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm[J]. APPLIED SCIENCES-BASEL,2018,8(2).
APA	Du, Xianjun,Wang, Junlu,Jegatheesan, Veeriah,&Shi, Guohua.(2018).Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm.APPLIED SCIENCES-BASEL,8(2).
MLA	Du, Xianjun,et al."Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm".APPLIED SCIENCES-BASEL 8.2(2018).