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- Fachbereich - Maschinenbau und Verfahrenstechnik (539) (remove)
Nachstehend wird der Wortlaut der Prüfungsordnung für den Masterstudiengang „Simulations- und Experimentaltechnik“ an der Hochschule Düsseldorf vom 17.06.2021 (Verkündungsblatt der Hoch-schule Düsseldorf, Amtliche Mitteilung Nr. 786) unter ihrer neuen Überschrift neu bekannt gemacht. Die Neubekanntmachung berücksichtigt die Erste Satzung zur Änderung der Prüfungsordnung für den Masterstudiengang „Simulations- und Experimentaltechnik“ an der Hochschule Düsseldorf vom 02.03.2022 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 829) sowie die Zweite Satzung zur Änderung der Prüfungsordnung für den Masterstudiengang „Simulations- und Ex-perimentaltechnik“ an der Hochschule Düsseldorf vom 14.02.2024 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 915).
In the context of using renewable energies and recycling climate-changing gases, methanation of CO2 provides one possibility. However, volatile availability of renewable energies for hydrogen production and fluctuating CO2streams from different sources lead to enhanced demand in investigating the influence of dynamic process operation on the catalyst performance, including detailed kinetic characterization. Reliable kinetic measurements require isothermal temperature control and absence of macrokinetic diffusion limitations. Both were investigated in this work while the determination of steady-state kinetic parameters is part of ongoing studies. Furthermore, a detailed analysis with respect to the long-term-stability of the nickel-based catalyst was performed
Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery.
Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.
A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control applica-tion (MPC) with a flexible objective function and on-line tunable weights, which runs on a In-dustrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dy-namics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the re-ceiver was developed. By parallelising the model solver, the estimation of service-life con-sumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and archi-tecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies.
A numerical study is carried out meticulously to scrutinize the impact of different shapes of chimneys like circular (outer dia, dc), convergent (outer dia, 0.5dc), divergent (outer dia, 1.5dc), sudden contraction (outer dia, 0.5dc), and sudden expansion (outer dia, 1.5dc) on the performance of an SCPP. Furthermore, the parametric impact with different chimney divergence angles (CDA, ϕ), and ground absorber slope angle (GSA, γ) on the SCPP performance is also scrutinized. Optimum divergence angle (ϕ=+0.75◦) enhances the power generation up to ~ 47% (76 kW) with a horizontal ground absorber surface. An increase or decrease in CDA lessens the power generation. With a sloped ground absorber angle γ=0.6◦, the gain in power generation is 60% (82 kW). The study of combination of ground sloped absorber (γ=0.6◦) and divergent chimney (ϕ=+0.75◦) shows enhancement of the power generation upto 80% (92 kW) more than the classical Manzaranes plant.