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@PHDTHESIS{Elixmann:565926,
      author       = {Elixmann, David},
      othercontributors = {Marquardt, Wolfgang and Nopens, Ingmar},
      title        = {{E}conomic model-predictive control of membrane bioreactors
                      for wastewater treatment},
      school       = {RWTH Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Shaker Verlag},
      reportid     = {RWTH-2016-00215},
      isbn         = {978-3-8440-4139-2},
      series       = {Berichte aus der Verfahrenstechnik},
      pages        = {xvii, 171 Seiten : Illustrationen},
      year         = {2015},
      note         = {Auch veröffentlicht auf dem Publikationsserver der RWTH
                      Aachen University; Dissertation, RWTH Aachen, 2015},
      abstract     = {The optimization of membrane bioreactor (MBR) operation by
                      nonlinear model-predictive control (NMPC) with activated
                      sludge models is investigated in this work. To this end,
                      different variants of NMPC are applied to simulation models
                      of single-train MBR systems in order to demonstrate the
                      technical feasibility of this concept and assess the
                      economic impact of NMPC to full-scale systems.The
                      application of economic NMPC to the model of a single-train
                      MBR system under nominal conditions shows that the
                      electricity cost of single-train MBR systems can be reduced
                      by up to $7-10\%$ and carbon dosage costs by up to $15\%,$
                      compared to well-tuned conventional control. The economic
                      potential of NMPC is shown to be greatest for control
                      applications with tight effluent limits and a large number
                      of control actuators.The feasibility of economic and
                      ecological NMPC for full-scale MBR is tested by simulation
                      with realistic measurement feedback from on-line
                      measurement. Simultaneous optimization of economic and
                      ecological plant performance is realized by a hybrid
                      discrete-continuous NMPC algorithm which considers economic
                      and ecological control objectives. The algorithm calculates
                      optimal switching times between the objectives together with
                      the optimal control inputs, satisfying optimality conditions
                      defined for the overall control problem. It is shown that
                      the chosen NMPC approach performs robustly when combined
                      with a well-tuned Extended Kalman Filter and a fast
                      trajectory-tracking NMPC despite imperfect state estimator
                      performance and inaccurate disturbance
                      predictions.Directions for future research are pointed out
                      in a discussion of the process control challenges for
                      large-scale MBR and the challenges which need to be
                      addressed to implement this technology in industrial
                      practice.},
      cin          = {416410},
      ddc          = {620},
      cid          = {$I:(DE-82)416410_20140620$},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:82-rwth-2016-002153},
      url          = {https://publications.rwth-aachen.de/record/565926},
}