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Data reconciliation using uncertain models

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Abstract

Data reconciliation is a technique which is used to obtain accurate estimates of variables and parameters from measured process data and a process model. The process model used for reconciling the measurements is generally derived from material and energy balances and is assumed to be exact. In this paper, we propose two modified reconciliation approaches that can be used to obtain accurate estimates of variables in the presence of model uncertainties arising due to uncertain parameters. While both approaches give identical estimates of measured and unmeasured variables, the second approach also provides an improved estimate of the model parameters. A formal procedure for observability and redundancy classification of flow variables in the presence of model uncertainties is also proposed.

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Authors and Affiliations

  1. Department of Chemical Engineering, IIT Madras, Chennai, 600036, India

    Sridharakumar Narasimhan & Shankar Narasimhan

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  1. Sridharakumar Narasimhan
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  2. Shankar Narasimhan
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Correspondence to Shankar Narasimhan.

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Narasimhan, S., Narasimhan, S. Data reconciliation using uncertain models. Int J Adv Eng Sci Appl Math 4, 3–9 (2012). https://doi.org/10.1007/s12572-012-0061-3

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  • DOI: https://doi.org/10.1007/s12572-012-0061-3

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