General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products

Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that of finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these sce...

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

[2]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[3]  Salvador García Muñoz,et al.  Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product , 2010, Comput. Chem. Eng..

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

[5]  J. Macgregor,et al.  Scale-up of a Pharmaceutical Roller Compaction Process Using a Joint-Y Partial Least Squares Model , 2011 .

[6]  Salvador García-Muñoz.  Establishing multivariate specifications for incoming materials using data from multiple scales , 2009 .

[7]  John F. MacGregor,et al.  Robust processes through latent variable modeling and optimization , 2011 .

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

[9]  Salvador García Muñoz,et al.  Application of multivariate latent variable modeling to pilot-scale spray drying monitoring and fault detection: Monitoring with fundamental knowledge , 2009, Comput. Chem. Eng..

[10]  J. Macgregor,et al.  Product optimization and control in the latent variable space of nonlinear PLS models , 2004 .

[11]  John F. MacGregor,et al.  Jackknife and bootstrap methods in the identification of dynamic models , 2001 .

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

[13]  John F. MacGregor,et al.  Optimal purchasing of raw materials: A data‐driven approach , 2008 .

[14]  H. Kramer,et al.  Towards a Crystalline Product Quality Prediction Method by Combining Process Modeling and Molecular Simulations , 2006 .

[15]  Theodora Kourti,et al.  Product transfer between sites using Joint-Y PLS , 2005 .

[16]  John F. MacGregor,et al.  Latent variable MPC for trajectory tracking in batch processes , 2005 .

[17]  Haruo Takada,et al.  Data-based control of an industrial tubular reactor , 1999 .

[18]  George Stephanopoulos,et al.  Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function , 2004, Bioinform..

[19]  S. Badawy,et al.  Role of drug substance material properties in the processibility and performance of a wet granulated product. , 2009, International journal of pharmaceutics.

[20]  John F. MacGregor,et al.  Rapid Development of New Polymer Blends: The Optimal Selection of Materials and Blend Ratios , 2006 .

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

[22]  András P Borosy,et al.  Quantitative composition–property modelling of rubber mixtures by utilising artificial neural networks , 1999 .

[23]  John F. MacGregor,et al.  Sequential design of mixture experiments for the development of new products , 2007 .

[24]  Salvador García-Muñoz,et al.  Optimization of Batch Operating Policies. Part II. Incorporating Process Constraints and Industrial Applications , 2008 .

[25]  J. Macgregor,et al.  Control of batch product quality by trajectory manipulation using latent variable models , 2004 .

[26]  Koji Muteki,et al.  De-risking Scale-up of a High Shear Wet Granulation Process Using Latent Variable Modeling and Near-Infrared Spectroscopy , 2011, Journal of Pharmaceutical Innovation.

[27]  Xue Z. Wang,et al.  Knowledge discovery from process operational data using PCA and fuzzy clustering , 2001 .

[28]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[29]  C. Kiparissides,et al.  A comprehensive model for the prediction of particle-size distribution in catalyzed olefin polymerization fluidized-bed reactors , 1998 .

[30]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[31]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[32]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[33]  John F. MacGregor,et al.  Modeling and Optimization of a Tablet Manufacturing Line , 2011, Journal of Pharmaceutical Innovation.

[34]  Francois Yacoub,et al.  Application of Quality by Design Principles to Legacy Drug Products , 2011, Journal of Pharmaceutical Innovation.