Product transfer between sites using Joint-Y PLS

Abstract The product transfer problem refers to that of estimating the operating conditions in a target plant in order for it to yield a final product with the same set of final characteristics as is yielded in another source plant. The estimation of such new operating conditions in the target plant often involves a combination of expertise, rules of thumb and in some cases some fundamental modeling. The use of latent variable modeling using historical data from both sites has been proposed in the past as an alternative approach (C.M. Jaeckle and J.F. MacGregor, Product transfer between plants using historical process data, AICHE J., 46 (2000) 1989–1997). This paper presents a new latent variable regression method: Joint-Y PLS (JYPLS) that is ideally suited for modeling the common latent variable structure in multiple plants. The mathematical foundations for the JYPLS model are presented and several parameter estimation approaches are given. A technique is proposed to solve the product transfer problem using the JYPLS model and two examples are given including an industrial scale-up case for a batch pulp digester from the pulp and paper industry.

[1]  Theodora Kourti,et al.  Model Predictive Monitoring for Batch Processes , 2004 .

[2]  J. Macgregor,et al.  A kinetic model for industrial gas-phase ethylene copolymerization , 1990 .

[3]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[4]  Agnar Höskuldsson,et al.  Quadratic PLS regression , 1992 .

[5]  Alison J. Burnham,et al.  Frameworks for latent variable multivariate regression , 1996 .

[6]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[7]  Y H Zhou,et al.  Visualizing integrated bioprocess designs through "windows of operation". , 1999, Biotechnology and bioengineering.

[8]  Garcia Salvador Munoz,et al.  BATCH PROCESS IMPROVEMENT USING LATENT VARIABLE METHODS , 2004 .

[9]  Xue Z. Wang,et al.  Discovery of Operational Spaces from Process Data for Production of Multiple Grades of Products , 2000 .

[10]  Eyal Dassau,et al.  New product design via analysis of historical databases , 2000 .

[11]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[12]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[13]  Alison J. Burnham,et al.  LATENT VARIABLE MULTIVARIATE REGRESSION MODELING , 1999 .

[14]  Theodora Kourti,et al.  Optimization of Batch Operating Policies. Part I. Handling Multiple Solutions , 2006 .

[15]  John F. MacGregor,et al.  Product design through multivariate statistical analysis of process data , 1998 .

[16]  John F. MacGregor,et al.  Product transfer between plants using historical process data , 2000 .

[17]  J. Macgregor,et al.  Optimal Grade Transitions in a Gas Phase Polyethylene Reactor , 1992 .

[18]  Thomas E Marlin,et al.  Process Control , 1995 .

[19]  John F. MacGregor,et al.  Nonlinear product property control in industrial gas‐phase polyethylene reactors , 1993 .

[20]  L. E. Wangen,et al.  A multiblock partial least squares algorithm for investigating complex chemical systems , 1989 .

[21]  Christiane M. Jaeckle,et al.  Industrial applications of product design through the inversion of latent variable models , 2000 .

[22]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[23]  Theodora Kourti,et al.  Troubleshooting of an Industrial Batch Process Using Multivariate Methods , 2003 .

[24]  A. Smilde,et al.  Deflation in multiblock PLS , 2001 .