Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

  • Authors:

  • J. Comas;I. Rodríguez-Roda;K. V. Gernaey;C. Rosen;U. Jeppsson;M. Poch

  • Affiliations:

  • Chemical and Environmental Engineering Laboratory (LEQUiA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Catalonia, Spain;Chemical and Environmental Engineering Laboratory (LEQUiA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Catalonia, Spain;Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Kgs. Lyngby, Denmark;Department of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-22100 Lund, Sweden;Department of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-22100 Lund, Sweden;Chemical and Environmental Engineering Laboratory (LEQUiA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Catalonia, Spain

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2008

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Abstract

This paper proposes a risk assessment model for settling problems of microbiological origin in activated sludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnose microbiology-related solids separation problems with absolute certainty but to quantify in dynamic scenarios whether simulated operational procedures and control strategies lead to favourable conditions for them to arise or not. The rationale behind the model (which integrates the mechanisms of standard activated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system and the details of its operation are illustrated in the different sections of the paper. The performance of the risk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. The results demonstrate that some control strategies, although performing better regarding operating costs and effluent quality, induce a higher risk for solids separation problems. In view of these results, it is suggested to integrate empirical knowledge into mechanistic models to increase reliability and to allow assessment of potential side-effects when simulating complex processes.