ABSTRACT
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.
Subject(s)
Artificial Intelligence , Bioreactors , Waste Disposal, Fluid/methods , Algorithms , Internet , Nitrogen , Pattern Recognition, Automated , Reproducibility of Results , Time Factors , Waste Disposal, Fluid/instrumentation , Water Pollutants, Chemical/chemistryABSTRACT
Early ultrasonographic antenatal diagnosis permits to perform intrauterine treatment to improve the prognosis of the fetus. These interventions are, however, invasive and associated with risks. In 2004, the results of the randomized trial comparing treatment options in the case of twin-to-twin transfusion syndrome were published. We summarize in this article the current knowledge on invasive fetal therapy.
