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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.
