Real-Time Water Treatment Process Control with Artificial Neural Networks

The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sediment...

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Bibliographic Details
Published in:Journal of environmental engineering (New York, N.Y.) N.Y.), 1999-02, Vol.125 (2), p.153-160
Main Authors: Zhang, Qing, Stanley, Stephen J
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
General purification processes
Pollution
TECHNICAL PAPERS
Wastewaters
Water treatment and pollution
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Summary:The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sedimentation processes. Presented is a review of influential control parameters and control requirements for these processes followed by the development of a feed forward neural network control scheme. A neural network process model was built based on nearly 2,000 sets of process control data. This model formed the major component of a software controller and was found to consistently predict the optimum alum and power activated carbon doses for different control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.
ISSN:0733-9372
1943-7870
DOI:10.1061/(ASCE)0733-9372(1999)125:2(153)