To calibrate or not to calibrate, that is the question
Open accessAbstract
Sensors used for control have become widespread in water resources recovery facilities during the strive for resource efficient operations. However, their accuracy is reliant on uncertain laboratory measurements, which are used for calibration and, in turn, to correct for sensor drift. At the same time, current sensor calibration practices are lacking clear theoretical understanding of how measurement uncertainties impact the final control action. The effects of a customarily, and ad hoc, applied calibration threshold are unknown, leading to the current situation where many wastewater treatment processes are controlled by measurements with unknown accuracy. To study how sensor accuracy is affected by calibration, including varying calibration thresholds, we developed a simple theoretical model with closed-form expressions based on the variance and bias in sensor and laboratory measurements. The model was then simulated to yield the results, which showed no practical gain of using a calibration threshold, apart from the situation when calibration is more time-consuming than validation. By contrast, the best accuracy was obtained when consistently executing calibration, which opposes common practice. Further, the sensor calibration error was shown to be transferred to the process, causing a similar deviation from the setpoint when the same sensor was used for control. This emphasizes the importance of minimizing laboratory measurement uncertainties during calibration, which otherwise directly impact operations. Due to these findings we strongly advice shifting mindset from considering calibration as a sequential detection and correction approach, towards an estimation approach, aiming to estimate bias magnitude and drift speed.
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Point calibrations can sometimes lead to uncertainties in the accuracy of measurements and the theoretical understanding of these measurement uncertainties remains unclear.
“Similarly, the method of point calibrations can sometimes lead to uncertainties in the accuracy of measurements and the theoretical understanding of these measurement uncertainties remains unclear [8].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [8]
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Cites
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Adaptive Filtering and Change Detection
Gustafsson, F. (2001) · Wiley eBooks · doi:10.1002/0470841613 · Cited by 1,230 · see citing works
Data-derived soft-sensors for biological wastewater treatment plants: An overview
Haimi, H., Mulas, M., Corona, F., & Vahala, R. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2013.05.009 · Cited by 275 · see citing works
Least squares when both variables have uncertainties
Orear, J. (1982) · American Journal of Physics · doi:10.1119/1.12972 · Cited by 141 · see citing works
Representative process sampling for reliable data analysis—a tutorial
Petersen, L. J., & Esbensen, K. H. (2005) · Journal of Chemometrics · doi:10.1002/cem.968 · Cited by 67 · see citing works
Stentoft, P. A., Munk-Nielsen, T., Møller, J. K., et al. (2021) · Water Research · doi:10.1016/j.watres.2021.116960 · Cited by 47 · see citing works
Quantifying the uncertainty of on-line sensors at WWTPs during field operation
Rieger, L., Thomann, M., Gujer, W., et al. (2005) · Water Research · doi:10.1016/j.watres.2005.09.040 · Cited by 33 · see citing works
Soft Sensing for On-Line Fault Detection of Ammonium Sensors in Water Resource Recovery Facilities
Cecconi, F., & Rosso, D. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c06111 · Cited by 23 · see citing works
Characterizing long-term wear and tear of ion-selective pH sensors
Ohmura, K., Thürlimann, C. M., Kipf, M., et al. (2019) · Water Science & Technology · doi:10.2166/wst.2019.301 · Cited by 21 · see citing works
Fault signatures and bias progression in dissolved oxygen sensors
Samuelsson, O., Björk, A., Zambrano, J., et al. (2018) · Water Science & Technology · doi:10.2166/wst.2018.350 · Cited by 19 · see citing works
Self-citationRossi, L., Rumley, L., Ort, C., Minkkinen, P., Barry, D. A., & Chèvre, N. (2011) · Water Science and Technology · doi:10.2166/wst.2011.177 · Cited by 10 · see citing works
Sensor bias impact on efficient aeration control during diurnal load variations
Samuelsson, O., Olsson, G., Lindblom, E., et al. (2021) · Water Science & Technology · doi:10.2166/wst.2021.031 · Cited by 7 · see citing works
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Research works printed but not linked: 4
- Eurachem, 2019. Eurachem/EUROLAB/CITAC/Nordtest/AMC Guide: Measurement Uncertainty Arising from Sampling: a Guide to Methods and Approaches, 2nd ed. Eurachem. http://refhub.elsevier.com/S0043-1354(2201283-0/sbref0003)
- International Organization for Standardization, 1994. Accuracy (Trueness and Precision) of Measurement Methods and Results -Part 1: General Principles and definiTions (ISO 5725-1:1994). International Organization for Standardization. http://refhub.elsevier.com/S0043-1354(2201283-0/sbref0007)
- Kay, S.M., 1998. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice-Hall PTR, Upper Saddle River, NJ, USA. http://refhub.elsevier.com/S0043-1354(2201283-0/sbref0008)
- S. Andersson, L.Å., Samuelsson, O., Nilsson, S., 2019. Instrumentera R ¨ att på Avloppsreningsverk. IVL Swedish environmental research institute, Svenskt Vatten. http://refhub.elsevier.com/S0043-1354(2201283-0/sbref0014)
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Taherdoost, H. (2024) · Computers, Materials & Continua · doi:10.32604/cmc.2024.054378 · Cited by 47 · see citing works
Bamgboye, O., Liu, X., Cruickshank, P., & Liu, Q. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9040108 · Cited by 4 · see citing works
“Similarly, the method of point calibrations can sometimes lead to uncertainties in the accuracy of measurements and the theoretical understanding of these measurement uncertainties remains unclear [8].”
- Authors
- Samuelsson, O., Lindblom, E. U., Björk, A., & Carlsson, B.
- Affiliations
- Uppsala University (SE)
- Lund University (SE)
- IVL Swedish Environmental Research Institute (CN)
- Corresponding author
- Samuelsson, Oscar
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 229
- Pages
- 119338
- ISSN
- 0043-1354 (ISSN-L)
- OpenAlex
- W4308886926
- Cited by
- 12
- References
- 12
- Citations by year
- 2025: 7 · 2024: 3 · 2023: 2
- FWCI
- 1.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Water Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Svenskt Vatten
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Scientific Measurement and Uncertainty Evaluation
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- sensor calibration, sensor drift, measurement uncertainty, wastewater treatment process control, sensor bias estimation, closed-form model, control accuracy
- MeSH terms
- Calibration, Models, Theoretical, Bias
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