A knowledge based neural network approach for waste water treatment system

Wastewater treatment system design can be identified as a planning problem. The wastewater consists of several chemicals (compounds) that need to be removed during the treatment process. A treatability database containing the treatability of various compounds through different types of treatment processes has been developed. In general, two or more compounds appear together, and one may need to combine and arrange two or more treatment processes to meet the treatment objectives. The wastewater treatment system has been designed in two phases. In the first phase, an inductive learning algorithm is used to extract the knowledge rules from the database. These rules are compiled to identify the effect of an individual treatment process on several compounds at different concentrations. The second phase involves selecting, combining, and arranging the unit treatment processes that will meet all treatment objectives. This phase has been formulated as an optimization problem. A Hopfield network is used to obtain the necessary sequence of treatment processes to achieve the desired level of effluent concentration from the given influent concentrations