Knowledge-based modelling and simulation of operational problems of microbiological origin in wastewater treatment plants

The activated sludge and anaerobic digestion processes have been modelled in widely accepted models. Nevertheless, these models still have limitations when describing operational problems of microbiological origin. The aim of this thesis is to develop a knowledge-based model to simulate risk of plant-wide operational problems of microbiological origin.For the risk model heuristic knowledge from experts and literature was implemented in a rule-based system. Using fuzzy logic, the system can infer a risk index for the main operational problems of microbiological origin (i.e. filamentous bulking, biological foaming, rising sludge and deflocculation). To show the results of the risk model, it was implemented in the Benchmark Simulation Models. This allowed to study the risk model's response in different scenarios and control strategies. The risk model has shown to be really useful providing a third criterion to evaluate control strategies apart from the economical and environmental criteria.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately