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Processes 2017, 5(4), 56; doi:10.3390/pr5040056

Numerical Aspects of Data Reconciliation in Industrial Applications

1
Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária,CP 68502, CEP 21941-972, Rio de Janeiro, RJ, Brazil
2
OptimaTech, Rio de Janeiro, RJ, CEP 21941-614, Brazil
3
Centro de Pesquisas Leopoldo Americo Miguez de Mello–CENPES, Petrobras–Petróleo Brasileiro SA, Rio de Janeiro, RJ, CEP 21941-915, Brazil
*
Author to whom correspondence should be addressed.
Received: 28 August 2017 / Revised: 25 September 2017 / Accepted: 29 September 2017 / Published: 3 October 2017
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Abstract

Data reconciliation is a model-based technique that reduces measurement errors by making use of redundancies in process data. It is largely applied in modern process industries, being commercially available in software tools. Based on industrial applications reported in the literature, we have identified and tested different configuration settings providing a numerical assessment on the performance of several important aspects involved in the solution of nonlinear steady-state data reconciliation that are generally overlooked. The discussed items are comprised of problem formulation, regarding the presence of estimated parameters in the objective function; solution approach when applying nonlinear programming solvers; methods for estimating objective function gradients; initial guess; and optimization algorithm. The study is based on simulations of a rigorous and validated model of a real offshore oil production system. The assessment includes evaluations of solution robustness, constraint violation at convergence, and computational cost. In addition, we propose the use of a global test to detect inconsistencies in the formulation and in the solution of the problem. Results show that different settings have a great impact on the performance of reconciliation procedures, often leading to local solutions. The question of how to satisfactorily solve the data reconciliation problem is discussed so as to obtain improved estimates. View Full-Text
Keywords: industrial data reconciliation; process monitoring; offshore oil production; nonlinear programming industrial data reconciliation; process monitoring; offshore oil production; nonlinear programming
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Câmara, M.M.; Soares, R.M.; Feital, T.; Anzai, T.K.; Diehl, F.C.; Thompson, P.H.; Pinto, J.C. Numerical Aspects of Data Reconciliation in Industrial Applications. Processes 2017, 5, 56.

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