Nonlinear Dynamic Data Reconciliation in Real Time in Actual Processes

Abstract In this work, some recent developments regarding the real time monitoring of industrial processes based on robust data reconciliation and gross error detection are reviewed. Particularly, robust data reconciliation and gross error detection are performed on line and in real time in a real polymerization process.

[1]  Diego Martinez Prata,et al.  Comparative analysis of robust estimators on nonlinear dynamic data reconciliation , 2008 .

[2]  E. Biscaia,et al.  Nonlinear parameter estimation through particle swarm optimization , 2008 .

[3]  Jose A. Romagnoli,et al.  Data Processing and Reconciliation for Chemical Process Operations , 1999 .

[4]  Günter Wozny,et al.  An optimization framework for parameter estimation of large-scale systems , 2007 .

[5]  Miguel J. Bagajewicz,et al.  On the Performance of Principal Component Analysis in Multiple Gross Error Identification , 1999 .

[6]  Chen Bingzhen,et al.  Correction coefficient method for gross error detection based on temporal redundancy , 2000 .

[7]  Joachim Werther,et al.  Flowsheet simulation of solids processes: Data reconciliation and adjustment of model parameters , 2008 .

[8]  Derya B. Özyurt,et al.  Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes , 2004, Comput. Chem. Eng..

[9]  Raghunathan Rengaswamy,et al.  A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems , 2001 .

[10]  Jose A. Romagnoli,et al.  A strategy for simultaneous dynamic data reconciliation and outlier detection , 1998 .

[11]  Ralph W. Pike,et al.  Source reduction from chemical plants using on-line optimization , 1995 .

[12]  C. M. Crowe,et al.  Formulation of linear data reconciliation using information theory , 1996 .

[13]  Wongphaka Wongrat,et al.  Modified genetic algorithm for nonlinear data reconciliation , 2005, Comput. Chem. Eng..

[14]  Diego Martinez Prata,et al.  In-Line Monitoring of Bulk Polypropylene Reactors Based on Data Reconciliation Procedures , 2008 .

[15]  Xueyu Chen,et al.  Optimal implementation of on-line optimization , 1998 .

[16]  Sigurd Skogestad,et al.  Scaled steady state models for effective on-line applications , 2008, Comput. Chem. Eng..

[17]  Bo Li,et al.  Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process , 2006, Simul. Model. Pract. Theory.

[18]  William Y. Svrcek,et al.  A robust direct approach for calculating measurement error covariance matrix , 1999 .

[19]  Jose A. Romagnoli,et al.  Robust estimation of measurement error variance/covariance from process sampling data , 1997 .

[20]  Serge Domenech,et al.  Simulation and data validation in multistage flash desalination plants , 1998 .

[21]  Bei Hu,et al.  Soft sensors based on nonlinear steady-state data reconciliation in the process industry , 2007 .

[22]  Victor M. Becerra,et al.  Combined bias and outlier identification in dynamic data reconciliation , 2002 .

[23]  Shankar Narasimhan,et al.  Data reconciliation & gross error detection: an intelligent use of process data , 1999 .

[24]  C. M. Crowe,et al.  Data reconciliation — Progress and challenges , 1996 .