ENBIS 2021 Online Spring Meeting: Data Science in Process Industries17 – 18 May 2021; Online
A 2 day online ENBIS Spring Meeting on Data Science in Process Industries will be held on 17/18th May 2021
Registration and abstract submission is open: Click here!
Process Industries have been an important part of Industrial Statistics for many years. Process industry data includes real-time, multivariate measurements as well as operations data relating to quality of finished output. Machine learning, artificial intelligence and predictive modelling are increasingly important and will enrich the statistical toolbox in industry. Future IoT and Industry 4.0 need these methods to develop and be successful. With the upsurge of interest in data science there are new opportunities for even greater focus on analysis of process industry data. This ENBIS Spring meeting aims to showcase new ideas and motivate further research and applications in the future.
Topics include but are not limited to:
- Artificial intelligence
- Bayesian adaptive design
- Data quality
- DoE and product design
- Forecasting technologies
- Importance of domain knowledge
- Machine learning
- Multivariate analysis in industry
- Predictive modelling
- Process monitoring in Industry 4.0
- Reliability, robustness
- Role of statistical thinking in process industries
- Simulation, emulators and metamodels
- Speed & demand vs quality
Shirley Coleman - Technical Director NUSolve, Newcastle University
Andrea Ahlemeyer-Stubbe - Director Strategic Analytics, Servicepro Agentur für Dialogmarketing und Verkaufsförderung GmbH
Nikolaus Haselgruber -CEO CIS consulting of industrial statistics GmbH
Kristina Krebs - Co-Founder and Business Development Director of prognostica, Würzburg
Marcus Perry - Professor of Statistics, University of Alabama
Marco Reis - Professor of Chemical Engineering, University of Coimbra
Eva Scheideler - Professor of Simulation, Physics and Mathematics, OWL University of Applied Sciences and Arts
Jonathan Smyth-Renshaw - JSR Training & Consultancy
Grazia Vicario – Prof.ssa, Department of Mathematical Sciences, Politecnico di Torino
Registration is free – donations are welcome.
A special issue of the Wiley Journal Applied Stochastic Models in Business and Industry on Data Science in Process Industries is being planned.
Call for contributed papers
There will be a call for papers in February.
An ENBIS webinar “Data Science in Process Industries” will be presented by Marco Reis in March.
David Littlejohn – Opening presentation on Day 1:
Galvanising inter-disciplinary cooperation in process analysis and control in the process industries
Modern process analysis and control generates a lot of data, especially in the high technology “Chemistry-using” industries. Optimising production of chemicals, drugs, food etc. requires multiple contributions across different disciplines to make sure that data from in situ analysers are correctly obtained, and that the data are used along with other process information to allow intelligent performance monitoring and real-time control.
The Centre for Process Analytics and Control Technology (CPACT) was formed in 1997 to provide a forum where the inventers, vendors and users of monitoring and control hardware and software could meet, exchange knowledge, do research and promote best practice. One of the thought-leaders and champions of CPACT was Professor Julian Morris FREng who sadly died in 2020. This talk will describe how the founding principles of CPACT have evolved to serve the current community of 45 international member organisations, and it will reflect on the contributions that Julian Morris made in the fields of multivariate statistical process control, process performance modelling and soft sensors. Given the increasing profile of the Industry 4.0 initiative, it is timely to reflect on how key components of this initiative are not new and were researched by Julian and his peers 20-30 years ago.
David Littlejohn is the Philips Professor of Analytical Chemistry at the University of Strathclyde. He was a founding member of the Centre for Process Analytics and Control Technology (CPACT) and is currently the Operations Director.
Stephen McGough – Opening presentation on Day 2:
Using AI to improve our understanding of waste-water processing
Waste-water treatment is an energy intensive process leading to many environmental concerns. It is very important to remove chemical compounds such as oestrogen from the effluent before it can be safely released into the environment. With increased restrictions on the amount of certain chemical compounds which can be tolerated in the released water there is a need to identify how to efficiently remove enough of these compounds. Compounds are removed by bacteria which exist in the processing system. Current approaches to identifying the best bacteria are based around lab-based experiments on small volumes of waste-water or computer simulations of small volumes of bacteria. However, there is a disconnect between these experiments and what happens in a full-scale wastewater treatment plant. In this talk I shall explain how we’re using AI to scale up and make more realistic simulations of bacterial systems to meet new effluent restrictions.
Stephen McGough is a Senior Lecturer in the School of Computing at Newcastle University. He heads up a team of data scientists working in the application and development of Machine Learning techniques to solve real-world challenges.