1,621 research outputs found

    Adjustable robust optimization with nonlinear recourses

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    Over the last century, mathematical optimization has become a prominent tool for decision making. Its systematic application in practical fields such as economics, logistics or defense led to the development of algorithmic methods with ever increasing efficiency. Indeed, for a variety of real-world problems, finding an optimal decision among a set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the research community raised more and more attention to the role of uncertainty in the optimization process. In particular, one may question the notion of optimality, and even feasibility, when studying decision problems with unknown or imprecise input parameters. This concern is even more critical in a world becoming more and more complex —by which we intend, interconnected —where each individual variation inside a system inevitably causes other variations in the system itself. In this dissertation, we study a class of optimization problems which suffer from imprecise input data and feature a two-stage decision process, i.e., where decisions are made in a sequential order —called stages —and where unknown parameters are revealed throughout the stages. The applications of such problems are plethora in practical fields such as, e.g., facility location problems with uncertain demands, transportation problems with uncertain costs or scheduling under uncertain processing times. The uncertainty is dealt with a robust optimization (RO) viewpoint (also known as "worst-case perspective") and we present original contributions to the RO literature on both the theoretical and practical side

    Mathematical Multi-Objective Optimization of the Tactical Allocation of Machining Resources in Functional Workshops

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    In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers and to maintain control of the tied-up working capital. We introduce new multi-item, multi-level capacitated resource allocation models with a medium--to--long--term planning horizon. The model refers to functional workshops where costly and/or time- and resource-demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimize the maximum excess resource loading above a given loading threshold while incurring as low qualification costs as possible and minimizing the inventory.In Paper I, we propose a new bi-objective mixed-integer (linear) optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. In Paper II, we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. In Paper III, we extend the TRAP with an inventory of semi-finished as well as finished parts, resulting in a tri-objective mixed-integer (linear) programming model. We create a criterion space partitioning approach that enables solving sub-problems simultaneously. In Paper IV, using our knowledge from our previous work we embarked upon a task to generalize our findings to develop an approach for any discrete tri-objective optimization problem. The focus is on identifying a representative set of non-dominated points with a pre-defined desired coverage gap

    Simulation of metal powder packing behaviour in laser-based powder bed fusion

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    Laser-based powder bed fusion (L-PBF) is a method of additive manufacturing, in which metal powder is fused into solid parts, layer by layer. L-PBF shows high promise for manufacture of functional Tungsten parts, but the development of Tungsten powder feedstock for L-PBF processing is demanding and expensive. Therefore, computer simulation is explored as a possible tool for Tungsten powder feedstock development at EOS Finland Oy, with whom this thesis was made. The aim of this thesis was to develop a simulation model of the recoating process of an EOS M 290 L-PBF system, as well as a validation method for the simulation. The validated simulation model can be used to evaluate the applicability of the used simulation software (FLOW-3D DEM) in powder material development, and possibly use the model as a platform for future application with Tungsten powder. In order to reduce complexity and uncertainties, the irregular Tungsten powder is not yet simulated, and a well-known, spherical EOS IN718 powder feedstock was used instead. The validation experiment is based on building a low, enclosed wall using the M 290 L-PBF system. Recoated powder is trapped inside as the enclosure is being built, making it possible to remove the sampled powder from a known volume. This enables measuring the powder packing density (PD) of the powder bed. The experiment was repeated five times and some sources of error were also quantified. Average PD was found to be 52 % with a standard deviation of 0.2 %. The simulation was modelled after the IN718 powder and corresponding process used in the M 290 system. Material-related input values were found by dynamic image analysis, pycnometry, rheometry, and from literature. PD was measured with six different methods, and the method considered as most analogous to the practical validation experiment yielded a PD of 52 %. Various particle behavior phenomena were also observed and analyzed. Many of the powder bed characterization methods found in literature were not applicable to L-PBF processing or were not representative of the simulated conditions. Many simulation studies were also found to use no validation, or used a validation method which is not based on the investigated phenomena. The validation model developed in this thesis accurately represents the simulated conditions and is found to produce reliable and repeatable results. The simulation model was parametrized with values acquired from practical experiments or literature and closely matched the validation experiment, and could therefore be considered a truthful representation of the powder recoating process of an EOS M 290. The model can be used as a platform for future development of Tungsten powder simulation

    Modeling and simulation of fixed-bed reactors made of metal foam pellets

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    Offenzellige MetallschĂ€ume werden hĂ€ufig als KatalysatortrĂ€ger fĂŒr katalytische Gasphasenreaktionen verwendet, da sie hervorragende Transporteigenschaften aufweisen. Aktuelle Fortschritte in den Herstellungstechniken haben zur Entwicklung von legierten SchĂ€umen (z. B. NiCrAl, FeCrAl) mit verbesserter thermischer StabilitĂ€t gefĂŒhrt, die zu Drop-in Pellets fĂŒr Festbettreaktoren geformt werden können. Die Metallschaum-Pellets gelten als vorteilhafte Alternative zu keramischen KatalysatortrĂ€gern, auch fĂŒr den Einsatz in Festbettrohrreaktoren fĂŒr großtechnische Prozesse wie die Dampfreformierung von Methan. Die gewundene Zellstruktur, die Strömungen innerhalb und zwischen den Partikeln in Verbindung mit den lokalen Effekten der Festbettstrukturen fĂŒhren jedoch zu komplexeren TransportphĂ€nomenen bei Festbetten aus Metallschaumpellets im Vergleich zu Feststoffpellets. Daher ist es wichtig, ein grundlegendes VerstĂ€ndnis der zugrunde liegenden Transportprozesse zu haben, um die optimale Form der Metallschaumpellets fĂŒr eine spezifische Betriebsbedingung zu bestimmen. In dieser Arbeit wird eine modifizierte Version des Partikelaufgelösten numerische Strömungsmechanik-Ansatzes prĂ€sentiert, um die Transportprozesse, insbesondere die Strömung und den radialen WĂ€rmetransport, in schlanken Festbettreaktoren aus Metallschaumpellets zu untersuchen. Das synthetische Festbett wird mit der Rigid Body Dynamics (RBD)-Methode generiert, und die TransportgrĂ¶ĂŸen werden in den ZwischenrĂ€umen vollstĂ€ndig dreidimensional aufgelöst. Die Strömung und der WĂ€rmetransport im Inneren der Metallschaumpellets werden jedoch durch den Ansatz ĂŒber ein poröses Medium unter BerĂŒcksichtigung geeigneter Submodelle behandelt. FĂŒr die DurchfĂŒhrung von Experimenten zum Druckverlust und der WĂ€rmeĂŒbertragung wurden Pilotmaßstab-Reaktoren gebaut. Die CFD-Simulationen zeigen eine sehr gute Übereinstimmung mit den experimentellen Daten. Als Ergebnis wurde eine virtuelle Designplattform entwickelt, die es ermöglicht, den Einfluss verschiedener Formen und Morphologien von Metallschaumpellets sowie von Betriebsbedingungen wie Durchflussraten, Einlass- und Wandtemperaturen auf die Transportprozesse in solchen Festbettreaktoren zu untersuchen. Zur Optimierung der Metallschaumpellets wird die Gesamtleistung verschiedener Pelletkonfigurationen auf der Grundlage der wĂŒnschenswerten Eigenschaften eines Festbettreaktors, darunter niedriger Druckverlust, hoher WĂ€rmeĂŒbergangskoeffizient, vergrĂ¶ĂŸerter OberflĂ€che sowie hohe Katalysatorbeladung, analysiert. DarĂŒber hinaus erfolgt eine umfassende Analyse der zugrunde liegenden WĂ€rmeĂŒbertragungsmechanismen mithilfe von experimentellen Daten und Simulationen. Dies ermöglicht die Entwicklung von Korrelationen fĂŒr kritische WĂ€rmetransportparameter wie die effektive radiale BettleitfĂ€higkeit und die Wand-Fluid-Nusselt-Zahl. Abschließend wird ein vereinfachter CFD-Ansatz zur Modellierung katalytischer Schaumpellets vorgestellt, der auch die externen und internen StoffĂŒbergangswiderstĂ€nde in einem beschichteten Schaumpellet berĂŒcksichtigt.Open-cell metal foams have been widely used as catalyst supports for gas-phase catalytic reactions, as they exhibit excellent transport characteristics. Recent advancements in manufacturing techniques have led to the development of alloyed foams (e.g., NiCrAl, FeCrAl) with improved thermal stability, and these can be shaped into drop-in pellets for fixed-bed reactors. The metal foam pellets are regarded as a beneficial alternative to ceramic catalyst supports, also for the use in tubular fixed-bed reactors for large-scale processes like steam methane reforming. However, the tortuous cellular structure, intraparticle and inter-particle flows, combined with local bed structure effects, result in more complex transport phenomena for fixed-beds made of metal foam pellets, compared with solid pellets. Therefore, a thorough understanding of the underlying transport processes is important to find the optimal metal foam pellet shape relevant to a particular operating condition. This thesis presents a modified version of the particle-resolved Computational Fluid Dynamics (PRCFD) approach to investigate the transport processes, particularly flow and radial heat transport, in slender fixed-bed reactors made of metal foam pellets. The synthetic bed structure is generated using the Rigid Body Dynamics (RBD) method, and the transport quantities are fully resolved three-dimensionally in the interstitial spaces. The flow and heat transport inside the metal foam pellets are modeled, however, by the porous-media approach with appropriate sub-models. Pilot-scale reactors were built to conduct pressure drop and heat transfer experiments. The CFD simulations show very good agreement with experimental data. As a result, a virtual design platform has been realized for exploring the influence of different shapes and morphologies of metal foam pellets, as well as operating conditions, such as flow rates, inlet and wall temperatures, on transport processes in such fixed-bed reactors. To optimize the foam pellet shape, the overall performance of different pellet configurations is analyzed, based on the desirable properties of a fixed-bed reactor, such as low pressure drop, high heat transfer coefficient, increased surface area, and high catalyst inventory. Furthermore, a thorough analysis of the underlying heat transfer mechanisms is carried out with the aid of experimental data and simulations. This results in the development of correlations for critical heat transport parameters such as effective radial bed conductivity and wall-fluid Nusselt number. Finally, a simplified CFD approach to model catalytic foam pellets is illustrated, which also considers the external and internal mass transfer resistances in a washcoated foam pellet

    Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

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    Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    Towards trustworthy computing on untrustworthy hardware

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    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level
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