Self-Validating Inferential Sensors with Application to Air Emission Monitoring
Abstract
Inferential sensors, or soft sensors, refer to a modeling approach to estimating hard-to-measure process variables from other easy-to-measure, on-line sensors. Since many sensors are used as input variables to estimate the output, the probability that one of the sensors fails increases significantly. In this paper, we propose a self-validating inferential sensor approach based on principal component analysis (PCA). The input sensors are validated using a fault identification and reconstruction approach proposed in Dunia et al. AIChE J. 1996, 42, 2797−2812. A principal component model is built for the input sensors for sensor validation, and the validated principal components are used to predict output variables using linear regression or neural networks. If a sensor fails, the sensor is identified and reconstructed with the best estimate from its correlation to other sensors. The principal components are also reconstructed accordingly for prediction. The number of principal components used in sensor validation and prediction are chosen differently based on different criteria. The typical input correlation or collinearity is utilized for sensor validation and removed in predicting the output to avoid ill-conditioning. The self-validating soft sensor approach is applied to air emission monitoring, where continuous monitoring of the air pollutants is required for environmental regulations.
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ANN MVPCs have already been used to monitor and control the growth of microalgae through monitoring basic CPPs and glucose concentrations in heterotrophic microalgal growth.
“ANN MVPCs have already been used to monitor and control the growth of microalgae through monitoring basic CPPs [103,104] and glucose concentrations in heterotrophic microalgal growth [105].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [103]
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- Authors
- Qin, S. J., Yue, H., & Dunia, R.
- Affiliations
- The University of Texas at Austin (US)
- ORCID
- Qin, S. Joe
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 1997-05-01
- Volume
- 36
- Issue
- 5
- Pages
- 1675-1685
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2033791852
- Cited by
- 111
- References
- 7
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 7 · 2022: 3 · 2021: 4 · 2020: 4 · 2018: 3 · 2017: 6 · 2016: 3 · 2015: 3 · 2014: 6 · 2013: 5 · 2012: 10
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Industrial & Engineering Chemistry Research (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- inferential sensors, soft sensors, sensor fault detection, principal component analysis, air quality monitoring, multicollinearity, ill-conditioning, process variable estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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