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Abstract Variable water quality within buildings is of increasing concern due to public health impacts (e.g., lead,Legionella pneumophila,Naegleria fowleri, disinfection byproducts). Advances in data acquisition and analytics provide the opportunity to monitor real‐time building‐wide water quality variability. Accordingly, the goal of this research was to create a water quality sensor platform including data acquisition, storage, and mining methods able to monitor, and ultimately improve, water quality within buildings. The platform was used to monitor water temperature, pH, conductivity, oxidation–reduction potential, dissolved oxygen, and chlorine using sensors only. Other building data infrastructure, specifically Wi‐Fi logins by occupants, were used to approximate activity rates and associated water use. An advanced machine‐learning technique, gradient boosting machines, predicted the chlorine residuals throughout the building plumbing network better than multivariate linear regression models. Finally, the implications of water quality monitoring on costs, scalability, reliability, human dimensions, regulatory compliance, and future green building designs are considered.
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How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control
Abstract Real‐time water quality control (WQC) in water distribution networks (WDN), the problem of regulating disinfectant levels, is challenging due to lack of (i) a proper control‐oriented modeling considering complicated components (junctions, reservoirs, tanks, pipes, pumps, and valves) for water quality modeling in WDN and (ii) a corresponding scalable control algorithm that performs real‐time water quality regulation. In this paper, we solve the WQC problem by (a) proposing a novel state‐space representation of the WQC problem that provides an explicit relationship between inputs (chlorine dosage at booster stations) and states/outputs (chlorine concentrations in the entire network) and (b) designing a highly scalable model predictive control (MPC) algorithm that showcases fast response time and resilience against some sources of uncertainty.
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- PAR ID:
- 10442579
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 5
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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