The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system.
These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters.
Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment.
The AI wastewater treatment system consists of two phases, the analysis phase, and the 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 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.