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 :
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