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Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)

Robles, A., Ruano, M.V., Ribes, J., Seco, A., Ferrer, J.
Journal of membrane science 2014 v.465 pp. 14-26
Monte Carlo method, algorithms, artificial membranes, cleaning, energy conservation, energy costs, filtration, membrane bioreactors, operating costs, screening
This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controller; (2) Monte Carlo method (trajectory-based random sampling) to find suitable initial values for the control inputs; and (3) optimisation algorithm (performing as a supervisory controller) to re-calibrate these control inputs in order to minimise plant operating costs. The model-based supervisory controller proposed allowed filtration to be optimised with low computational demands (about 5min). Energy savings of up to 25% were achieved when using gas sparging to scour membranes. Downtime for physical cleaning was about 2.4% of operating time. The operating cost of the AnMBR system after implementing the proposed supervisory controller was about €0.045/m³, 53.3% of which were energy costs.