Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview

Authors:
Henri Haimi;Michela Mulas;Francesco Corona;Riku Vahala
Affiliations:
Department of Civil and Environmental Engineering, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland;Department of Civil and Environmental Engineering, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland;Department of Information and Computer Science, Aalto University, School of Science, P.O. Box 15400, FI-00076 Aalto, Finland;Department of Civil and Environmental Engineering, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland
Venue:
Environmental Modelling & Software
Year:
2013

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Abstract

This paper surveys and discusses the application of data-derived soft-sensing techniques in biological wastewater treatment plants. Emphasis is given to an extensive overview of the current status and to the specific challenges and potential that allow for an effective application of these soft-sensors in full-scale scenarios. The soft-sensors presented in the case studies have been found to be effective and inexpensive technologies for extracting and modelling relevant process information directly from the process and laboratory data routinely acquired in biological wastewater treatment facilities. The extracted information is in the form of timely analysis of hard-to-measure primary process variables and process diagnostics that characterize the operation of the plants and their instrumentation. The information is invaluable for an effective utilization of advanced control and optimization strategies.