MSPC-Multivariate Statistical Process Control

Course Description

Today's highly instrumented chemical and manufacturing processes produce a tremendous amount of data, much of which is archived and only reviewed after a major process upset or fault. MSPC-Multivariate Statistical Process Control covers methods and strategies for dealing with this data overload and extracting critical information about process health. The course covers monitoring and fault detection in chemical and manufacturing processes. Methods for monitoring continuous, batch and transient processes are covered. Using diagnostic plot to track down root causes is covered, along with methods for dealing with process drift. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox.

Prerequisites

Linear Algebra for Chemometricians, MATLAB for Chemometricians or equivalent experience. Chemometrics I--PCA or equivalent experience highly recommended.

MSPC Course Outline

General principles of SPC and fault detection
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
Theoretical basis for MSPC 
- Time Series Models and Lagged Variables
- More examples
Monitoring Batch Processes-Multi-way Models
- Unfold PCA (aka Multi-way PCA)
- PARAFAC and Tucker Models
- Comparison of methods on some example data
Dealing with process drift
- Examples
Conclusions
Additional examples and homework

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Eigenvector Research, Inc., 3905 West Eaglerock Drive, Wenatchee, WA 98801
B.M. Wise, bmw@eigenvector.com, Phone: 509.662.9213, Fax: 509.662.9214
N.B. Gallagher, nealg@eigenvector.com, Phone: 509.687.1039, Fax: 509.687.2033