In the era of Big Data, Industry 4.0 and AI, multivariate analysis is an essential tool for manufacturers engaged in analytics projects.
This 16-hour virtual course (4 hours per day x 4 days) provides comprehensive training on how to effectively extract actionable insights from available historical data, including how to efficiently visualize and identify the independent driving forces affecting process performance.
Workshops using real industrial data, executed on desktop MVA software (Aspen ProMV), will provide practical experience to strengthen your Big Data analytics skillset.
Location: Virtual (Webex)
Price: $1,250 USD / participant for 16-hour course.
$5,250 USD / participant for 16-hour + 20-hours coaching*.
Software: Course participants require Aspen ProMV for completion of the software workshops.
*ProSensus is offering ad-hoc consulting hours to assist attendees with MVA adoption and best practices for implementation on their datasets and unique challenges. These 20-hours of “coaching” may be used by the course attendee only, and expire 4 months after completion of the course.
Course instructors are expected to include 2-3 of the following individuals:
Marlene Cardin, MASc – Project Director
Since 2007, Marlene has helped many Fortune 500 companies in the pharmaceutical, food & beverage, speciality chemical, and oil & gas industries get actionable insights from their big data. In addition to working closely with our consulting clients, Marlene oversees the growth of our Machine Vision business which offers fully turnkey machine vision systems for real-time quality monitoring & control. Marlene has a Bachelor’s degree in Chemical Engineering and a Master’s Degree in Applied Science from McMaster University (Hamilton, Ontario). Marlene completed her Master’s degree under the supervision of ProSensus founder, Dr. John F. MacGregor.
John F. MacGregor, PhD – Founder and Chairman
John has dedicated the last 40 years to helping manufacturers improve and optimize their processes using multivariate data analysis. During his tenure at McMaster University, John authored over 200 peer-reviewed journal publications in the areas of mathematical modeling of processes, optimization and control, rapid product development and image analysis. John has received many awards for his pioneering work in developing and applying multivariate analysis to solve complex manufacturing problems, including the Shewhart Medal and W.G. Hunter Award from the American Society for Quality, the Herman Wold Medal from the Swedish Chemical Society and the R.S. Jane Award and Century of Achievement Award from the Canadian Society for Chemical Engineering. In 2004, John founded ProSensus to help manufacturers learn more from their big data to increase yield, reduce operating costs, and improve product quality.
Monica Salib, BEng – Project Engineer
Monica holds a chemical engineering degree from McMaster University. She has been involved with client projects in rapid product development, troubleshooting analyses, in-house courses, and advanced modeling sessions. Monica has impressed clients with her attention to detail and ability to organize large datasets.
Alex Nguyen, MASc – Project Engineer
Alex has a Bachelor’s Degree in Chemical Engineering and a Master’s in Applied Science (with focus on optimization), both from McMaster University. Prior to joining ProSensus, Alex spent time with Total S. A. and ConocoPhilips. Alex is involved with ProSensus client projects in rapid product development, troubleshooting analyses and in-house courses.
Day 1 (8:00 AM – 12:00 PM Eastern)
- Objectives and overview of the course
- Nature of multivariate process data
- Why use multivariate methods?
- The concept of latent variables
- Some process examples
- Principal component analysis (PCA)
- PCA: a method for extracting information from a single data matrix
- PCA concepts and methods
- Software introduction
- PCA workshops
- PCA workshops to learn the software and to explore PCA analysis of industrial data sets
Day 2 (8:00 AM – 12:00 PM Eastern)
- PLS: Projection to Latent Structures / Partial Least Squares
- Relationship of PLS to multiple linear regression (MLR) and Principal Component Regression
- PLS: geometric & algebraic interpretations
- PLS tools: scores & loading plots, analysis of residuals
- Model interrogation: contribution plots
- Calculating the principal components
- PLS workshops
- PLS workshops to learn the software and to explore PLS analysis of industrial data sets
- Multivariate SPC
- A multivariate approach to process monitoring (MSPC)
- Detecting & diagnosing abnormal operation
- Interpreting and optimizing processes using empirical models correlation vs. causation
- Soft sensors and inferentials
- Optimizing processes based on models from historical data
- Empirical Models
- Interpretation of empirical models built from historical data
Day 3 (8:00 AM – 12:00 PM Eastern)
- Additional workshops
- PLS workshops to learn the software and to explore industrial data sets
- Soft Sensors
- Soft sensors (inferential models)
Day 4 (8:00 AM – 12:00 PM Eastern)
- MVA for Batch Processes
- Analysis, monitoring and control of batch processes
- Batch Alignment
- Batch PLS Workshops
- Batch PLS workshops to learn the software and to explore industrial batch data sets
- Next Steps