Prof. Dr.-Ing. Carsten Schilde
In this lecture, students develop a comprehensive understanding of the key principles and methods driving the digital transformation of process engineering. The course begins with fundamental concepts of process engineering and their integration with computer science approaches. The focus then shifts to systematic data generation, processing, and utilization - from plant and sensor design through inline measurement techniques to data management, databases, and pre-/postprocessing.
Students are introduced to various modelling and simulation methods, including Black-, Grey-, and White-Box approaches, as well as Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), Molecular Dynamics (MD), and Population Balance Models (PB). A major part of the lecture is devoted to Artificial Intelligence (AI) and Machine Learning (ML), covering the foundations, historical context, and a range of supervised and unsupervised methods, such as Genetic Algorithms (GA), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), as well as Decision Trees, Support Vector Machines (SVM), and Fuzzy Logic. These approaches are applied to analyse and optimize complex process engineering systems.
- Digital transformation in process engineering – objectives, challenges, and applications
- Fundamentals of process engineering in the context of computer science and digitalization
- Data generation and processing – from sensor design to structured data utilization
- Modeling and simulation of physical and technical processes
- Introduction to artificial intelligence and machine learning in process engineering