Process Analytics using Multivariate Methods Course

Process Analytics using Multivariate Methods Course



Dr Salvador Garcia-Munoz,  Senior Engineering Advisor at Eli Lilly and Visiting Professor at Imperial, will be teaching a short course on Process Analytics using Multivariate Methods. The participants will be introduced to modern day multivariate data analytics methods through lectures and hands-on workshops. The syllabus is geared towards general concepts on latent variable modeling (LVM) theory and  advanced topics on the analysis of specific data scenarios (e.g. batch data, image analysis and chemometrics).

The course will be split in two parts, the Introductory course (Day 1) and the Advanced course (Day 2), attendance to both courses is not required but knowledge of multivariate methods is recommended to fully understand the concepts covered in the advanced course.


Detailed Course Summary

The Introductory course will take place on 28 September 2020 ,1pm to 5pm; the Advanced course will take place on 29 September 2020, 1pm to 5pm.

DAY 1 - 28th September 2020, 1pm - 5pm

  • From univariate to multivariate thinking
  • Fundamentals of Principal Components Analysis (PCA)
  • Process understanding and process troubleshooting tools
  • Multivariate Statistical Process Control (MSPC)
  • Workshop: Case studies

DAY 2 – 29th September 2020, 1pm – 5pm

  • Fundamentals of Projection to Latent Structures (PLS)
  • Development of soft sensors with process data
  • Development of soft sensors from spectra (Chemometrics)
  • Workshop: Case studies

Basic understanding of python is required, please download the pyphi package from:


 Registration Fee:

  • £480 for industry participants
  • (20% discount for CPSE Consortium companies)
  • £240 for non-CPSE researchers
  • £80 for non-CPSE students
  • Free for CPSE students and researchers



To register, please complete the registration form on Eventbrite.



Enquiries about the course can be sent to Miss Senait Selassie: The deadline for registration is 30 August 2020.