DocumentCode
381037
Title
Neural networks based optimum coagulation dosing rate control applied to water purification system
Author
Bai, Hua ; Gao, Lixin ; Li, Guibai
Author_Institution
Sch. of Mechatronics Eng., Harbin Inst. of Technol., China
Volume
2
fYear
2002
fDate
2002
Firstpage
1432
Abstract
By the analysis of coagulant dosing rate and its influencing factors, the neural network predicting theory was introduced into the water treatment technology creatively and a predicting model of coagulant dosing rate was established. The test results obtained indicate that this model is adaptive and its self-learning ability is effective. The prediction results´ accuracy can be markedly improved by the neural network´s online self-learning. The online predictive control of coagulant dosing rates can be achieved by using this model, and presents an effective way for the realization of optimal coagulant dosing rates.
Keywords
adaptive control; learning (artificial intelligence); neurocontrollers; predictive control; process control; self-adjusting systems; water treatment; adaptive control; coagulant dosing rate; neural networks; online predictive control; process control; self-learning; water purification system; water treatment; Automatic testing; Automation; Coagulation; Control systems; Electronic mail; Mechatronics; Neural networks; Optimal control; Predictive models; Purification;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN
0-7803-7268-9
Type
conf
DOI
10.1109/WCICA.2002.1020819
Filename
1020819
Link To Document