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Joint state and parameter estimation for a membrane bioreactor system
Author(s) -
Madyastha Venkatesh K,
Prasad Vijaysai
Publication year - 2011
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.578
Subject(s) - computer science , energy consumption , aeration , reuse , novelty , membrane bioreactor , feedback control , control theory (sociology) , estimation theory , control (management) , process engineering , wastewater , control engineering , engineering , environmental engineering , artificial intelligence , waste management , electrical engineering , philosophy , theology , algorithm
Growing environmental concerns and shrinking water resources require methods beyond conventional wastewater treatment. Membrane bioreactor (MBR) is a technology that has become a ubiquitous choice for high quality treatment and reuse of wastewater. One of the key challenges in wastewater treatment is the high energy cost associated with aeration. MBR systems use feedback control to regulate the measured dissolved oxygen level at a predetermined set point by manipulating the blower throughputs. However, for high dynamic loads, feedback control may not result in the best performance and energy efficiency. Any attempt to optimize performance and power consumption beyond a simple controls strategy requires a proper trade‐off analysis between investments on additional sensors and the long‐term benefits. This article proposes a joint state and parameter estimation methodology, which measures and controls the MBR system using available measurements. Thus, limitations of feedback strategies can be overcome by predicting the impact of time varying disturbances on the outputs. The novelty in this approach is the ability to reconstruct the unknown states and parameters with available measurements. The unknown parameters are adaptively estimated online. Lyapunov's direct method is employed to show boundedness of state and parameter estimation errors. Simulation results illustrate the efficacy of the approach. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.