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@PHDTHESIS{Vaupel:810954,
      author       = {Vaupel, Yannic},
      othercontributors = {Mitsos, Alexander and Lucia, Sergio},
      title        = {{A}ccelerating nonlinear model predictive control through
                      machine learning with application to automotive waste heat
                      recovery},
      volume       = {12 (2020)},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2021-00835},
      series       = {Aachener Verfahrenstechnik series - AVT.SVT - Process
                      systems engineering},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2020},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2021; Dissertation, Rheinisch-Westfälische
                      Technische Hochschule Aachen, 2020},
      abstract     = {Waste heat recovery (WHR) from heavy-duty (HD) diesel
                      trucks is a viable option for reducing the carbon footprint
                      of the transport industry. Among the various available
                      technology options for WHR, using a bottoming organic
                      Rankine cycle (ORC) with the exhaust gas as heat source is
                      considered the most promising. The ORC system in a HD diesel
                      truck is subject to strong heat source fluctuations, which
                      is in contrast to ORC operation in established processes.
                      This poses substantial challenges for safe and efficient
                      operation of the WHR system. In this thesis, we address
                      these challenges using model-based methods. We first develop
                      a dynamic ORC model for WHR and validate it with measurement
                      data from a test rig. Next, we extend our dynamic model to a
                      switching model that it is capable of accounting for
                      start-up and shutdown situations. We compare two popular
                      modeling approaches for the heat exchangers, identifying
                      their perks and weaknesses. With our model established, we
                      use dynamic optimization to understand how the system is
                      best operated and we find that it can be beneficial to
                      temporarily increase workfing fluid superheat in certain
                      situations.From our findings, we derive a control structure
                      for model-based control of the process. We apply this
                      structure in silico to nonlinear model predictive control
                      (NMPC) and to a PI controller with feedforward term. Our
                      findings indicate good control performance of NMPC but
                      excessive computational demand for on-board application. The
                      PI controller achieves similar control performance at
                      insignificant computational demand. Next, we apply a
                      machine-learning (ML) based method for NMPC to the ORC
                      system. While this achieves a drastic reduction in online
                      computational demand, constraint satisfaction cannot be
                      guaranteed. Hence, as a final contribution, we develop
                      methods that use ML to reduce the computational demand of
                      NMPC while promoting constraint satisfaction.},
      cin          = {416710},
      ddc          = {620},
      cid          = {$I:(DE-82)416710_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2021-00835},
      url          = {https://publications.rwth-aachen.de/record/810954},
}