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- W4296982015 abstract "This paper describes a gray-box modeling framework for accurate soft sensor estimates of flow meters in industrial storage tank-farms. Having such estimates are critical in ensuring fence-line mass balance and preventing loss of revenue due to instrumentation error. To address this, a novel framework is proposed which combines Kalman filter, Bayesian optimization, and uncertainty quantification techniques to get an accurate point estimate for flow meter biases in addition to other states such as tank levels. We describe the formulation and methodology and present the results generated from implementing this framework in a representative case study." @default.
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- W4296982015 date "2022-09-25" @default.
- W4296982015 modified "2023-09-27" @default.
- W4296982015 title "A State Estimation, Kalman Filter Auto-tuning and Uncertainty Quantification Framework with Application to Industrial Storage Tank-farms" @default.
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- W4296982015 doi "https://doi.org/10.1007/978-981-19-3938-9_42" @default.
- W4296982015 hasPublicationYear "2022" @default.
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