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, Xianjun1,2,3,4 ; Wang, Junlu 1,3,4; Jegatheesan, Veeriah 2; Shi, Guohua 5
	2018-02
发表期刊	APPLIED SCIENCES-BASEL
ISSN	2076-3417
卷号	8 期号:2
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.
关键词	dissolved oxygen concentration radial basis function (RBF) neural network adaptive PID dynamic simulation
DOI	10.3390/app8020261
收录类别	SCI ; SCIE
语种	英语
资助项目	Excellent Young Teacher Project of Lanzhou University of Technology[Q201408]
WOS研究方向	Chemistry ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000427510300113
出版者	MDPI
引用统计	被引频次：58[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/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, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China; 2.Royal Melbourne Inst Technol RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia; 3.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Gansu, Peoples R China; 4.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Edu, Lanzhou 730050, Gansu, Peoples R China; 5.North China Elect Power Univ, Dept Energy & Power Engn, Baoding 071003, Peoples R China
第一作者单位	电气工程与信息工程学院; 兰州理工大学
第一作者的第一单位	电气工程与信息工程学院
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).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Du-2018-Dissolved Ox（2711KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载