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Using Artificial Neural Networks for Modeling BNR Processes

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As part of a WERF-supported project (03-CTS-11), dynamic artificial neural network models for nitrification and denitrification processes were developed for the Stamford Water Pollution Control Authority's treatment plant in Stamford, CT. The models used only readily measured parameters from on-line instrumentation including effluent flow rate, primary effluent ammonia concentration, methanol, feed rate, and nitrate/nitrite concentrations at the feed and effluent of a post-denitrification process. The analyzers were accurate and required relatively minimal maintenance. The nitrification and denitrification models were accurate and can be used for predicting future performance and on-line process control.

Keywords: Artificial Neural Network; Automation; BNR; Instrumentation; Modeling

Document Type: Research Article


Publication date: January 1, 2010

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  • Proceedings of the Water Environment Federation is an archive of papers published in the proceedings of the annual Water Environment Federation® Technical Exhibition and Conference (WEFTEC® ) and specialty conferences held since the year 2000. These proceedings are not peer reviewed.

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