Literature DB >> 27258618

Optimal flow sensor placement on wastewater treatment plants.

Kris Villez1, Peter A Vanrolleghem2, Lluís Corominas3.   

Abstract

Obtaining high quality data collected on wastewater treatment plants is gaining increasing attention in the wastewater engineering literature. Typical studies focus on recognition of faulty data with a given set of installed sensors on a wastewater treatment plant. Little attention is however given to how one can install sensors in such a way that fault detection and identification can be improved. In this work, we develop a method to obtain Pareto optimal sensor layouts in terms of cost, observability, and redundancy. Most importantly, the resulting method allows reducing the large set of possibilities to a minimal set of sensor layouts efficiently for any wastewater treatment plant on the basis of structural criteria only, with limited sensor information, and without prior data collection. In addition, the developed optimization scheme is fast. Practically important is that the number of sensors needed for both observability of all flows and redundancy of all flow sensors is only one more compared to the number of sensors needed for observability of all flows in the studied wastewater treatment plant configurations.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Fault detection; Mass balancing; Multi-objective optimization; Redundancy; Sensor placement; Wastewater treatment

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Year:  2016        PMID: 27258618     DOI: 10.1016/j.watres.2016.05.068

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  1 in total

1.  Monitoring and detecting faults in wastewater treatment plants using deep learning.

Authors:  Behrooz Mamandipoor; Mahshid Majd; Seyedmostafa Sheikhalishahi; Claudio Modena; Venet Osmani
Journal:  Environ Monit Assess       Date:  2020-01-29       Impact factor: 2.513

  1 in total

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