E2.09 Newhart 2018 ReNUWIt Annual Meeting Poster

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Abstract/Contents

Abstract: Onsite, decentralized, and satellite water reclamation facilities are ideal to meet the increasing water demand and wastewater production in the US. However, working on a smaller scale poses process challenges: changes in influent quality can cause biological system disturbances; flow interruption can occur suddenly (mechanical failure) or unnoticeably slowly (accumulation of debris). Early fault detection is key to preventing downtime, avoiding effluent limit exceedances, and reducing the cost of operation at water and wastewater treatment facilities. In this work, a multivariate statistical analysis program is developed for early detection of system faults at a demonstration-scale municipal wastewater treatment facility. Future work on the program includes fault diagnosis and automated corrective control in order to produce water of a desired effluent quality at the lowest cost.

Type of resource	other
Date created	May 2018

Subject	Re-inventing the Nation’s Urban Water Infrastructure
Subject	ReNUWIt
Subject	E2.09
Subject	Efficient Engineered Systems
Subject	Energy and resource recovery
Subject	Mines Park
Subject	Golden
Subject	Colorado
Subject	fault detection
Subject	management
Subject	optimization
Subject	sensors
Subject	statistical process control
Subject	wastewater treatment

Related Publication	Newhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 157, 498-513. http://doi.org/10.1016/j.watres.2019.03.030 Y9
Location	https://purl.stanford.edu/vm786bj8253

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License: This work is licensed under an Open Data Commons Attribution License v1.0.

Preferred Citation: Newhart, K. B., Hering, A. S., & Cath, T. Y. (2018). E2.09 Newhart 2018 ReNUWIt Annual Meeting Poster. Stanford Digital Repository. Available at: https://purl.stanford.edu/vm786bj8253

Re-inventing the Nation's Urban Water Infrastructure (ReNUWIt)

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