Advanced control system for optimal filtration in submerged anaerobic MBRs (SAnMBRs)

Abstract The main aim of this study was to develop an advanced controller to optimise filtration in submerged anaerobic MBRs (SAnMBRs). The proposed controller was developed, calibrated and validated in a SAnMBR demonstration plant fitted with industrial-scale hollow-fibre membranes with variable influent flow and load. This 2-layer control system is designed for membranes operating sub-critically and features a lower layer (on/off and PID controllers) and an upper layer (knowledge-based controller). The upper layer consists of a MIMO (multiple-input–multiple-output) control structure that regulates the gas sparging for membrane scouring and the frequency of physical cleaning (ventilation and back flushing). The filtration process is monitored by measuring the fouling rate on-line. This controller demonstrated its ability to keep fouling rates low (close to 0 mbar min −1 ) by applying sustainable gas sparging intensities (approx. 0.23 Nm 3  h −1  m −2 ). It also reduced the downtimes needed for ventilation and back-flushing (less than 2% of operating time).

[1]  Günter Wozny,et al.  Improving the efficiency of membrane bioreactors by a novel model-based control of membrane filtration , 2007 .

[2]  Simon Judd,et al.  Membrane Fouling in Membrane Bioreactors for Wastewater Treatment , 2002 .

[3]  Ignasi Rodríguez-Roda,et al.  Automatic control systems for submerged membrane bioreactors: a state-of-the-art review. , 2012, Water research.

[4]  A. B. de Haan,et al.  Novel spacers for mass transfer enhancement in membrane separations , 2005 .

[5]  Harvey Arellano-Garcia,et al.  Model-based recognition of fouling mechanisms in membrane bioreactors , 2009 .

[6]  Anastasios J. Karabelas,et al.  An investigation of the long-term filtration performance of a membrane bioreactor (MBR): The role of specific organic fractions , 2011 .

[7]  Ludo Diels,et al.  Validation of a supervisory control system for energy savings in membrane bioreactors. , 2011, Water research.

[8]  Wolfgang Marquardt,et al.  Run‐to‐run control of membrane filtration processes , 2007 .

[9]  J Ferrer,et al.  Sub-critical filtration conditions of commercial hollow-fibre membranes in a submerged anaerobic MBR (HF-SAnMBR) system: the effect of gas sparging intensity. , 2012, Bioresource technology.

[10]  Hèctor Monclús,et al.  Automatic control system for energy optimization in membrane bioreactors , 2011 .

[11]  Hongjun Lin,et al.  Influence of elevated pH shocks on the performance of a submerged anaerobic membrane bioreactor , 2010 .

[12]  P. Martin Larsen,et al.  Industrial applications of fuzzy logic control , 1980 .

[13]  Gürkan Sin,et al.  A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs , 2010, Environ. Model. Softw..

[14]  A Seco,et al.  Methane recovery efficiency in a submerged anaerobic membrane bioreactor (SAnMBR) treating sulphate-rich urban wastewater: evaluation of methane losses with the effluent. , 2012, Bioresource technology.

[15]  S. Judd The status of membrane bioreactor technology. , 2008, Trends in biotechnology.

[16]  I Rodríguez-Roda,et al.  A knowledge-based control system for air-scour optimisation in membrane bioreactors. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.

[17]  Ignasi Rodríguez-Roda,et al.  Online monitoring of membrane fouling in submerged MBRs , 2011 .

[18]  Hèctor Monclús,et al.  Knowledge-based control module for start-up of flat sheet MBRs. , 2012, Bioresource technology.

[19]  M. V. Ruano,et al.  An advanced control strategy for biological nutrient removal in continuous systems based on pH and ORP sensors , 2012 .

[20]  Pierre Aimar,et al.  Model for colloidal fouling of membranes , 1995 .

[21]  R. Field,et al.  Critical flux concept for microfiltration fouling , 1995 .

[22]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[23]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[24]  Hèctor Monclús,et al.  Development of a control algorithm for air‐scour reduction in membrane bioreactors for wastewater treatment , 2011 .

[25]  Simon Judd,et al.  The cost of a large-scale hollow fibre MBR. , 2010, Water research.

[26]  E. Brauns,et al.  A new method for the evaluation of the reversible and irreversible fouling propensity of MBR mixed liquor , 2008 .

[27]  G. Oron,et al.  pH effects on the adherence and fouling propensity of extracellular polymeric substances in a membra , 2011 .

[28]  Alejandro Vargas,et al.  Controlled backwashing in a membrane sequencing batch reactor used for toxic wastewater treatment , 2008 .

[29]  David Jeison,et al.  On-line cake-layer management by trans-membrane pressure steady state assessment in Anaerobic Membrane Bioreactors for wastewater treatment , 2006 .

[30]  J Ribes,et al.  Experimental study of the anaerobic urban wastewater treatment in a submerged hollow-fibre membrane bioreactor at pilot scale. , 2011, Bioresource technology.

[31]  Anja Drews,et al.  Membrane fouling in membrane bioreactors—Characterisation, contradictions, cause and cures , 2010 .

[32]  H. Ngo,et al.  A new approach to backwash initiation in membrane systems , 2006 .

[33]  Hee-Deung Park,et al.  Reduction of membrane fouling by simultaneous upward and downward air sparging in a pilot-scale submerged membrane bioreactor treating municipal wastewater. , 2010 .

[34]  Henk B. Verbruggen,et al.  Fuzzy control and conventional control: What is (and can be) the real contribution of Fuzzy Systems? , 1997, Fuzzy Sets Syst..