Optimize Plant Performance Using Multivariate Data Analysis
Our attendees return to work ready to apply multivariate data analysis to their own applications. In addition, ProSensus' highly-trained instructors can serve to propel projects forward in a consulting capacity subsequent to course-completion.
A Good Fit For: Engineers, scientists and statisticians responsible for process troubleshooting, control and optimization.
Prerequisites: None. Experience in the collection and/or analysis of process data is desirable. A background in basic statistics is helpful, but not required.
- Introduction to the nature of process data
- Concept of latent variables
- Principal Components Analysis (PCA)
- Analysis of historical data and troubleshooting using PCA
- Projection to Latent Structures/Partial Least Squares (PLS)
- Interpretation of empirical models and soft sensor applications
- SPC and multivariate monitoring
- Multivariate classification