Model Building by Merging Submodels Using PLSR

摘要

PLSR (partial least squares regression)has become a basic tool for chemometrics,monitoring and modeling of processes,etc.The bvasic idea of PLSR is to relate two data matrices X and Y into a multivariate linear model,for analysis of the data with noisy and collinear variables.In industrial processes,sub-models of the specific units are built for monitoring and engineering process control.However,we know that these process units are not individually independent.As a result,the full model is usually desirable.In an actual process,there are thousands to ten thousands of variables being measured and conveniently recorded instantaneously.It is not practical to deal with such a large number of variables to construct a full model at time,especially they are collinear and embedded with noise.In this paper,the linear regression model merging procedureis proposed to incorporate PLSR models of subsystems into a full model.By way of this approach,the computation time and memory can thus significantly be reduced.It is quite suitable to merge the process sub-models built from PLSR into the complete one.The method could be extended to dynamic and non-linear modeling easily.Two exampls for dynamic modeling and monitoring are presented for illustration.One is the dynamic modeling of a 4X4 linear process.Second one is the process monitoring of a double effect evaporator.