Sludge bulking is a problem widespread in the operation of activated sludge processes. Over the past fifteen years much research has been applied to identifying the nature of the microbial species responsible for bulking and to developing an understanding of their population dynamics. While some isolated attempts have been made at developing a unit process model capable of simulating the behaviour of bulking, none have been extensively evaluated against field data from full scale plants. This paper describes the development of a multiple-species model of the activated sludge process (for both the reactor and settler), its application to the assessment of various operational strategies for the control of bulking, and its simplification for incorporation into an on-line estimation scheme using a Kalman filter. Routine operating data from the Davyhulme Wastewater Treatment Works in Manchester are used for identification (calibration) of the model. Simulation studies of control strategies are based on the same Works. By using the Kalman filter to reconstruct real-time estimates of the “unmeasurable” states of the process model, the paper also explores the extent to which this filter-based control can bring improvements over similar control based entirely upon conventionally measured operating variables.

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