Analytical methods for online data quality assessment

Book · Sat Jun 01 04:00:00 EDT 2024

DOI:https://doi.org/10.2166/9781789061154_0163· OSTI ID:2376363

Aguado, Daniel ^[1]; Alferes, Janelcy ^[2]; Arteaga, O. ^[3]; Belia, Lina ^[4]; Copp, John ^[4]; Corominas, Lluis ^[5]; Corona, Francesco ^[6]; Ferrer, Alberto ^[1]; Haimi, Henri ^[6]; Kazemi, Pezhman ^[7]; Le, Quan H. ^[8]; Miletic, Ivan ^[9]; Mulas, Michela ^[10]; Robles, Angel ^[11]; Ruano, Maria Victoria ^[12]; Russo, Stefania ^[13]; Samuelsson, Oscar ^[14]; Steyer, Jean-Philippe ^[15]; Villez, Kris ^[16]; Volcke, Eveline ^[8] ; Wade, Matthew ^[17]; Zambrano, Jesús ^[18]

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1. Universitat Politècnica de València
2. VITO, Belgium
3. Universitat de Barcelona, Spain
4. Primodal Inc.
5. Catalan Institute for Water Research
6. Aalto University, Finland
7. University of Manchester, UK
8. Ghent University, Belgium
9. inCTRL Solutions Inc.
10. Universidade Federal do Ceará
11. Universitat de València
12. Universidad de Valencia, Spain
13. Eawag
14. IVL Swedish Environmental Research Institute
15. French National Institute for Agricultural Research (INRA)
16. ORNL
17. UK Health Security Agency
18. Mathworks

This chapter provides a comprehensive overview of the main steps for algorithmic sensor signal quality assessment, which can enhance the decision-making process for water resource recovery facility (WRRF) operation and optimization. It introduces the concept of redundancy as the basis for data quality assessment. It also explains the typical data processing pipeline, which consists of preliminary analysis, data pre-processing, and specific algorithmic approaches. Each of these processes is presented and discussed in three separate sections. Importantly, this chapter introduces the main approaches for data quality assessment, provides guidelines for selecting the most suitable one and the key performance indicators to evaluate them and explains how to collect metadata through such an algorithmic approach.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2376363
• Country of Publication: United States
• Language: English