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      42
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    Photonische CuxO-Strukturen als kolorimetrische CO/CO2-Sensoren
    This project aims at developing self-sustaining, label-free, colorimetric sensors for carbon monoxide (CO) and carbon dioxide (CO2) detection, utilizing the concept of adaptable 3D photonic structures composed of copper oxides (CuxO). These 3D structures exhibit refractive index modulation, either with long- or short-range periodicity, resulting in photonic bandstops or reflection edges that manifest as structural colors within the visible wavelength spectrum. The hypothesis revolves around leveraging the phase changes induced in CuxO by the interaction with CO/CO2, leading to observable shifts in structural color, enabling a straightforward, naked-eye assessment of gas exposure without the need for external power or underlying electronics. Building on our expertise in manipulating the reflective properties of photonic structures, the goal is to create CuxO photonic structures with short-range order, referred to as photonic glasses (PhGs). These PhGs will feature tunable structural color due to reaction-induced refractive index changes. The project will investigate the interplay between the 3D photonic structure's parameters, such as macropore size, volume filling, and oxide phase ratios, influencing their colorimetric response pre and post-exposure to CO/CO2. The focus is on in-situ characterization methods, tracking the morphology changes and alterations in optical properties induced by gas exposure and gas-solid reactions. The ultimate aim is to provide a user-friendly, photonic-based label-free colorimetric sensor for facile gas exposure assessment.
      24
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    CRC 1333 - Project A03: Electrocatalysis under confinement: CO2 reduction with COF catalysts
    Ziel ist das Verständnis, die Modellierung und Kontrolle von Confinement-Effekten in der elektrokatalytischen CO2-Reduktionsreaktion (CO2RR) in atomar präzisen Poren kovalent-organischer Gerüstverbindungen (COFs). COF-Design, Elektrochemie und theoretische Modellierung werden kombiniert, um zu einem umfassenden Verständnis der CO2RR-Reaktivität in einschränkenden Geometrien zu gelangen. Confinement-Effekte umfassen (i) CO2-Abscheidung und Überlöslichkeit in Mesoporen, (ii) Erhöhung der CO2RR- vs. HER-Selektivität durch lokale pH-Effekte, Massentransport und Design der elektrischen Doppelschicht, sowie (iii) Selektivitätsverbesserung für Multikohlenstoff-(C2+)-Produkte durch Tandemkatalyse.
      42
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    Stochastic Optimal Control and MPC - Dissipativity, Risk, and Performance
    It stands to reason that three of the most impactful concepts in systems and control in the 20th century have been the optimal control siblings – i.e. the Pontryagin Maximum Principle (PMP) and Dynamic Programming together with the Hamilton-Jacobi-Bellman approach – as well as the dissipativity framework for dynamic systems coined by Jan Willems. Moreover, Model Predictive Control (MPC) can be regarded as a successful attempt to alleviate the curse of dimensionality in solving the Hamilton-Jacobi-Bellman Equation (HJBE) by resorting to a receding-horizon application of open-loop optimal inputs – either obtained directly via the PMP or via direct solution methods for optimal control. The last decades have seen significant progress, for example, in terms of stability and performance analysis of MPC considering nonlinear prediction models, real-time implementation of MPC, MPC accounting for uncertainty surrounding system dynamics and exogenous disturbances. To this end, disturbances may be treated in a stochastic or in a worst-case manner. While the usual control-centered approach to MPC considers underlying tasks such as setpoint stabilization and trajectory tracking, in the last years several breakthroughs were obtained in analyzing MPC with generic, i.e. economic cost functions, which do not express control tasks directly. Indeed, the analysis of these schemes uses concepts from the PMP, Dynamic Programming, and dissipativity notions. Moreover, these approaches are of major interest for directly optimizing process economics via closed-loop control in various applications ranging from process industries to energy systems and economics. Yet, from a systems and control point of view, the combination of stochastic uncertainty descriptions with economic objective formulations is not well-explored, despite the fact that combining both approaches promises great potential for applications. In this context, the present project develops new tools for analysis and numerical solution of stochastic optimal control problems and MPC. The proposed research enables the development of novel tools for economic optimization-based control of stochastic nonlinear systems. The focus of the project is on the development and understanding of stochastic dissipativity concepts and their use in analysing the closed-loop performance of stochastic economic MPC schemes. In addition to standard cost functions based on expectation and variance we also investigate risk-aware formulations. The methods are tested on benchmark problems for multi-energy systems, particularly on scheduling problems for energy storage populations under uncertainty.
      16
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    Investigation of Seabed-Anchor Interaction of Floating Offshore Wind Turbines
    In the future, floating offshore wind turbines (FOWTs) will increasingly offer the possibility of generating renewable energy even at locations with great ocean depths. An essential element is the safe and reliable anchoring of these platforms at the sea bed. In practice, a fixed bearing has been assumed up to now. It is well known that this assumption is not conservative. The underlying physical principles of the seabed-anchor interaction have yet not been completely understood. This interaction depends on the anchor type, its installation process, and the drainage conditions of the seabed. Other influences result from load direction and velocity. Yielding and failure results from the complex combinations of wave, current, wind, and operational loads, which pose a challenge to the safe and reliable operation of FOWTs. The deployment of FOWTs is still in its early stages. While there is a transfer of knowledge from the oil and gas industry in certain areas, the aforementioned influences on sedbed-anchor interaction and the interaction itself are largely unexplored. The research project is intended to contribute to a fundamental mechanical understanding of the soil-anchor interaction so that it can be modelled as a component of the design of anchored FOWT. Based on the fundamental research, a simplified drag embedment anchor model (anchor force as a function of anchor chain motion) will be developed to describe the soil-anchor interaction, taking into account anchor geometry, seabed, and load history. The anchor model describes the reaction force of the anchor for a given velocity and direction of the anchor load. To achieve the defined objectives, anchor models will be developed based on the following steps. In the beginning, the movement of the floating offshore platform of a reference installation and the resulting force at the anchor point due to wind, waves and currents will be calculated for different load scenarios. Preliminary numerical investigations follow this to calculate the anchor resistance and displacement considering the seabed-anchor interaction based on a continuum model for the saturated seabed, a rigid body motion for the anchor and possibly the part of the anchor chain on the seabed, and a contact model for the anchor/seabed and chain/seabed interface. Model tests in the centrifuge will validate the numerical model at prototype scale. In addition, the tests will serve to determine the anchor pull-out resistance. A parameter study will be used to investigate the soil-anchor interaction for the reference plant based on the validated FE model. In the final phase of the research project, an anchor model will be developed as a component of future mooring analyses.
      25
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    SPP 2170: Towards control of population heterogeneity induced variations in process performance during L-phenylalanine production with Escherichia coli
    Upscaling of bioprocesses is often accompanied by loss in productivity compared to well-mixed lab-scale processes. One reason is the formation of population heterogeneity whose mechanistic understanding is still comparably low. During the first funding period (project ProPHet) quantitative understanding of population heterogeneity originating from cell-bioreactor interaction in fed-batch processes with E. coli FUS4 (pF81kan) converting glycerol to L-phenylalanine (L-Phe) was raised. Experimental investigation was done with a quadruple reporter strain monitoring growth, general stress response, oxygen availability and product formation of single cells. Cultivations in a well-mixed stirred-tank bioreactor (STR) were compared with large-scale bioprocess conditions simulated in a scale-down reactor system consisting of a STR and a novel by-pass reactor that is designed as a coiled flow inverter (CFI). Theoretical investigation was done with population balance equations and agent based modeling after generating a non-segregated coarse-grained model. Building upon results from ProPHet, ProPHet2Con is aiming at controlling population heterogeneity induced variations in process performance in the fed-batch processes for L-Phe production. A population heterogeneity level that is beneficial for process performance will be maintained by promoting the abundance of subpopulations with advantageous characteristics. A special focus will be put on the process in the STR-CFI. Prio to implementation of process control, the L-Phe production process will be optimized applying the coarse-grained model and stoichiometric modeling to improve final product titer and productivity, especially in the STR-CFI setup. As a prerequisite for influencing the level of population heterogeneity by automated process control, manual at-line flow cytometry measurement will be replaced by automated real-time flow cytometry (ART-FCM). The ART-FCM setup will be advanced by the previously published process concept segregostat, that allows to control the degree of diversification of a bioreactor population at a predefined setpoint by single-cell physiology-based feeding of the carbon source. Furthermore, model-based soft sensors will implemented to estimate quantities cannot be measured directly. Then, based on the outcome of experimental investigations, strategies will be formulated to influence the level of population heterogeneity by model-based process feedback control following ART-FCM evaluation of single cell physiology. To our knowledge such a coupling between ART-FCM and an advanced model-based process control has so far never been realized. The formulated strategies will be experimentally validated in the fed-batch process for L-Phe production in the STR and the STR-CFI.Once process control for L-Phe production is realized, scale-up potential of the process to pilot-scale will be investigated.
      62
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    Multi-method analysis of production planning and control for make-to-order companies
    Despite progress in the theoretical understanding of logistic objectives and production planning and control, the actual achievement of objectives in practice is often unsatisfactory. This raises two questions for the scientific community: (1) Why do companies fail to achieve their logistical goals? (2) How can these causes be systematically recorded and analyzed?The idea of the research project is to further develop and combine proven analysis methods in order to answer the outlined research questions. This will make it possible to (1) completely record the actuating, control and target variables of production control, (2) collect reference, planned and actual processes for the tasks of production control, (3) detect deviations between reference, planned and actual processes and (4) check the data quality. In doing so, the analysis will have to cover the complete order fulfillment process from scheduling of quotations to completion of the order.The evaluation is to be carried out step by step together with five partner companies to ensure the applicability and practical relevance of the method.In a follow-up project, the analysis method is to be used to conduct a comprehensive field study on the practice of production planning and control and to build up a publicly accessible, comprehensive database.
      29
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    Logistic Operating Curves for Ship-to-Shore Cranes
    Container terminals are the central hubs of global trade. Smooth container transport operations require efficient and stable logistical processes. The heart of the container terminal is the ship-to-shore crane. This equipment loads and unloads the vessels and therefore handles the containers between the quayside and the vessel. Together with the vehicles for horizontal transport, the so-called terminal trucks, the ship-to-shore crane is the success-critical work system to achieve logistic objectives. It is therefore all the more surprising that the design and control of this system in practice today is often mostly based on empirical values. Up to now, science has relied on a simulation or mathematical optimisation to design and control ship-to-shore cranes and terminal trucks. In practice, these approaches have not yet been able to establish themselves: Simulations require a great deal of technical effort. The mathematically optimised solutions quickly lose their validity due to disruptions and changes in the operational process. So far, there is no procedure that can represent the conflicting goals with manageable effort, sufficient generality and high accuracy and thus enable the design and control of container terminals. The idea of this research project is to transfer the Funnel Model and Logistic Operating Curves Theory to container terminals. These theories have proven themselves in production already and are able to illustrate the conflicting goals in a simple and clear way. Based on this, a procedure for a model-based design and order assignment for container terminals will be developed. The aim is to ensure high productivity of the ship-to-shore cranes and to achieve short dispatch times for the vessels. Both the modelling and the procedures for design and order assignment will be evaluated in a multi-stage process with the help of simulations.
      89  1
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    Whole Body Magnetic Particle Imaging Measurement Sequences (second funding period)
    Magnetic particle imaging (MPI) is a tomographic imaging technique for superparamagnetic nanoparticles. Currently, MPI is in the preclinical research stage and allows experiments to be performed on small animal models. Since the magnetic field applied in MPI can cause physiological side effects such as tissue heating and nerve stimulation, the measurement field size is limited in practice. For typical scanner and sequence parameters, the measurement field is limited to 1-3 cm side length. Since this is neither sufficient for a full-body measurement of a mouse nor for targeted human applications, MPI needs a component to shift the measurement field. This works with the so-called focus field, which is applied either discretely or continuously at low frequency. Consequently, much higher field amplitudes can be used without stressing the animal/patient. The aim of this project is to develop MPI measurement sequences that allow the measurement of large measurement volumes. For this purpose, the targeted measurement field is divided into several patches, which can be scanned with the fast excitation field. The individual patches are measured successively, while the patch position is shifted by the focus field after or even during each measurement. The project investigates how many patches are needed to scan a predefined measurement volume as fast as possible. In addition to the development of the measurement sequence, the project focuses on the reconstruction of multi-patch measurement data. Since the measurement signals of the individual patches are not independent of each other, the data are reconstructed in a joint step. This reconstruction is time consuming and requires tailored algorithms that exploit the sparse structure of the multi-patch system matrix. Another challenge is the imperfection of the applied magnetic field, which leads to image artifacts if not taken into account. After measuring the field profiles, these are taken into account in the MPI signal equation, thus reducing image artifacts. The developed measurement sequences and data reconstruction algorithms are first evaluated with static and dynamic particle phantoms. In a second step, in-vivo measurement data are considered to show that the developed methods also allow an enlargement of the measurement field in dynamic small animal experiments without reducing the image quality.
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