Applying and optimizing case-based reasoning for wastewater treatment systems

For the last years, artificial intelligence (AI) approaches have become useful tools in environmental engineering. Here, one relevant application area is the optimization of wastewater treatment plants (WWTP). In this paper, we present several examples for real-time Control (RTC) tasks and decision support systems (DSS) for wastewater treatment (WWT), specifically based on case-based reasoning (CBR). Moreover, we present an approach for optimizing the prediction accuracy of these systems. The idea of this approach is to employ knowledge-intensive similarity measures instead of simple distance metrics. In order to facilitate the modeling of these measures resulting in lower deployment costs of the CBR systems, we propose a novel machine learning technique.