Ticket prices range from £0-£800 depending on attendee category.
This exciting and pioneering 4-day course taught by Dr Salvador Garcia-Munoz provides a rare opportunity to explore both the fundamentals and advanced elements of Process Analytics using Multivariate Methods, taught at the world-renowned Sargent Centre, the world’s largest systems process engineering centre, within Imperial College London’s impressive South Kensington Campus. Currently working in the US as a Senior Engineering Advisor within the pharmaceutical R&D sector, leading the application of digital design tools for the development of new products and accelerated process design, Dr Garcia-Munoz is a Visiting Professor at Imperial, with +20 years of experience in the implementation of systems engineering tools to industrial problems.
Participants on this unmissable course will be introduced to modern day multivariate data analytics methods through a variety of 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 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, however participants can take advantage of the course even with basic knowledge of running scripts in Python. If you do not have previous experience of Python, you can take this 4-hour course, or a 5 hour course here, to bring you comfortably up to speed. For the advanced topics, knowledge of multivariate methods is required to fully understand the concepts covered in this course.
Itinerary
Process Analytics using Multivariate Methods – Fundamentals:
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
- General Practicalities
Process Analytics using Multivariate Methods – Advanced Topics
Day 1
- Analyzing dynamic data (Batch process analysis)
- Process and product design using PLS with optimization tools
- Optimization Based Chemometrics
- Estimating models with missing data
- EIOT to determine sample compositions (mass fractions)
Day 2
(cont’d) Simultaneous reaction kinetic estimation and spectral deconvolution
- Adaptive and localized modeling
- Non-linear PLS
- Multivariate Image Analysis
- Multivariate Texture Analysis