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Considerations in Parameter Estimation, and Optimal Operations in Urban Water Infrastructure

Rana, S. M. Masud
http://orcid.org/0000-0002-7360-6412
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173308600961

Abstract Details

2023, PhD, University of Cincinnati, Engineering and Applied Science: Environmental Engineering.
Parameter estimation problems are ubiquitous in the field of environmental engineering, for example, in natural systems, an accurate assessment of the nutrient processing capacity of mountain streams is important for the estimation of nutrient load delivered by these streams to downstream water bodies. Similarly, in urban water systems, the ability to optimize pump operations in drinking water networks (DWN) to reduce energy costs is critically dependent on the ability to predict consumer demands that are often estimated from indirect measurements. Parameter estimation problems are challenging, as they are often ill-posed, and without proper considerations given to parameter uncertainty and observability, incomplete or sometimes incorrect conclusions might be drawn. The objectives of this research focus on considerations in parameter estimation, in the field of hydrology and hydraulics, in the fist two studies, and the remaining two studies focus on developing real-time optimal operation frameworks for urban water systems. In the first study, uncertainty in the parameters of the transient storage model (TSM) was estimated using the Markov chain Monte Carlo method, revealing the presence of large uncertainty in the TSM parameters. The TSM is a popular model used by researches to characterize the nutrient (e.g., nitrogen and phosphorus) processing capacity of small streams. The presence of broad uncertainty in the TSM parameters can be of significant interest to regulatory bodies, such as the Chesapeake Bay program who uses these parameter values to develop guidelines for different stakeholders. The second study introduces a consumer node clustering method using self-organizing map (SOM) in DWNS to improve the observability of estimated demands of the clusters. High frequency (e.g., hourly) consumer demands in DWNs are key parameters that drive system hydraulics and are rarely measured directly, and hence are estimated from indirect measurements. Consumer nodes are clustered to reduce the number of unknown demand parameters to be estimated from a limited number of indirect measurements. The estimation of cluster demands is a difficult problem due to observability issues posed by the spatial connectivity of the measurements. Improved observability properties of the SOM clusters would improve demand estimation accuracy and consequently allow improved real-time demand forecasting capabilities required for real-time DWN pump operation optimization. The third study presents a rolling horizon real-time pump operation optimization framework for urban water systems. The framework was used to develop pump operation decisions to minimize pumping costs while maintaining system reliability for an example DWN. The control framework is flexible regarding the incorporation of real-time demands and electricity tariff forecasts, and performs well without any constraints placed on terminal tank levels and the maximum number of pump switches. The optimization framework uses the population-based differential evolution (DE) optimization algorithm to find optimal solutions at each control time step. The fourth study presents the development of a deep neural network (DNN) that can potentially speed up the convergence of the DE algorithm. The DNN was trained using training examples generated by the DE algorithm, and was able to produce good hotstart solutions for the DE algorithm initialization.
Patrick Ray, Ph.D. (Committee Chair)
Drew McAvoy, Ph.D. (Committee Member)
Xi Chen, Ph.D. (Committee Member)
Dominic Boccelli, Ph.D. (Committee Member)
127 p.
Environmental Engineering
parameter estimation; MCMC; Optimal Operation; Drinking Water Network; OTIS-MC; Transient Storage

Recommended Citations

Citations

  • Rana, S. M. M. (2023). Considerations in Parameter Estimation, and Optimal Operations in Urban Water Infrastructure [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173308600961

    APA Style (7th edition)

  • Rana, S. M. Masud. Considerations in Parameter Estimation, and Optimal Operations in Urban Water Infrastructure. 2023. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173308600961.

    MLA Style (8th edition)

  • Rana, S. M. Masud. "Considerations in Parameter Estimation, and Optimal Operations in Urban Water Infrastructure." Doctoral dissertation, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173308600961

    Chicago Manual of Style (17th edition)

ucin1703173308600961
© 2023, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.