**Registration now open**

Introduction to Process Analytics using Multivariate Methods – Fundamentals            20- 21 May 2024

Course 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 modelling (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 modelling technique particularly useful to understand processes 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.

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

Advanced Applications of Process Analytics using Multivariate Methods                        22-23 May 2024

Day 3 and Day 4 of the course will explore advanced applications of the Latent Variable Modelling (LVM). The syllabus is geared towards more advanced topics such as the analysis of batch data, process and product design, multivariate image and texture analysis and chemometrics. Knowledge of multivariate methods is required to fully understand the concepts covered in this course.

Day 3 topics:

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

Day 4 topics:

• Handling of missing samples
• Adaptive and localized modeling
• Building hybrid models with PLS

This course will be delivered by Dr Salvador Garcia-Munoz, Visiting Professor at Imperial College London, with 20+ years of experience in the implementation of systems engineering tools to industrial problems. He works for the pharmaceutical R&D sector leading the application of digital design tools for the development of new products and accelerated process design. He is an active member of AIChE, a founder of the Systems Based Pharmaceutics Alliance and associate editor for Chemical Engineering Research and Design. His research in multivariate modelling spans from industrial applications to the development of new methods and algorithms to analyse complex datasets common in contemporary industrial scenarios.

 Registration Fee:

• £1550 for industry participants (£1250 Early bird fee - registration and payment before 6^th of April 2024 )
• £350 for Academic participants (£250 Early bird fee - registration and payment before 6^th of April 2024)

***Register HERE***

Cancellations

Full refunds, less 20% administration fee, will be given for cancellations that are received in writing on or before 10^th April 2024. After this date, until 30^th April, participants who cancel will receive refunds of 50% of the registration fee paid. No refunds will be provided for cancellations received after 30^th April 2024.

Substitutions may be made at any time, whilst a valid place is held. The organizer cannot accept liability for costs incurred in the event of a course having to be cancelled as a result of circumstances beyond its reasonable control. 

Please email sargent.centre@imperial.ac.uk for further information