The control and prediction of Wastewater Treatment Plants poses an
important goal: to avoid breaking the environmental balance by always
keeping the system in stable operating conditions. It is known that
qualitative information ---coming from microscopic examinations
and subjective remarks--- has a deep influence on the activated sludge
process. In particular, on the total amount of effluent suspended
solids, one of the measures of overall plant performance. The search
for an input-output model of this variable and the prediction of
sudden increases (bulking episodes) is thus a central concern to
ensure the fulfillment of current discharge limitations.
Unfortunately, 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.
Through the combined use of several methods ---rough set theory and
artificial neural networks, mainly--- reasonable prediction models are
found, which serve also to show the different importance of variables
and give insight to the process dynamics.