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Abstract: Hydrogen is playing an important role in many sectors of modern economy (green vehicles, energy conversion and storage in electrical grids, processing industry). Solid oxide electrolysis cell (SOEC) is an emerging technology for the production of hydrogen from steam and electrical energy as well as for renewable energies storage. Unfortunately, operating at high current and electrical transients cause degradation that leads to premature end of life. A remedy is to implement a hardware module capable to perform online condition monitoring and optimization of SOEC systems resulting in improved overall performance and extended lifetime. That is expected to significantly expand their deployment on the market. However, very little has been done so far. The H2020 project REACTT seems to be one of the first attempts to build an embedded system for monitoring, diagnosis, prognostics, and control (MDPC) for SOEC system. The underlying master's thesis contributes to the REACTT project in the segment related to the supervision of different modules of the MDPC system. The supervisor module is aimed to orchestrate the operation of various functional modules (agents) such as data acquisition, system optimization, diagnosis, prognostics, and mitigation. The thesis focuses on the design of the supervisor module and its implementation on a control platform based on Raspberry Pi 4. The main contributions of the thesis are twofold. First, the dynamic operation of the supervisor modelled by using the state transition diagram (STD). Second, the code for implementation of the supervisor on the target platform done in Python in a way that complies with the requirements imposed in the project.
Keywords: supervisor, module, agent, method, solid oxide electrolysis cell system, diagnosis, prognostics, real-time optimization, Python programming, state transition diagram
Published in RUNG: 20.06.2023; Views: 1004; Downloads: 15
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Abstract: Agent-based simulations are rule-based models traditionally used for the simulations of complex systems. In this paper, an algorithm based on the concept of agent-based simulations is developed to predict the lowest energy packing of a set of identical rigid molecules. The agents are identified with rigid portions of the system under investigation, and they evolve following a set of rules designed to drive the system toward the lowest energy minimum. The algorithm is compared with a conventional Metropolis Monte Carlo algorithm, and it is applied on a large set of representative models of molecules. For all the systems studied, the agent-based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated).
Keywords: Self-assembly, self-organisation, agent based, Monte Carlo, rigid molecules, simulation
Published in RUNG: 10.10.2016; Views: 4825; Downloads: 0
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