Neural networks based optimum coagulation dosing rate control applied to water purification system

Hua Bai; Lixin Gao; Guibai Li

Neural networks based optimum coagulation dosing rate control applied to water purification system

Hua Bai^*
, Lixin Gao
, Guibai Li

^*Corresponding author for this work

School of Architecture and Design

School of Mechatronics Engineering, Harbin Institute of Technology
Harbin Institute of Technology

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

By the analysis of coagulant dosing rate and its influencing factors, neural networks predicting theory was introduced into water treatment technology creatively, and a predicting model of coagulant dosing rate was established. Testing results indicate that this model is adaptive and its self-learning ability is strong. The accuracy of predicting results can be improved obviously by networks on-line self-learning. On-line predicting control of coagulant dosing rate can be achieved by the use of this model, and it presents an effective way for the realization of optimal coagulant dosing rates.

Original language	English
Pages	1432-1435
Number of pages	4
State	Published - 2002
Event	Proceedings of the 4th World Congress on Intelligent Control and Automation - Shanghai, China Duration: 10 Jun 2002 → 14 Jun 2002

Conference

Conference	Proceedings of the 4th World Congress on Intelligent Control and Automation
Country/Territory	China
City	Shanghai
Period	10/06/02 → 14/06/02

Keywords

Automation
Coagulation control
Neural network
Optimal coagulant rate
Predictive model
Self-learning
Water treatment

Cite this

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title = "Neural networks based optimum coagulation dosing rate control applied to water purification system",

abstract = "By the analysis of coagulant dosing rate and its influencing factors, neural networks predicting theory was introduced into water treatment technology creatively, and a predicting model of coagulant dosing rate was established. Testing results indicate that this model is adaptive and its self-learning ability is strong. The accuracy of predicting results can be improved obviously by networks on-line self-learning. On-line predicting control of coagulant dosing rate can be achieved by the use of this model, and it presents an effective way for the realization of optimal coagulant dosing rates.",

keywords = "Automation, Coagulation control, Neural network, Optimal coagulant rate, Predictive model, Self-learning, Water treatment",

author = "Hua Bai and Lixin Gao and Guibai Li",

year = "2002",

language = "英语",

pages = "1432--1435",

note = "Proceedings of the 4th World Congress on Intelligent Control and Automation ; Conference date: 10-06-2002 Through 14-06-2002",

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TY - CONF

T1 - Neural networks based optimum coagulation dosing rate control applied to water purification system

AU - Bai, Hua

AU - Gao, Lixin

AU - Li, Guibai

PY - 2002

Y1 - 2002

N2 - By the analysis of coagulant dosing rate and its influencing factors, neural networks predicting theory was introduced into water treatment technology creatively, and a predicting model of coagulant dosing rate was established. Testing results indicate that this model is adaptive and its self-learning ability is strong. The accuracy of predicting results can be improved obviously by networks on-line self-learning. On-line predicting control of coagulant dosing rate can be achieved by the use of this model, and it presents an effective way for the realization of optimal coagulant dosing rates.

AB - By the analysis of coagulant dosing rate and its influencing factors, neural networks predicting theory was introduced into water treatment technology creatively, and a predicting model of coagulant dosing rate was established. Testing results indicate that this model is adaptive and its self-learning ability is strong. The accuracy of predicting results can be improved obviously by networks on-line self-learning. On-line predicting control of coagulant dosing rate can be achieved by the use of this model, and it presents an effective way for the realization of optimal coagulant dosing rates.

KW - Automation

KW - Coagulation control

KW - Neural network

KW - Optimal coagulant rate

KW - Predictive model

KW - Self-learning

KW - Water treatment

UR - https://www.scopus.com/pages/publications/0036946976

M3 - 论文

AN - SCOPUS:0036946976

SP - 1432

EP - 1435

T2 - Proceedings of the 4th World Congress on Intelligent Control and Automation

Y2 - 10 June 2002 through 14 June 2002

ER -

Neural networks based optimum coagulation dosing rate control applied to water purification system

Abstract

Conference

Keywords

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Cite this