The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.

https://www.mv.uni-kl.de/mec/home.
 

There is an ever increasing demand for the use of powder particles with micron size, providing very high specific surface area, in order to increase the dissolution rate. This is  important to powder processing industries including pharmaceuticals when achieving the maximum effect of the products. However, handling and processing of such submicron size powders is challenging. Granulation is a process to modify the particle size distribution for improved material handling and dosing properties in powder processing. On the other hand, data-driven control, including reinforcement learning (RL) has been experiencing a renaissance as data availability and deep neural network (DNN) are rapidly progressing by pushes arising in multi-domains. In process engineering, challenges arise as data availability is rather limited and afflicted with relatively large errors. Thus, the use of data-driven or combined methods with classical approaches (grey-box models) seems appropriate to handle the complex relationships between particle and product properties.
 

This project aims to develop the key scientific understanding of how to overcome these challenges in the granulation process by utilising mathematical tools concerning modelling, dynamical analysis and control, as well as the experimental data for parameter identification. Thereby, the main task is to create a predictive tool based on population balance modelling (PBM) and approximate method of moment (AMOM) for the design of continuous wet granulation on a twin-screw machine. The control task is to optimize the process with respect to a desired particle size distribution of the granule product, which is a critical quality attribute for subsequent unit operations, such as tabletting. Hereby, the screw configuration and different process parameters are the main impact factors within this optimization problem, which have to be addressed primarily. To this end, classical control approaches of optimal control theory or emerging learning-based algorithms, such as reinforcement learning, as well as a suitable combination thereof may be invoked.

This research work shall be conducted in a close cooperation with academician researchers who are specialized in granulation and experimental research.

The research compiles from the following list of tasks.

• Developing of finite dimensional mathematical models using AMOM in the form of continuous ODEs for various classes of PBMs
• Analytical approximations of various functions appearing in the PBM in terms of orthogonal polynomials
• Optimal control of granulation process to obtain desired particle size distribution using Pontryagin maximum principle and dynamic programming
• Development of a learning based control algorithm based on reinforcement learning (RL)  and deep-neural networks (DNN)
• Numerical solution of PDE and ODE models using classical methods or DNN
   

• Above average university degree in applied mathematics, process engineering or control engineering
• Knowledge of at least one programming language: Matlab, Python, C++ is expected
• Knowledge in dynamical systems
• Proficiency in English or / and German is essential
• Highly motivated, eager to work within a team or independently.
   

• Payment according to TV-L E13 with an initial one-year time limit
• The possibility to do a PhD and to teach is given in case of scientific aptitude
• TUK strongly encourages qualified female academics to apply
• Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
• Electronic application is preferred. Please attach only one coherent PDF.

You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.

Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax:  +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de

Granulation
Population balance models
Approximate method of moment
Reinforcement learning
Optimal control
 

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31. Oktober 2023

Immediate