Nonlinear Grey-box Identification of Gravity-driven Sewer Networks with the Backwater Effect

Krisztian Mark Balla*, Casper Houtved Knudsen, Adis Hodzic, Jan Dimon Bendtsen, Carsten Kallesøe

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Real-time control of urban drainage networks requires knowledge about stored volumes and flows in order to predict overflows and optimize system operation. However, using flow sensors inside the pipelines means prohibitively high installation and maintenance costs. In this article, we formulate two nonlinear, constrained estimation problems for identifying the open-channel flow in urban drainage networks. To this end, we distribute cost-efficient level sensors along the pipelines and formulate the estimation problems based on the spatially-discretized kinematic and diffusion wave approximations of the full Saint-Venant partial differential equations. To evaluate the capabilities of the two models, the two approaches are compared and evaluated on modeling a typical phenomenon occurring in drainage systems: the backwater effect. An extensive real-world experiment demonstrates the effectiveness of the two approaches in obtaining the model parameters on a scaled water laboratory setup, in the presence of measurement noise.
Original languageEnglish
Title of host publicationNonlinear Grey-box Identification of Gravity-driven Sewer Networks with the Backwater Effect
Number of pages6
Place of PublicationSan Diego
PublisherIEEE
Publication statusSubmitted - 14 Jan 2021

Keywords

  • System Identification
  • backwater effect
  • Urban Drainage
  • Smart Water Infrastructures Laboratory
  • SWIL
  • nonlinear modeling
  • Partial Differential Equation
  • Energy system
  • green lab
  • water infrastructures
  • Sewer
  • Waste Water
  • Disturbance
  • Saint-Venant
  • Kinematic Wave Model
  • Diffusion Wave model
  • Optimization

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