Application of stochastic search for gross error detection and data reconciliation

Author

Zhao, Peng ; Jiang, Weisun

Author_Institution

Res. Inst. of Autom. Control, East China Univ. of Sci. & Technol., Shanghai, China

fYear

1996

fDate

2-6 Dec 1996

Firstpage

728

Lastpage

730

Abstract

Gross error detection and data reconciliation are important problems in operating chemical plants. Typically, constrained nonlinear optimization techniques combined with statistical methods are used to solve these problems. In this study, we explore the use of stochastic search for these purposes. One significant advantage of the method is that it does not depend on any model structure information and only needs simple algebraic calculation. Therefore, it is especially suitable for gross error detection and data reconciliation of complicated connected processes

Keywords

chemical industry; data analysis; distillation; error detection; optimisation; process control; search problems; stochastic processes; chemical plants; constrained nonlinear optimization; data reconciliation; distillation; genetic algorithm; gross error detection; process control; simulated annealing; statistical methods; stochastic search; Computational modeling; Covariance matrix; Current measurement; Energy measurement; Equations; Error correction; Genetic mutations; Measurement standards; Power measurement; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

0-7803-3104-4

Type

conf

DOI

10.1109/ICIT.1996.601691

Filename

601691