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

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. This research work has been supported by the Spanish Ministry of Science and Innovation (MICINN, Projects CTM2008-06809CO2-01/02 and FPI grant BES-2009-023712) and the Spanish Ministry of Economy and Competitiveness (MINECO, Projects CTM2011-28595-0O2-01/02), jointly with the European Regional Development Fund (ERDF) and Generalitat Valenciana GVAACOMP2013/203, which are gratefully acknowledged. Robles Martínez, Á.; Ruano García, MV.; Ribes Bertomeu, J.; Seco Torrecillas, A.; Ferrer, J. (2014). Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR). Journal of Membrane Science. 465:14-26. https://doi.org/10.1016/j.memsci.2014.04.012