In this work we present the advances in the design of an hybrid methodology that combines tools of Artificial Intelligence and Statistics to extract a model of explicit knowledge in regards to the dynamics of a Wastewater Treatment Plant (WWTP). Our line of work is based in the development of methodologies of AI & Stats to solve problems of Knowledge Discovery of Data (KDD) where an integral vision of the pre-process, the automatic interpretation of results and the explicit production of knowledge play a role as important as the analysis itself. In our current work we approach the knowledge discovery with a focus that we named Clustering Based on Rules by States (ClBRxE), which consists in the analysis of the stages that the water treatment moves through, to integrate the knowledge discovered from each subprocess into a unique model of global operation of the phenomenon.
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