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Enhancing model predictive control using dynamic data reconciliation
Author(s) -
Abuelzeet Z. H.,
Roberts P. D.,
Becerra V. M.
Publication year - 2002
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690480216
Subject(s) - model predictive control , outlier , computer science , process (computing) , identification (biology) , dynamic data , data mining , controller (irrigation) , control theory (sociology) , process control , system identification , control (management) , control engineering , artificial intelligence , engineering , database , botany , agronomy , biology , measure (data warehouse) , operating system
The use of data reconciliation techniques can considerably reduce the inaccuracy of process data due to measurement errors. This in turn results in improved control system performance and process knowledge. Dynamic data reconciliation techniques are applied to a model‐based predictive control scheme. It is shown through simulations on a chemical reactor system that the overall performance of the model‐based predictive controller is enhanced considerably when data reconciliation is applied. The dynamic data reconciliation techniques used include a combined strategy for the simultaneous identification of outliers and systematic bias.