Numerical Aspects of Data Reconciliation in Industrial Applications
AbstractData 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
Câmara MM, Soares RM, Feital T, Anzai TK, Diehl FC, Thompson PH, Pinto JC. Numerical Aspects of Data Reconciliation in Industrial Applications. Processes. 2017; 5(4):56.Chicago/Turabian Style
Câmara, Maurício M.; Soares, Rafael M.; Feital, Thiago; Anzai, Thiago K.; Diehl, Fabio C.; Thompson, Pedro H.; Pinto, José C. 2017. "Numerical Aspects of Data Reconciliation in Industrial Applications." Processes 5, no. 4: 56.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.