Imperial College London

Dr. Salvador Garcia Munoz

Faculty of EngineeringDepartment of Chemical Engineering

Visiting Professor







ACE ExtensionSouth Kensington Campus





PhD. Chemical Engineering (McMaster University, Canada).
MS. Chemical Engineering (Monterrey Tech, Mexico)
BS. Chemical and Computer Systems Engineering ( Monterrey Tech, Mexico)

NEW - Process Analytics using multivariate methods - May 15 to May 19 2023

Description of the course:

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). LVM is a data-driven modeling technique particularly useful to understand systems where acquired data is: abundant, complex, correlated and noisy. Basic knowledge of statistics, linear algebra and geometry are helpful to fully understand the concepts of this course.

The course includes various hands-on workshops using data from real applications, using Python tools.

2023 Course offerings 

"Introduction to Process Analytics using Multivariate Methods " -  May 15 and 16 2023

Day 1: Principal Components Analysis Fundamentals and common applications

  • Geometric and statistical introduction to PCA
  • Algorithms and objective functions
  • Global diagnostics and contributions
  • Outlier Detection
  • Multivariate process monitoring
  • Establishing multivariate specifications for materials
  • Unsupervised clustering and classification

Day 2: Partial Least Squares fundamentals and common applications

  • Objective function and reduced rank regression
  • Algorithms
  • Parametric interpretation and model diagnostics
  • Chemometrics  and soft sensors
  • Multi-block methods
  • General Practicalities

"Advanced Applications of Process Analytics using Multivariate Methods" - MAY 17 - 19  2023

Day 3 – Batch analysis and optimization based solutions

  • Batch process analysis and monitoring
  • NEW ! - Quick introduction to PYOMO 
  • Process and product design using PLS with optimization tools
  • NEW !  In-silico formulation of new products (blending optimization)
  • Optimization Based Chemometrics for spectral calibration to mass fractions (EIOT)

Day 4

  • NEW ! - Handling of missing samples
  • Adaptive and localized modeling
  • Multivariate Image Analysis
  • Multivariate Texture Analysis
  • NEW! - Building hybrid models with PLS

Day 5  (Half day - Tentative)

  • NEW ! - Implementing a real-time monitoring framework in an industrial environment
  • NEW ! –Process monitoring workshop using industrial grade software

Registration Fee applies (discounts available for CPSE Consortium companies, students and researchers ) 

For inquires regarding the detailed agenda or to register please email:



Odgers J, Kappatou C, Misener R, et al., 2023, Probabilistic predictions for partial least squares using bootstrap, Aiche Journal, Vol:69, ISSN:0001-1541, Pages:1-16

Nașcu I, Diangelakis NA, Muñoz SG, et al., 2023, Advanced model predictive control strategies for evaporation processes in the pharmaceutical industries, Computers and Chemical Engineering, Vol:173, ISSN:0098-1354

Kappatou C, Odgers J, García-Muñoz S, et al., 2023, An optimization approach coupling pre-processing with model regression for enhanced chemometrics, Industrial and Engineering Chemistry Research, Vol:62, ISSN:0888-5885, Pages:6196-6213

Zhao F, Ochoa MP, Grossmann IE, et al., 2022, Novel formulations of flexibility index and design centering for design space definition, Computers & Chemical Engineering, Vol:166, ISSN:0098-1354

Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Probabilistic framework for optimal experimental campaigns in the presence of operational constraints, Reaction Chemistry and Engineering, Vol:7, ISSN:2058-9883, Pages:2359-2374

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