4.8 Article

Reducing aeration energy consumption in a large-scale membrane bioreactor: Process simulation and engineering application

Journal

WATER RESEARCH
Volume 93, Issue -, Pages 205-213

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2016.02.026

Keywords

Membrane bioreactor; Ammonia-N-based aeration control strategy; Simulation; Engineering application

Funding

  1. Program for Outstanding PhD thesis of Beijing [2131000306]
  2. Major Science and Technology Program for Water Pollution Control and Treatment [2011ZX07301-002]

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Reducing the energy consumption of membrane bioreactors (MBRs) is highly important for their wider application in wastewater treatment engineering. Of particular significance is reducing aeration in aerobic tanks to reduce the overall energy consumption. This study proposed an in situ ammonia-N-based feedback control strategy for aeration in aerobic tanks; this was tested via model simulation and through a large-scale (50,000 m(3)/d) engineering application. A full-scale MBR model was developed based on the activated sludge model (ASM) and was calibrated to the actual MBR. The aeration control strategy took the form of a two-step cascaded proportion-integration (PI) feedback algorithm. Algorithmic parameters were optimized via model simulation. The strategy achieved real-time adjustment of aeration amounts based on feedback from effluent quality (i.e., ammonia-N). The effectiveness of the strategy was evaluated through both the model platform and the full-scale engineering application. In the former, the aeration flow rate was reduced by 15-20%. In the engineering application, the aeration flow rate was reduced by 20%, and overall specific energy consumption correspondingly reduced by 4% to 0.45 kWh/m(3)-effluent, using the present practice of regulating the angle of guide vanes of fixed-frequency blowers. Potential energy savings are expected to be higher for MBRs with variable-frequency blowers. This study indicated that the ammonia-N-based aeration control strategy holds promise for application in full-scale MBRs. (C) 2016 Elsevier Ltd. All rights reserved.

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