Process Observers and Data Reconciliation Using Mass and Energy Balance Equations

Hodouin, D.

doi:10.1007/978-1-84996-106-6_2

Cited by 10 publications

(5 citation statements)

References 89 publications

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“…[ 13 ] Detailed discussions of the DR, gross error detection, and similar techniques are provided in the literature. [ 14–20 ]…”

Section: Introductionmentioning

confidence: 99%

“…[13] Detailed discussions of the DR, gross error detection, and similar techniques are provided in the literature. [14][15][16][17][18][19][20] DR techniques are significantly useful in the accurate estimation of plant production/consumption, quality control, and improvements. Most of the DR algorithms have been fed into widely used commercial software, and detailed discussions of the techniques used are scarce due to commercial competitiveness and confidentiality.…”

Section: Introductionmentioning

confidence: 99%

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Non‐linear steady‐state data reconciliation: Theoretical perspective and practical scenarios

2023

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Data reconciliation (DR) is one of the primary error handling methods to reduce measurement errors in industries that may otherwise cause misleading information about the plant. In this article, the mathematical aspects of measurement errors and their treatment by DR are discussed in detail. The flaws in the existing DR methods have been identified and re-investigated. More importantly, the feasibility and health check-up of the DR problem have been discussed. The primary objective of the work is to develop a DR code based on the observations made in the present study, which involves DR solutions by both successive linearization (SL) and sequential quadratic programming (SQP) schemes. Benchmarking of the code with standard cases showed its wider suitability in solving DR problems. The algebraic SL method was found suitable for proper data health check-ups and reliable solutions, whereas SQP was robust. The developed code was tested successfully for a chemical plant as well.

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“…[ 13 ] Detailed discussions of the DR, gross error detection, and similar techniques are provided in the literature. [ 14–20 ]…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non‐linear steady‐state data reconciliation: Theoretical perspective and practical scenarios

2023

View full text Add to dashboard Cite

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“…For instance, plant data obtained in chemical and bio-tech industries are often corrupted by measurement errors which undermine the quality of such data-driven methods. In such scenarios, data reconciliation (DR) as a technique has been used for an accurate and consistent estimation of process parameters and variables from measurements (Dabros et al, 2009;Narasimhan and Jordache, 1999;Hodouin, 2010). The DR based methods exploit the redundancies arising due to physical constraints such as the closure of mass and energy balance equations to improve the estimates of the underlying variables (Narasimhan and Jordache, 1999).…”

Section: Introductionmentioning

confidence: 99%

Thermodynamically consistent estimation of Gibbs free energy from data: data reconciliation approach

Salike

Bhatt

2019

Bioinformatics

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Motivation Thermodynamic analysis of biological reaction networks requires the availability of accurate and consistent values of Gibbs free energies of reaction and formation. These Gibbs energies can be measured directly via the careful design of experiments or can be computed from the curated Gibbs free energy databases. However, the computed Gibbs free energies of reactions and formations do not satisfy the thermodynamic constraints due to the compounding effect of measurement errors in the experimental data. The propagation of these errors can lead to a false prediction of pathway feasibility and uncertainty in the estimation of thermodynamic parameters. Results This work proposes a data reconciliation framework for thermodynamically consistent estimation of Gibbs free energies of reaction, formation and group contributions from experimental data. In this framework, we formulate constrained optimization problems that reduce measurement errors and their effects on the estimation of Gibbs energies such that the thermodynamic constraints are satisfied. When a subset of Gibbs free energies of formations is unavailable, it is shown that the accuracy of their resulting estimates is better than that of existing empirical prediction methods. Moreover, we also show that the estimation of group contributions can be improved using this approach. Further, we provide guidelines based on this approach for performing systematic experiments to estimate unknown Gibbs formation energies. Availability and implementation The MATLAB code for the executing the proposed algorithm is available for free on the GitHub repository: https://github.com/samansalike/DR-thermo. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Thermodynamically Consistent Estimation of Gibbs Free Energy from Data: Data Reconciliation Approach

Salike

Bhatt

2018

Preprint

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Motivation: Thermodynamic analysis of biological reaction networks requires the availability of accurate and consistent values of Gibbs free energies of reaction and formation. These Gibbs energies can be measured directly via the careful design of experiments or can be computed from the curated Gibbs free energy databases. However, the computed Gibbs free energies of reactions and formations do not satisfy the thermodynamic constraints due to the compounding effect of measurement errors in the experimental data. The propagation of these errors can lead to a false prediction of pathway feasibility and uncertainty in the estimation of thermodynamic parameters. Results: This work proposes a data reconciliation framework for thermodynamically consistent estimation of Gibbs free energies of reaction, formation and group contributions from experimental data. In this framework, we formulate constrained optimization problems that reduce measurement errors and their effects on the estimation of Gibbs energies such that the thermodynamic constraints are satisfied. When a subset of Gibbs free energies of formations is unavailable, it is shown that the accuracy of their resulting estimates is better than that of existing empirical prediction methods. Moreover, we also show that the estimation of group contributions can be improved using this approach. Further, we provide guidelines based on this approach for performing systematic experiments to estimate unknown Gibbs formation energies. Availability: The MATLAB code for the executing the proposed algorithm is available for free on the GitHub repository: https://github.com/samansalike/

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Process Observers and Data Reconciliation Using Mass and Energy Balance Equations

Cited by 10 publications

References 89 publications

Non‐linear steady‐state data reconciliation: Theoretical perspective and practical scenarios

Non‐linear steady‐state data reconciliation: Theoretical perspective and practical scenarios

Thermodynamically consistent estimation of Gibbs free energy from data: data reconciliation approach

Thermodynamically Consistent Estimation of Gibbs Free Energy from Data: Data Reconciliation Approach

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