4.0 Shades of Digitalisation for the Chemical and Process Industries

Discover the benefits of digital technologies with the University of Padova

We are in the midst of the fourth industrial revolution (Industry 4.0). By embracing new digital technologies, industries can increase automation, streamline processes, and save on costs.

On this four-week course from the University of Padova, with the collaboration of several Italian universities, you’ll explore digitalisation in the context of chemical and process industries.

You’ll gain practical skills in implementing key data-driven techniques within an Industry 4.0 paradigm, and obtain knowledge to understand the change of paradigm occurring in the industrial sectors.

Explore machine learning for the process industry

You’ll start by exploring the importance of data digitalisation in the process industry.

Through this exploration, you’ll discover machine learning methods and multivariate statistics analysis that will help you analyse your data with efficiency and accuracy.

Unpack data reconciliation and artificial neural networks

You’ll delve further into data analysis techniques such as regression and classification. This knowledge will help you extract reliable and accurate information about your industry processes.

Taking your skills further, you’ll unpack the uses of artificial neural networks and real-time optimisation to help you exploit data for advanced modelling.

Develop an understanding of model predictive control

Finally, you’ll learn the advanced method of model predictive control. This understanding will help you solve optimisation problems and further streamline your processes.

Throughout the course, you’ll explore case studies to see these methods in action, helping you gain both practical and theoretical knowledge.

By the end, you’ll understand how to choose the most convenient routes to tackle practical problems and improve processes in data management.

This course is for students, undergraduates, or professionals in the field of engineering who want to learn more about digitalisation in the process industries.

• Machine learning and multivariate statistics
• Introduction: Let’s get started
• Reduction of data dimensions
• Putting it all together
• Regression, classification, and data reconciliation
• Regression and PLS
• Data reconciliation
• Putting it all together
• Artificial neural networks and real-time optimization
• Artificial Networks
• Real-time optimization
• Practicing what we know
• Putting it all together
• Model predictive control
• Introduction to Process Control
• System identification
• Model Predictive Control
• Putting it all together