Abstract
This paper discusses a case–based reasoning approach to modeling the specific or experiential knowledge coming directly from wastewater treatment plant (WWTP) operation within the overall supervisory task of a plant. A concrete implementation is detailed: case structure, case library organization, retrieving algorithm, matching function, and learning techniques. Starting from some initial cases (learning by observation), the system evolves, adapting its experiential knowledge (learning by own experience) from the actual operation of the WWTP under control. The result is a more accurate supervisory system. Recording previous experiences—cases—in the system helps to solve new similar or related situations in the plant with less effort than other methods that start from scratch to build up new solutions. Moreover, the continuous execution of the system enhances its adaptation to new situations that could appear.
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