Lluis A. Belanche, Julio J. Valdes, Joaquim Comas, Ignasi R. Roda, Manel Poch
The correct control and prediction of Wastewater Treatment Plants poses an important goal in order to avoid breaking the environmental balance and to always keep the system in stable operating conditions. In this respect, it is known that qualitative information -coming from microscopic examinations and subjective remarks- has a deep influence on the activated sludge process, especially in the total amount of effluent suspended solids (TSS), one of the measures of overall plant performance. The strong interrelation between variables, their heterogeneity, and the very high amount of missing information make the use of traditional techniques difficult, or even impossible. Despite this problems, and through the use of several soft computing methods ---rough set theory and artificial neural networks, mainly--- acceptable prediction models are found that show the interplay between variables and give insight to the dynamics of the process.