A knowledge-based framework for the diagnosis of sludge bulking in the activated sludge process

Abstract

Despite advances in the understanding of the activated sludge process for treating wastewater, the operation of an activated sludge process plant - in particular, the detection, diagnosis and remedy of operational problems - still involves a significant amount of qualitative knowledge derived from empirical observations. Expert systems can be of assistance to plant operators in problem diagnosis by automating the problem-solving behavior of human experts and retrieving the appropriate chunks of qualitative knowledge from a large collection of such knowledge as the context of the problem dictates. A new generation of expert systems shell delivers better performance by providing (i) an object-centred framework with interesting computational properties to organize the considerable amounts of information about the physical world, (ii) flexible, context-dependent and programmable inference strategies to better model the problem-solving behavior of human experts, (iii) a reliable means of integrating numeric and symbolic computation, and (iv) a means for the expert system application to couple its inference procedure with its ability to interact with events in the real world through sensors and actuators. A prototype expert system employing a new generation expert system shell has been developed for diagnosing the sludge bulking problem in the activated sludge process. The paper discusses the knowledge representation scheme employed in the prototype, which is general enough to be extended to cover other operational problems occurring in sewage treatment plants. A study was performed to validate the knowledge in the prototype by comparing the conclusions of a panel of human experts reported in the literature with those of the prototype in response to a wide range of operating conditions. The study shows close agreement between the two sets of conclusions.