Course
AI & Chemistry: Future of Innovation and Efficiency
What is the value of artificial intelligence? What steps are needed to automate processes based on measured data? What gains can be achieved through more efficient use of captured data, and what is the investment cost in return?
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What can you expect from this course?
This course consists of two parts:
Data: the investment versus the impact teaches you the most important basic concepts in AI, and zooms in on the trade-off between investments in data (collection, processing and feedback to the process) and the potential gains in efficiency and innovation. If you are in a strategic chair in a company, then this inspiring day is definitely for you! You will leave with a good understanding of the basics of AI and machine learning and be able to assess what investment steps are needed to make the best use of data within your company.
A second part: Data for the process industry: practical deep-dive is split into several modules, which ensure that you can also get to work practically with the necessary steps to better capture, process, analyze and finally use the data within your company to interpret and automate processes. These sessions are geared towards profiles with a background in data processing and a base of programming experience, so that during the course days you can get to work with your own or a provided case and make real steps in the digitization of your processes.
This course is being developed by professors from industrial chemistry and artificial intelligence. The strength of the course is that it really combines two areas of expertise, facilitating cross-fertilization between the two.
Program
Part 1: Data for the process industry: practical deep-dive (1 day)
- What is the value of data? What investment does it represent?
- What type of results can you achieve with what data, data manipulation or AI tools?
- Basics of AI and machine learning
- Applications of AI in the chemical industry
- Context for AI integration (removing biases, ethics, legislation)
Part 2: Data for the process industry: practical deep-dive
Module 0: Prior knowledge required (self-study).
- Technical skills required to follow the rest of the modules
- Basic programming, Python, ...
- Self-study: Online materials, Webinars
Module 1: Data collection and analysis (2.5 days)
- What data is available? Sensor data, Lab data/experimental data
- How to process different types of data (tabula vs timeseries)
- How do you assess data quality?
- Basic data processing techniques, feature engineering
- Basic machine learning techniques (supervised/unsupervised/basic neural networks)
- Basic data analysis (data insights, anomaly detection)
- How to evaluate models
- Getting started with your own case or supplied case
Module 2: Advanced Data Processing and AI (3-4 days)
- Deep learning applied to chemical use cases
- Convolutional Neural Networks
- Long Short-Term Memory Networks
- Explainable AI
- Getting started with your own case or supplied case
Module 3: Simulation and forecasting for the chemical industry (3-4 days)
- Digital twin
- Soft sensing
- Hybrid modeling techniques
- Getting started with your own case or supplied case
Additional information
Organization
Speakers
Date
Fall 2025
Location
University of Antwerp
Campus Groenenborger
Campus Groenenborger is easily accessible with various transportation options.
Price
After registering for this course, our cancellation policy apply.
This course something for you?
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Good to know
Engineers of tomorrow has more to offer than the annual job and networking event.
What
Engineers of Tomorrow aims to support the ecosystem of future industrial engineers, (alumni) engineers, the Faculty of Applied Engineering and technology and innovation-driven companies in their broader professional and personal development.
Who
We welcome future (graduate) industrial engineers and companies driven by technology and innovation.
Where
Engineers of Tomorrow's job and networking event takes place at Bluepoint in Antwerp.
Why
EoT is a sustainable, enabling platform that connects stakeholders with increasing focus on broader societal issues. Fundamentally, each stakeholder contributes to the platform and also has the opportunity to use it.