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Artificial neural network real‐time process control system for small utilities
Author(s) -
Zhang Qing J.,
Shariff Riyaz,
Smith Daniel W.,
Cudrak Audrey,
Stanley Stephen J.
Publication year - 2007
Publication title -
journal ‐ american water works association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 74
eISSN - 1551-8833
pISSN - 0003-150X
DOI - 10.1002/j.1551-8833.2007.tb07961.x
Subject(s) - artificial neural network , clarifier , supervisory control , control system , control engineering , process (computing) , process engineering , chemical plant , real time control system , engineering , control (management) , computer science , artificial intelligence , waste management , environmental engineering , electrical engineering , operating system
This article presents the final results from an Awwa Research Foundation project to implement an artificial intelligence real‐time automatic control system for a full‐scale dissolved‐air flotation (DAF) water treatment plant. The system developed through study used an artificial neural network to model the DAF clarifier and was integrated with the plant supervisory control and data acquisition system for real‐time control. The system was found to be robust and flexible, and it adapted well to fast‐changing raw water conditions and different coagulants. Once established, the system ran the plant continuously, as if an expert were in control, providing quality drinking water with minimum operator intervention. The system was also capable of optimizing chemical dosage and reducing the costs of production.