Keywords
WWTP management, data mining, heuristic knowledge
Location
Session G2: Data Mining for Environmental Sciences (s-DMTES IV)
Start Date
17-6-2014 2:00 PM
End Date
17-6-2014 3:20 PM
Abstract
Wastewater treatment plants (WWTP) are comprised of complex processes that need to be optimally managed. To attain that, in the last years an impressive effort has been made to incorporate monitoring devices able to provide from several hundred to more than ten thousand signals. With the aim to take benefit of those data, different data mining techniques have been applied to transform them into information and knowledge in order to help WWTP's managers. Furthermore, several mathematical models have been developed intending to simulate process behaviour including biomass and pollutants transformation. However, it is recognized that this it is not enough to cope with all the operational problems, but rather it is necessary to integrate heuristic knowledge to better manage specific situations. In this context, it is clear that hybrid systems integrating ontologies, statistical techniques and mathematical models should be developed. In this communication, an overview of some of the main tools applied to obtain information and knowledge from raw data in WWTP's is presented. This overview is complemented by the work developed by the authors of managing two important specific problems: sludge bulking and greenhouse gas emissions, which are good examples of situations that require integration of different types of knowledge.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Where Are We In Wastewater Treatment Plants Data Management? A Review and a Proposal
Session G2: Data Mining for Environmental Sciences (s-DMTES IV)
Wastewater treatment plants (WWTP) are comprised of complex processes that need to be optimally managed. To attain that, in the last years an impressive effort has been made to incorporate monitoring devices able to provide from several hundred to more than ten thousand signals. With the aim to take benefit of those data, different data mining techniques have been applied to transform them into information and knowledge in order to help WWTP's managers. Furthermore, several mathematical models have been developed intending to simulate process behaviour including biomass and pollutants transformation. However, it is recognized that this it is not enough to cope with all the operational problems, but rather it is necessary to integrate heuristic knowledge to better manage specific situations. In this context, it is clear that hybrid systems integrating ontologies, statistical techniques and mathematical models should be developed. In this communication, an overview of some of the main tools applied to obtain information and knowledge from raw data in WWTP's is presented. This overview is complemented by the work developed by the authors of managing two important specific problems: sludge bulking and greenhouse gas emissions, which are good examples of situations that require integration of different types of knowledge.
