Eigenvector University returns to Seattle, USA May 7-12, 2023 Complete Info Here!

Multivariate Statistical Process Control (MSPC) and Batch SPC (BSPC)

Course Description

Today’s highly instrumented manufacturing processes produce tremendous amount of data, much of which is archived and reviewed only after a major process upset or fault. Multivariate Statistical Process Control (MSPC) and Batch Statistical Process Control (BSPC) provide useful tools for proactive process monitoring, fault detection and fault diagnosis resulting in higher productivity and efficiency applicable to chemical, pharmaceutical, semiconductor and other manufacturing endeavors. The course introduces MSPC methods and strategies for dealing with data overload in continuous processes and extracting critical information about process health. The concepts are then extended to batch processes. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox and Solo software.

Prerequisites

Linear Algebra for ChemometriciansMATLAB for Chemometricians or equivalent experience. Chemometrics I–PCA or equivalent experience highly recommended.

Course Outline

  1. Introduction
    General Principles of SPC and Fault Detection
    Use of Models
    The Multivariate Advantage
  2. A favorite tool: Principal Components Analysis
    Some examples of PCA for MSPC
    Diagnostic Plots for Interpreting and Sourcing Faults
    PCA Scores and Loadings
    Q and T2 Statistics
    Contribution plots
  3. Theoretical basis for MSPC
    Time Series Models and Lagged Variables
    More examples
  4. Modeling Batch Processes
    Unfold PCA (aka Multi-way PCA)
    Summary PCA (SPCA)
    Batch Maturity Index Models
    Multi-way Models: Parallel Factor Analysis (PARAFAC) and PARAFAC2
    Comparison of models on example data
  5. Strategies for Dealing with Unequal Batch Record Length
    Summary Variables
    Correlation Optimized Warping (COW)
    Dynamic Time Warping (DTW)
    Extent of Reaction and other Indicator Variables
    Batch Maturity Index
    Comparison of methods on some example data 
  6. Dealing with Process Drift
  7. Conclusions