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Urban air pollution has become a pressing challenge in recent times, demanding innovative solutions. This review delves into the potential of Solar Chimney Power Plants (SCPPs) as a sustainable approach to mitigating air pollution. The idea of mitigation of pollution may be an added advantage to the use of SCPPs in practice. Recent advancements, such as the integration of photocatalytic reactors (PCRs) for the elimination of greenhouse gases (GHGs), emphasizing the importance of addressing non-CO2 GHGs like CH4 and N2O are analyzed. The novelty of this review is that it not only focuses on the shifting and removal of particulate matter but also on the removal of greenhouse gases. Numerous case studies, ranging from filter-equipped SCPPs to Solar-Assisted Large-Scale Cleaning Systems (SALSCSs), are reviewed, providing a comprehensive understanding of their design, performance, and potential benefits. This review serves as a guide for researchers and policymakers, emphasizing the need for multifaceted approaches to address the intricate nexus of air pollution, renewable energy generation, and climate change mitigation.
The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.
Die katalytische Methanisierung von Kohlendioxid (CO2) mit Hilfe von elektrolytisch erzeugtem Wasserstoff (H2) aus erneuerbaren Energiequellen kann einen wichtigen Beitrag zur Reduktion von CO2-Emissionen leisten. Aufgrund einer immer stärker fluktuierenden Energie- und Rohstoffbereitstellung spielt die Möglichkeit einer flexiblen, dynamischen Betriebsweise eine zunehmend große Rolle für zukünftige Technologien. Dies bedingt auch im Fall der katalytischen Methanisierung umfangreiche Untersuchungen, um zu ermitteln, inwieweit eine dynamische Fahrweise Auswirkungen auf Prozess und Katalysator besitzt. An diesem Punkt setzte das HiFF-Projekt Power2Gas an.
So wurden verschiedene nickelbasierte Katalysatorsysteme synthetisiert, wobei sich der via Imprägnierung hergestellte Ni/Al2O3-Katalysator sowohl aufgrund seiner guten Leistung als auch der praktikablen und reproduzierbaren Herstellung als geeignetes Benchmark-System für weitere Untersuchungen - vor allem im kinetischen Bereich - erwies. Während umfassender Parametervariationen wurden kinetische Datensätze im stationären Zustand bestimmt, die zur Modellierung der katalysierten CO2-Methanisierung verwendet werden können. Darüber hinaus wurden in ausgewählten Versuchsreihen die isotherme Temperaturkontrolle und eine ausreichende Langzeitstabilität sichergestellt sowie makrokinetische Diffusionsbeschränkungen ausgeschlossen.
Die Ergebnisse wurden in einer peer-review-Publikation veröffentlicht und auf zwei Konferenzen präsentiert. Sie waren zudem die Basis für eine erfolgreiche Masterarbeit und flossen in die Antragstellung im Rahmen des DFG-Programms „Großgeräteaktion für Hochschulen für Angewandte Wissenschaften“ ein.
This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (hi = 1.85 to 0.1 m), chimney inlet diameter (dch = 10.16 to 55.88 m), and chimney outlet diameter (do = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of hi = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5dch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.
Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.
Knowledge is our most valuable asset! Due to the increasing fluctuation caused by demographic change, the change in society and the industry, companies are increasingly confronted with knowledge management. Therefore, companies have problems with carrying out projects effectively. There are several models, methods and tools to develop an effective concept for knowledge management. Can the process capability of projects be ensured despite high fluctuation? What appropriate methods and tools are available to manage knowledge? These and more questions are clarified in the course of this work and presented in a conceptual proposal.
Parabolrinnen nutzen parabolisch gekrümmte Spiegel, um die direkte Globalstrahlung auf ein Vakuum-Absorberrohr zu konzentrieren. Eine Effizienzsteigerung dieser Technologie wird durch eine vollständige Automatisierung der Sonnennachführung optimiert. Die an einem Parabolrinnen-Versuchsträger der Hochschule Düsseldorf realisierte Steuerung und Berechnung der Nachführung werden kontinuierlich über einen Microcontroller realisiert. Eine Kalibrierung der Parabolrinne im Labormaßstab zur Erkennung des Winkels erfolgt über Infrarotsensoren. Die Berechnung des Sonnenstandes, durch den Algorithmus, kann unabhängig von einer Stromzufuhr durch eine Real-Time-Clock bestimmt werden. Mittels Schrittmotor und Treiber verfährt die Parabolrinne in die errechnete Position. Die Feinausrichtung der Parabolrinne erfolgt über ein Verschattungsmodul und zwei Photowiderstände. Durch den Einsatz von 3D-gedruckten Bestandteilen sind die genutzten Komponenten kostengünstig und können standortunabhängig reproduziert werden. Durch das Zusammenspiel der Komponenten ist ein autonomer Einsatz möglich.
Rotary mowers are ofen used for forage harvesting in agriculture, as this technology can achieve a high throughput. These knives are therefore exposed to high levels of wear and tear, and will also be ofen changed because of loss of material by bad cutting quality. That
is why it is interesting to save costs during manufacturing in spite of a correspondingly long service life. This research records the current manufacturing quality of the mower blades and examines how they behave in comparison. Based on the obtained results, further decisions on the methods, procedures and ways of extending the life of the disc mower blades should be proposed. For the investigations, a test bench based on a wear pot was used. The wear that occurs on the blade is determined using several parameters and compared accordingly. The parameters of the test setup determined as a result of the test can be used for further studies of wear resistance.
Artificial intelligence and design of experiments for resource adequacy assessment in power systems
(2024)
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).
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.
Magnetic Resonance (MR) elastography is a method for measuring tissue elasticity via phase images acquired with an MR scanner. The propagation of periodic mechanical waves through the tissue can be captured by means of a modified phase contrast sequence. These waves are generated with a mechanical oscillator (actuator) and coupled into the tissue through the skin. The actuator must be capable of generating a sinusoidal excitation with excellent phase and amplitude stability, while not disturbing the MR imaging process. In this work, an actuator based on a piezoelectric principle was developed. Based on the imaging evaluation of several material samples, the housing for the piezoelectric ceramic was constructed of aluminum. Smaller parts of the housing were manufactured from brass and titanium to fulfill the mechanical constraints. A lever was used to transfer the oscillation generated by the piezoelectric ceramic to the point of excitation. The lever amplifies the piezoelectric motion, allowing for a more compact design. Three different lever designs were characterized by an acceleration sensor both outside and inside the magnet. It was shown that the rigidity of the lever, as determined by its material and form, was decisive in determining the resonant frequency of the system and therefore the maximum practical frequency of operation. It was also shown that the motion of the oscillator is unaffected by the electromagnetic fields of the MR imager. The final design can be placed directly in the magnet bore within a few centimeters of the tissue volume to be imaged without generating significant artifacts. An amplitude range of 0–1 mm in the frequency range from 0 to over 300 Hz was achieved, sufficient for performing most MR elastography applications. © 2002 Wiley Periodicals, Inc. Concepts in Magnetic Resonance (Magn Reson Engineering) 15: 239–254, 2002
Men afflicted with hypogonadism have reduced fat-free mass and in some cases a loss in muscle strength. We hypothesize that muscle elasticity of the lower extremities differs between patients with hypogonadism and healthy controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in applied force.
Men afflicted with chronic obstructive pulmonary disease (COPD) show signs of muscle dysfunction, such as increased muscle fatigue and acidosis during exercise. We hypothesize that muscle elasticity of the lower extremities differs between patients with COPD and age-related controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in the maximum applied force.
The Chylla–Haase polymerization reactor is widely accepted as a benchmark process for the evaluation of control strategies for batch reactors. In this contribution a control concept based on Exact I/O-Linearization is proposed and compared to a conventional cascade control structure. In order to adapt the exact linearization control strategy to various polymerization products and batch conditions, an advanced probabilistic inference algorithm (Sigma-Point Kalman Filter) is applied and investigated. Sigma-Point Kalman Filters have the major improvement of simplified implementation compared to local linearization methods (i.e. Extended Kalman Filter) because no analytical Jacobians are required. Stochastic simulation studies are introduced and show the effectiveness, accuracy and benefit of the control concept. Within several scenarios a satisfying robustness against structural errors in the underlying model equations for the nonlinear control law and the inference algorithm is demonstrated. Furthermore it is pointed out, that with little effort in reassembling the plant design, control performance can be improved significantly.
This article deals with the cost optimal commitment of power systems. System stability is often only considered after solving the commitment problem. We show by example that a cost optimization may result in a mode of operation that, while economically optimal, is unstable or has otherwise unacceptable dynamics. As a remedy, we propose to use the so-called normal vector method, which has been developed for the optimization of nonlinear dynamical systems with stability boundaries and uncertain parameters. We apply the method to a small sample power grid, where the optimization goal is to minimize the energy production costs for a given power consumption.
Stability and cost optimality of power generation and supply systems must not be considered independently from one another. Simple examples show that optimizing cost without considering stability may result in modes of operation that, while economically optimal, are unstable. We demonstrate that stability, robustness, and optimality can be considered systematically and simultaneously by combining bifurcation theory and nonlinear optimization. Essentially, the proposed method enforces a backoff distance between the optimal point of operation and operational or stability boundaries in the space of the optimization variables, where bifurcation theory is used to describe nonlinear stability boundaries.
We present a control scheme for a parabolic trough power plant that is equipped with a molten salt thermal energy storage system. We show that the multivariable control problem for the solar field can be decoupled by controlling the three way valve that splits the heat transfer fluid between the storage system and the steam generator. The steam generation cycle is regulated with inlet pressure control. The subsystems of the plant are modeled based on first principles, where we focus on the steam generation cycle. A multi-group extraction turbine with pre- and reheater is considered, which is suitable for solar power plants. We demonstrate the benefits of the proposed control scheme with a day-to-day simulation.
We derive a nonlinear model of the steam cycle of a solar power plant, estimate its unknown parameters with measured data, and design a linear model predictive controller based on the resulting model. Simulations show that the linear model predictive controller is able to regulate the electrical power of the nonlinear steam process of the plant to a given reference trajectory very well. Moreover, it results in very good disturbance rejection.
Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual’s eyes. It allows for an evaluation of the individual’s gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness.
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.
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.
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.
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
Computational investigation of oxy-combustion of pulverized coal and biomass in a swirl burner
(2022)
Experimental investigation of heat transfer performance of corrugated tube with spring tape inserts
(2019)