Intelligent monitoring system for long-term control of Sequencing Batch Reactors.
This paper discusses the application of artificial intelligence (AI) concepts to the monitoring of a lab-scale Sequencing Batch Reactor (SBR) treating nitrogen-rich wastewater (sanitary landfill leachate). The paper describes the implementation of a fuzzy inferential system to identify the correct switching sequence of the process and discusses the results obtained with six months of uninterrupted operation, during which the process conditions varied widely. The monitoring system proved capable of adjusting the process operation, in terms of phase length and external COD addition, to the varying environmental and loading conditions, with a percentage of correct phase recognition in excess of 95%. In addition, the monitoring system could be remotely operated through the internet via TCP/IP protocol.
Marsili-Libelli
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Department of Systems and Computers, University of Florence, Via S Marta 3, I-50139, Florence, Italy. marsili@dsi.unifi.it
Spagni
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Susini
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D000465:Algorithms; D001185:Artificial Intelligence; D019149:Bioreactors; D020407:Internet; D009584:Nitrogen; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D013997:Time Factors; D014865:Waste Disposal, Fluid / Q000295:instrumentation / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000737:chemistry
D014874:Water Pollutants, Chemical; D009584:Nitrogen