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Multi-layer model predictive control of complex water systems

  • Autores: Congcong Sun
  • Directores de la Tesis: Vicenç Puig (dir. tes.), M. Gabriela Cembrano Gennari (dir. tes.)
  • Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2015
  • Idioma: inglés
  • Tribunal Calificador de la Tesis: Ramón Pérez Magramé (presid.), Eric Duviella (secret.), Rudy Rafaël Negenborn (voc.)
  • Programa de doctorado: Programa Oficial de Doctorado en Automática, Robótica y Visión
  • Materias:
  • Enlaces
    • Tesis en acceso abierto en: TDX
  • Resumen
    • This thesis is devoted to design a multi-layer MPC controller applied to the complex water network taking into account that the different layers with different time scales and control objectives have their own controller. A two-layer temporal hierarchy coordinating scheme has been applied to coordinate the MPC controllers for the supply and transportation layers. An integrated real-time simulation-optimization approach which contributes to consider the effect of more complex dynamics, better represented by the simulation model, has been developed for regional water networks. The use of the combined approach of optimization and simulation coordination between simulator and optimizer allows to test the proposed multi-layer MPC in a feedback scheme using a realistic simulator of the regional network. The second part of this thesis is focused on the design of a control scheme which uses the combination of linear MPC with a constraint satisfaction problem (CSP) to optimize the non-linear operational control of DWNs. The network aggregation method (NAM) is used to simplify a complex water network into an equivalent conceptual one for the bidirectional network before the use of CSP. The proposed approach is simulated using Epanet to represent the real DWN. Non-linear MPC is used for validation using a generic operational tool for controlling water networks named PLIO. A two-layer scheduling scheme for pump stations in a water distribution network has also been designed in the second part of this thesis. The tuning parameters of such algorithm are the lower layer control sampling period and the number of parallel pumps in the pump station.


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