For the next academic year we are announcing the topics of dissertation theses in the new study programme Smart Process Control (code D-IRPxA). The topics will be visible in AIS from 19.3.2025 and you can apply via AIS until 30.5.2025. It is strongly recommended to meet with the supervisor before applying.

List of topics:

• Data Based Process Control (M. Fikar):
  The main aim of this proposed research is to investigate and design new advanced methods of automatic control in process industries to improve efficiency profitability, stability, and competitiveness of process plants. We will focus on processes with heat and mass transfer where efficiency can be improved significantly. These processes are inherently complex, exhibit nonlinear and hybrid behavior that has consequences in control quality and performance. The project will effectively achieve its aim by implementing Model Predictive Control (MPC). We will focus on robust MPC design, incorporating modern research directions utilizing data-based models. Furthermore, we will prioritize the software implementation of proposed solutions, making them accessible to a wider community through open-source code. Finally, the effectiveness of the proposed methods will be rigorously verified through laboratory experiments and collaborations with industrial partners.
• Development of Advanced Methods for Embedded MPC (J. Oravec):
  The development of methods for embedded Model Predictive Control policies focuses on creating advanced approaches and algorithms to enable distributed or parallelizable control for complex and embedded systems. These control strategies aim to optimize control performance of the interconnected subsystems while ensuring computational efficiency, closed-loop system stability, and robustness subject to uncertainties and disturbances. The research project considers designing mathematically tractable approaches that introduce robust control policy to enable distributed and parallel decision-making, addressing challenges such as communication constraints, subsystem interactions, and the demands of real-time implementation.
• Design of Numerically Efficient Near-Optimal Control Methods (J. Oravec):
  This PhD thesis focuses on developing novel control strategies and numerically efficient methods for evaluating near-optimal control laws. In particular, the research aims to design advanced methods and mathematically tractable control algorithms that enable real-time implementations of approximated control laws. The methods are developed with a special focus on minimizing computational complexity, addressing physical constraints, ensuring closed-loop system stability, and satisfying the recursive feasibility of the control law. By addressing the challenges associated with designing near-optimal control laws under the practical limitations of industrial implementations, this research project aims to bridge the gap between theoretical optimal control design methods and their real-world application in industrial plants.
• Set-based control of nonlinear systems (R. Paulen):
  As the computers and algorithms get generally faster, many new control concepts become tractable and can be developed. Set-based control is one of these, where the primary use of sets is in enveloping a space of possible evolutions of variables of a system over time. If these envelopes can be obtained in reasonable time, many properties of dynamic systems such as stability or robustness can be reasoned about. The first goal of the thesis is to build a novel type of multi-base set arithmetics that combines elements such as interval analysis, convex-set theory, and polynomial-functions theory to achieve the best trade-off between accuracy of representation and the burden associated with the underlying calculations to obtain the envelopes. The second goal of the thesis is to develop methods of synthesis of controllers that can be used for safe and reliable control of nonlinear systems. The project of the thesis will be finished with a successful demonstration of the developed techniques on a laboratory plant.
• Development of reliable and explainable models for industrial monitoring, optimization, and control (R. Paulen):
  Safe and sustainable process systems, which constitute the backbone of a modern, developed society, require sensing of key process variables, estimation of unmeasured variables, and application of actions that steer the systems towards desired goals. Automation of human decisions in such tasks would make these decisions become fast, reliable, and error-free. A key technology on the rise in this context is the use of combined mathematical modelling and statistical learning to gather information through software (soft) sensors to monitor, assess, and steer the behaviour of dynamic systems (e.g., industrial processing plants, water, gas and energy networks, or manmade machines and vehicles) into desired operating regimes. The delivered tools will exploit domain knowledge – making the designed mathematical models explainable – and assess and improve the information content of the data – making the models reliable and fit for industrial needs.
• Modelling, Optimal Design and Optimal Operation of Membrane Processes (R. Paulen):
  Membrane processes are crucial in various industrial sectors, including water purification, pharmaceuticals, and food processing, due to their efficiency and sustainability. This proposed research aims to develop an integrative framework that combines advanced mathematical modeling techniques with optimization algorithms to achieve optimal design and operation of membrane processes. The study will involve the development of comprehensive mathematical models that capture the complex phenomena involved in membrane processes, considering factors such as mass transfer, fluid dynamics, and membrane fouling. Furthermore, the research will focus on optimizing the design parameters of membrane systems to enhance performance, minimize energy consumption, and reduce environmental impact. Finally, the proposed framework will facilitate real-time optimization strategies for the optimal operation of membrane processes, ensuring efficient and sustainable operation under varying operating conditions. Overall, this research will contribute to the advancement of membrane technology and its widespread adoption in industrial applications.