Analytical methods for online data quality assessment

D. Aguado; J. Alferes; F. Arteaga; L. Belia; J. B. Copp; L. Corominas; F. Corona; A. Ferrer; H. Haimi; P. Kazemi; Q. H. Le; I. Miletic; M. Mulas; A. Robles; M. V. Ruano; S. Russo; O. Samuelsson; J. P. Steyer; K. Villez; E. I.P. Volcke; M. J. Wade; J. Zambrano

doi:10.2166/9781789061154_0163

Analytical methods for online data quality assessment

D. Aguado, J. Alferes, F. Arteaga, L. Belia, J. B. Copp, L. Corominas, F. Corona, A. Ferrer, H. Haimi, P. Kazemi, Q. H. Le, I. Miletic, M. Mulas, A. Robles, M. V. Ruano, S. Russo, O. Samuelsson, J. P. Steyer, K. Villez, E. I.P. VolckeM. J. Wade, J. Zambrano

Sensors and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

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.

Original language	English
Title of host publication	Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems
Publisher	IWA Publishing
Pages	163-240
Number of pages	78
ISBN (Electronic)	9781789061154
ISBN (Print)	9781789061147
DOIs	https://doi.org/10.2166/9781789061154_0163
State	Published - Jun 15 2024

Keywords

Data processing
Data visualization
Data-driven methods
Mechanistic methods
Quality assessment
Redundancy

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10.2166/9781789061154_0163

Cite this

Aguado, D., Alferes, J., Arteaga, F., Belia, L., Copp, J. B., Corominas, L., Corona, F., Ferrer, A., Haimi, H., Kazemi, P., Le, Q. H., Miletic, I., Mulas, M., Robles, A., Ruano, M. V., Russo, S., Samuelsson, O., Steyer, J. P., Villez, K., ... Zambrano, J. (2024). Analytical methods for online data quality assessment. In Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems (pp. 163-240). IWA Publishing. https://doi.org/10.2166/9781789061154_0163

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abstract = "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.",

keywords = "Data processing, Data visualization, Data-driven methods, Mechanistic methods, Quality assessment, Redundancy",

author = "D. Aguado and J. Alferes and F. Arteaga and L. Belia and Copp, {J. B.} and L. Corominas and F. Corona and A. Ferrer and H. Haimi and P. Kazemi and Le, {Q. H.} and I. Miletic and M. Mulas and A. Robles and Ruano, {M. V.} and S. Russo and O. Samuelsson and Steyer, {J. P.} and K. Villez and Volcke, {E. I.P.} and Wade, {M. J.} and J. Zambrano",

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doi = "10.2166/9781789061154_0163",

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Aguado, D, Alferes, J, Arteaga, F, Belia, L, Copp, JB, Corominas, L, Corona, F, Ferrer, A, Haimi, H, Kazemi, P, Le, QH, Miletic, I, Mulas, M, Robles, A, Ruano, MV, Russo, S, Samuelsson, O, Steyer, JP, Villez, K, Volcke, EIP, Wade, MJ & Zambrano, J 2024, Analytical methods for online data quality assessment. in Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, pp. 163-240. https://doi.org/10.2166/9781789061154_0163

TY - CHAP

T1 - Analytical methods for online data quality assessment

AU - Aguado, D.

AU - Alferes, J.

AU - Arteaga, F.

AU - Belia, L.

AU - Copp, J. B.

AU - Corominas, L.

AU - Corona, F.

AU - Ferrer, A.

AU - Haimi, H.

AU - Kazemi, P.

AU - Le, Q. H.

AU - Miletic, I.

AU - Mulas, M.

AU - Robles, A.

AU - Ruano, M. V.

AU - Russo, S.

AU - Samuelsson, O.

AU - Steyer, J. P.

AU - Villez, K.

AU - Volcke, E. I.P.

AU - Wade, M. J.

AU - Zambrano, J.

PY - 2024/6/15

Y1 - 2024/6/15

N2 - 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.

AB - 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.

KW - Data processing

KW - Data visualization

KW - Data-driven methods

KW - Mechanistic methods

KW - Quality assessment

KW - Redundancy

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DO - 10.2166/9781789061154_0163

M3 - Chapter

AN - SCOPUS:85202907639

SN - 9781789061147

SP - 163

EP - 240

BT - Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems

PB - IWA Publishing

ER -

Analytical methods for online data quality assessment

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