Computational intelligence techniques for supervision and diagnosis of biological wastewater treatment systems

Abstract

Wastewater treatment systems (WWTS) are based on complex, dynamic, and highly nonlinear processes. Depending on the design and the specific application, these systems can achieve biological nitrogen and phosphorus removal, besides removal of organic carbon substances. Also, depending on the type/quantity of effluent to be treated, different configurations can be proposed being the most common the aerobic, anoxic and anaerobic schemes working in continuous or sequential modes. In common they have the fact that they deal with communities of different microorganisms which are more or less sensitive to external and/or internal variations of the process conditions. This is a real problem having in mind that usually, influents to be treated are highly inconsistent in flow and concentration being the changes most of the times completely unpredictable. In this way, the knowledge and experience obtained from operational difficulties of one wastewater treatment plant cannot be easily generalized to another. Recent increased regulation over discharge of nutrients to receiving waterways, associated with operational difficulties of wastewater treatment plants, resulted in an increased need for tools to evaluate the organic matter and nutrient-removal capabilities of wastewater treatment processes. However, the description of its behavior requires complex models that involve a very large number of state variables, parameters and biochemical phenomena that have to be accurately identified and quantified. When deterministic models as the activated sludge model (ASM) and anaerobic digestion model (ADM), fail in predicting the WWT process, alternative modeling methodologies usually known as black-box models, may complement and support the knowledge about the wastewater treatment process and operation. Black-box models are entirely identified based on input-output data without reflecting physical, biological or chemical process knowledge in the model structure. What we purpose in this chapter is to identify and detail the black-box models, also known as artificial intelligent (AI) techniques that are being used for WWT monitoring and control. Particularly focused will be the Multivariate Statistical Methods (MVS), Knowledge Based Systems (KBS), Fuzzy Logic (FL)and Artificial Neural Networks (ANN) as they already proved its potential in different real applications. © 2009 Springer-Verlag Berlin Heidelberg.