An AI approach for wastewater treatment systems

Most wastewaters consist of several contaminants (compounds) that need to be removed during the treatment process. A treatability database has been developed containing the treatability of various compounds through different types of treatment processes. In most wastewaters several compounds appear together and two or more treatment processes in series may be needed to meet the effluent limits of the contaminants. The proposed AI wastewater treatment system consists of two phases, analysis phase and synthesis phase. In the analysis phase, an inductive learning algorithm with a grammar based knowledge representation is used to extract knowledge rules from the database. These rules are combined with another set of rules obtained from the experts. All these rules are arranged together to identify the effect of an individual treatment process on several compounds at various concentrations. In the synthesis phase, knowledge rules generated from the analysis phase are used to obtain the sequence of technologies that can satisfy the necessary treatment constraints. Two different methodologies are developed to generate the sequence of technologies. In the first approach, the synthesis phase is formulated as a search problem and a heuristic search function is developed. In the second approach, the synthesis phase is formulated as an optimization problem and a Hopfield neural network is used to obtain the sequence of technologies. Both approaches are compared for the optimality of the solution and the processing time required.Artificial Intelligence & Computer Vision Laboratory, University of CincinnatiDept of Civil & Environmental Engineering, University of Cincinnati