Artificial Neural Network as a Virtual Sensor of Nitrate Nitrogen (V) Concentration in an Activated Sludge Reactor

• Autorzy: Płonka Lesław

Identyfikatory

DOI

10.2478/ceer-2020-0059

• Języki publikacji: EN

Abstrakty

EN

The paper discusses the use of an artificial neural network to control the operation of wastewater treatment plants with activated sludge. The task of the neural network in this case is to calculate (predict) the readings of the probe measuring the concentration of nitrate nitrogen (V) in one of the biological reactor tanks. Neural networks are known for their ability to universal approximation of virtually any relationship, including the function of many variables, but the process of "training" the network requires the presentation of many sets of input data and corresponding expected results. This is a difficulty in the case of wastewater treatment plants, because some key process parameters are usually not measured online (samples are taken and measurements are taken in the laboratory), and even if they are, the time intervals are large. Bearing in mind the aforementioned difficulty, this work uses a set of input data consisting only of information that can be measured with measuring probes. As a result of the conducted experiments a high compliance of the probe's prediction with the expected values was obtained. The paper also presents data preparation and the network "training" process.

Słowa kluczowe

EN

wastewater treatment; activated sludge process; artificial neural networks; virtual sensor

PL

oczyszczalnia ścieków; reaktor z osadem czynnym; sztuczne sieci neuronowe; czujnik wirtualny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: Civil and Environmental Engineering Reports
• Rocznik: 2020
• Tom: Vol. 30, no. 4
• Strony: 188--200
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Płonka Lesław

• The Silesian University of Technology, Gliwice, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)