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Model Predictive Control of Water Networks Considering Flow and Pressure

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Part of the book series: Advances in Industrial Control ((AIC))

Abstract

This chapter proposes a nonlinear model predictive control (NMPC) strategy for WDNs including both flow and pressure constraints. A WDN might be regarded as a nonlinear system described by differential-algebraic equations (DAEs), when flow and hydraulic head equations are considered in the model. The main operational goal of WDNs is the minimization of the economic costs associated with pumping. In addition to the minimization of costs, the optimal operation of WDNs should guarantee water supply with required flows and pressures at all the control/demand nodes in the network. Other operational goals related to safety and reliability are usually sought. From a control point of view, NMPC is a suitable control strategy for WDNs, since the optimal operation of the network cannot be established a priori by fixing reference volumes in the tanks. Alternatively, the NMPC strategy should determine the optimal filling/emptying sequence of the tanks taking into account that electricity price varies between day and night and that the demand also follows a 24-h repetitive pattern. On the other hand, as a result of the ON/OFF operation of pumps in pumping stations, a two-layer control scheme has been utilized: the NMPC strategy at the hourly sampling timescale is chosen in the upper layer while the pump scheduling approach at the minutely sampling timescale dealing with pumps in the ON/OFF manner is proposed in the lower layer. Finally, results of applying the proposed control strategy to a portion of the Barcelona WTN are provided in simulation.

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References

  1. Brdys M, Ulanicki B (1994) Operational control of water systems: structures. Algorithms and applications. Prentice-Hall, Upper Saddle River

    Google Scholar 

  2. Brooke A, Kendrick D, Meeraus A, Raman R (2004) GAMS. A user’s guide. GAMS Development Corporation, Washington DC, USA

    Google Scholar 

  3. Cembrano G, Quevedo J, Puig V, Pérez R, Figueras J, Verdejo JM, Escaler I, Ramón G, Barnet G, Rodríguez P, Casas M (2011) PLIO: a generic tool for real-time operational predictive optimal control of water networks. Water Sci Technol 64(2): 448–459

    Google Scholar 

  4. Cembrano G, Wells G, Quevedo J, Perez R, Argelaguet R (2000) Optimal control of a water distribution network in a supervisory control system. Control Eng Pract 8(10): 1177–1188

    Google Scholar 

  5. Grosso JM, Ocampo-Martinez C, Puig V, Joseph B (2014) Chance-constrained model predictive control for drinking water networks. J Process Control 24(5): 504–516

    Google Scholar 

  6. Grosso JM, Ocampo-Martinez C, Puig V, Limon D, Pereira M (2014) Economic MPC for the management of drinking water networks. In: 2014 European control conference, Strasburg, France, pp 790–795

    Google Scholar 

  7. Ocampo-Martinez C, Ingimundarson A, Puig V, Quevedo J (2008) Objective prioritization using lexicographic minimizers for MPCc of sewer networks. IEEE Trans Control Syst Technol 16(1): 113–121

    Google Scholar 

  8. Ocampo-Martinez C, Puig V, Cembrano G, Quevedo J (2013) Application of MPC strategies to the management of complex networks of the urban water cycle. IEEE Control Syst Mag 33(1): 15–41

    Google Scholar 

  9. Rawlings JB, Mayne DQ (2009) Model predictive control: theory and design. Wis. Nob Hill Pub. cop, Madison

    Google Scholar 

  10. Streif S, Kogel M, Bathge T, Findeisen R (2014) Robust nonlinear model predictive control with constraint satisfaction: a relaxation-based approach. In: 19th IFAC world congress, Cape Town, South Africa, pp 11073–11079

    Google Scholar 

  11. Sun CC, Puig V, Cembrano G (2016) Combining CSP and MPC for the operational control of water networks. Eng Appl Artif Intell 49: 126–140

    Google Scholar 

  12. Wang Y, Ocampo-Martinez C, Puig V (2015) Robust model predictive control based on Gaussian processes: application to drinking water networks. In: 2015 European control conference, Linz, Austria, pp 3292–3297

    Google Scholar 

  13. Wang Y, Ocampo-Martinez C, Puig V (2016) Stochastic model predictive control based on Gaussian processes applied to drinking water networks. IET Control Theory Appl 10(8): 947–955

    Google Scholar 

  14. Wang Y, Puig V, Cembrano G, Economic MPC with periodic terminal constraints of nonlinear differential-algebraic-equation systems: application to drinking water networks. In: European control conference, Aalborg, Denmark, pp. 1013–1018

    Google Scholar 

  15. Wang Y (1), Cembrano G (1), Puig V (2), Urrea M (1), Romera J (1), Saporta D (3), Valero jG (3) (1)Institut de Robotica i Informatica Industrial, CSIC-UPC, Barcelona, Spain (2) Research Center “Monitoring, Safety and Automatic Control” (CS2AC-UPC), Terrassa, Spain (3) Aguas de Barcelona (AGBAR), Barcelona, Spain

    Google Scholar 

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Correspondence to Gabriela Cembrano .

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Wang, Y. et al. (2017). Model Predictive Control of Water Networks Considering Flow and Pressure. In: Puig, V., Ocampo-Martínez, C., Pérez, R., Cembrano, G., Quevedo, J., Escobet, T. (eds) Real-time Monitoring and Operational Control of Drinking-Water Systems. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-50751-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-50751-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50750-7

  • Online ISBN: 978-3-319-50751-4

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