SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency.
Keywords: Self organizing map; activated sludge; coke wastewater; denitrification; neural gas; nitrification.