18- 20 June 2024
*** Register now***
The course will be delivered by:
- Dr. Francisco Navarro (Sr. Data Science Training Lead at IFF and Visiting Researcher at Imperial College London) [in]
Senior Data Science Training Lead, Imperial College London Visiting Researcher and chemical engineer working at IFF, a global leader and manufacturer of food, beverage, health & biosciences, scent and pharma solutions
Francisco Navarro is leading the application of Machine Learning in manufacturing, where production engineers use industrial data science to monitor, troubleshoot and optimize their chemical processes. His industrial and research experience in Solvay, P&G, and Bayer uniquely combined data-driven methods with manufacturing systems, advances process control and process systems engineering.
He holds a Ph.D. in modelling and simulation where he designed (and patented) multiphase-flow sonoreactors. He also visited Prof. Jensen’s lab at MIT (USA) during his doctoral studies. In 2012, he co-created cacheme.org, an open-source ChemE organization based at the University of Alicante (Spain).
- Dr. Mattia Vallerio (Manufacturing Excellence Site Manager at Syensqo (ex Solvay) and Advanced Process Control Specialist) [in]
As the Manufacturing Excellence Site Manager for Syensqo's Spinetta Marengo production site, Mattia Vallerio is leading a team in charge of delivering operational excellence leadership, guide the site digital transformation, implement advanced process control, industrial data analytics and operational technology solutions.
Mattia Vallerio has almost 10 years of experience in digital transformation and performance optimization of chemical production sites. He has a strong experience in cross-functional project & team management, focusing on delivering value through operational excellence best practices, digitalization, advanced data analysis and advanced process control.
He holds a master degree in Chemical engineering from Politecnico di Milano and a Ph.D. in Engineering Science from KU Leuven.
Before his current position he worked @BASF Antwerp as Advanced Data Analytics lead and Advanced Process Control Engineer. In this role he kick-started the industrial data science field within BASF and he was at the fore-front of the BASF Antwerp site digital transformation.
- Dr. Carlos Perez (Industrial Data Scientist at Syensqo (ex Solvay) and Optimization Specialist) [in]
Carlos Perez Galvan is an Industrial Data Scientist at Syensqo in Brussels, Belgium. Currently, he is the technical leader of an Advanced Analytics corporate team that collaborates with all of Syensqo's businesses.
In cooperation with the Advanced Process Control team, he focuses on leveraging data analytics, process control and process systems engineering methods to optimize plant performance.
During his 7+ years career in P&G (modelling and simulation) and Syensqo he has had the opportunity to develop practical expertise in the fields of modelling, simulation, optimization and machine learning.
He holds a Ph.D. in Chemical Engineering from University College London. He graduated from Universidad Autonoma de Coahuila in Mexico as a Chemical Engineer in 2012.
The instructors combine more than 20+ years of industrial data science experience covering machine learning, first-principle modelling and simulation, optimization, process control, and chemical engineering applied to chemical and process industries.
Their research is co-authored with Reinforcement Learning experts from Imperial College London and Manchester University:
- Industrial data science – a review of machine learning applications for chemical and processes industries [React. Chem. Eng., 2022, 7, 1471]
- Industrial Data Science for Batch Manufacturing Processes (arXiv:2209.09660v1 [cs.LG] 20 Sep 2022)
Day 1 – Industrial data science
- Distillation tower (full example)
- Industrial databases (tags, historians, and automation pyramid)
- Contextual data (asset hierarchies, batch events)
- Quality and tabular data (LIMS, ERP)
- Data democratization and software alternatives
- Hands-on session (connect to databases with Excel, ODBC, and RestAPIs).
Day 2 – Monitoring assets
- Batch dryer example
- Defining KPIs for continuous and batch processes (feature engineering)
- Tracking variability (visual analytics, statistical process control, robust statistics)
- Batch data alignment (e.g., time warping)
- Machine learning for anomaly detection (KNN, PCA, Autoencoders)
- Identifying plant changes in the Tennessee Eastman Process
- Hands-on session (Bring your own data!)
Day 3 – Troubleshooting processes
- Problem definition
- Screening process variables (bootstrap forest, decision trees, and boosted trees)
- Improving processes (sensitivity analysis, explainable AI with SHAP)
- Modelling processes (missing data, Lasso regression, and neural networks)
- Industrial applications (inferential sensors and digital twins)
- Hands-on session (Bring your own data!)
This course will be delivered by:
- Dr. Francisco Navarro (Sr. Data Science Training Lead at IFF and Visiting Researcher at Imperial College London) [in]
Senior Data Science Training Lead, Imperial College London Visiting Researcher and chemical engineer working at IFF, a global leader and manufacturer of food, beverage, health & biosciences, scent and pharma solutions
Francisco Navarro is leading the application of Machine Learning in manufacturing, where production engineers use industrial data science to monitor, troubleshoot and optimize their chemical processes. His industrial and research experience in Solvay, P&G, and Bayer uniquely combined data-driven methods with manufacturing systems, advances process control and process systems engineering.
He holds a Ph.D. in modelling and simulation where he designed (and patented) multiphase-flow sonoreactors. He also visited Prof. Jensen’s lab at MIT (USA) during his doctoral studies. In 2012, he co-created cacheme.org, an open-source ChemE organization based at the University of Alicante (Spain).
- Dr. Mattia Vallerio (Manufacturing Excellence Site Manager at Syensqo (ex Solvay) and Advanced Process Control Specialist) [in]
As the Manufacturing Excellence Site Manager for Syensqo's Spinetta Marengo production site, Mattia Vallerio is leading a team in charge of delivering operational excellence leadership, guide the site digital transformation, implement advanced process control, industrial data analytics and operational technology solutions.
Mattia Vallerio has almost 10 years of experience in digital transformation and performance optimization of chemical production sites. He has a strong experience in cross-functional project & team management, focusing on delivering value through operational excellence best practices, digitalization, advanced data analysis and advanced process control.
He holds a master degree in Chemical engineering from Politecnico di Milano and a Ph.D. in Engineering Science from KU Leuven.
Before his current position he worked @BASF Antwerp as Advanced Data Analytics lead and Advanced Process Control Engineer. In this role he kick-started the industrial data science field within BASF and he was at the fore-front of the BASF Antwerp site digital transformation.
- Dr. Carlos Perez (Industrial Data Scientist at Syensqo (ex Solvay) and Optimization Specialist) [in]
Carlos Perez Galvan is an Industrial Data Scientist at Syensqo in Brussels, Belgium. Currently, he is the technical leader of an Advanced Analytics corporate team that collaborates with all of Syensqo's businesses.
In cooperation with the Advanced Process Control team, he focuses on leveraging data analytics, process control and process systems engineering methods to optimize plant performance.
During his 7+ years career in P&G (modelling and simulation) and Syensqo he has had the opportunity to develop practical expertise in the fields of modelling, simulation, optimization and machine learning.
He holds a Ph.D. in Chemical Engineering from University College London. He graduated from Universidad Autonoma de Coahuila in Mexico as a Chemical Engineer in 2012.
The instructors combine more than 20+ years of industrial data science experience covering machine learning, first-principle modelling and simulation, optimization, process control, and chemical engineering applied to chemical and process industries.
Their research is co-authored with Reinforcement Learning experts from Imperial College London and Manchester University:
- Industrial data science – a review of machine learning applications for chemical and processes industries [React. Chem. Eng., 2022, 7, 1471]
- Industrial Data Science for Batch Manufacturing Processes (arXiv:2209.09660v1 [cs.LG] 20 Sep 2022)
Registration Fee:
Please note that the maximum capacity is 30 participants for this event
- Early bird Industrial price: £1,150 (until 14 April 2024, from 15 April - £1,450)
- Early bird Academic price: £250 (until 14 April 2024, from 15 April - £350)
- Industrial Consortium Partners (One place per company only): Free
To register, please follow this link .
Cancellations
Full refunds, less 10% administration fee, will be given for cancellations that are received in writing on or before 31 May 2024 . After this date, until 10 June 2024, participants who cancel will receive refunds of 50% of the registration fee paid. No refunds will be provided for cancellations received after 11th June 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.
Venue: The Sargent Centre for Process Systems Engineering
Imperial College London
Roderic Hill Building
South Kensington Campus
London SW7 2BB
For the campus website, use this link.
Closest Underground Stations are South Kensington or Gloucester Road.
Accommodation can be booked via the below websites.
Please note that Imperial College has no affiliations with these websites and other websites are available.
.png){kind=logo}
