9,934 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Production Systems Performance Optimization through Human/Machine Collaboration

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    The growth of enterprises is a constant source of research and development of new technologies. Indeed, to stand out from the competition and optimize their production, companies are moving toward the centralization of information and the implementation of machines. This dynamic requires a significant investment in terms of organization and research. Industry 4.0 is therefore at the heart of this reflection, as shown in the literature. It brings together many technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data. This chapter focuses on company performance optimization through a sustainable Industry 4.0 framework involving methodologies such as lean manufacturing and DMAIC, new technologies as robotics, in addition to social, societal, and environmental transformations. This chapter will present robotic displacement solutions adapted to the industrial environment for improving production systems performance. Solutions for human-machine interaction problems such as human-machine interface or flexibility 4.0 will be shown

    Gasificação direta de biomassa para produção de gás combustível

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    The excessive consumption of fossil fuels to satisfy the world necessities of energy and commodities led to the emission of large amounts of greenhouse gases in the last decades, contributing significantly to the greatest environmental threat of the 21st century: Climate Change. The answer to this man-made disaster is not simple and can only be made if distinct stakeholders and governments are brought to cooperate and work together. This is mandatory if we want to change our economy to one more sustainable and based in renewable materials, and whose energy is provided by the eternal nature energies (e.g., wind, solar). In this regard, biomass can have a main role as an adjustable and renewable feedstock that allows the replacement of fossil fuels in various applications, and the conversion by gasification allows the necessary flexibility for that purpose. In fact, fossil fuels are just biomass that underwent extreme pressures and heat for millions of years. Furthermore, biomass is a resource that, if not used or managed, increases wildfire risks. Consequently, we also have the obligation of valorizing and using this resource. In this work, it was obtained new scientific knowledge to support the development of direct (air) gasification of biomass in bubbling fluidized bed reactors to obtain a fuel gas with suitable properties to replace natural gas in industrial gas burners. This is the first step for the integration and development of gasification-based biorefineries, which will produce a diverse number of value-added products from biomass and compete with current petrochemical refineries in the future. In this regard, solutions for the improvement of the raw producer gas quality and process efficiency parameters were defined and analyzed. First, addition of superheated steam as primary measure allowed the increase of H2 concentration and H2/CO molar ratio in the producer gas without compromising the stability of the process. However, the measure mainly showed potential for the direct (air) gasification of high-density biomass (e.g., pellets), due to the necessity of having char accumulation in the reactor bottom bed for char-steam reforming reactions. Secondly, addition of refused derived fuel to the biomass feedstock led to enhanced gasification products, revealing itself as a highly promising strategy in terms of economic viability and environmental benefits of future gasification-based biorefineries, due to the high availability and low costs of wastes. Nevertheless, integrated techno economic and life cycle analyses must be performed to fully characterize the process. Thirdly, application of low-cost catalyst as primary measure revealed potential by allowing the improvement of the producer gas quality (e.g., H2 and CO concentration, lower heating value) and process efficiency parameters with distinct solid materials; particularly, the application of concrete, synthetic fayalite and wood pellets chars, showed promising results. Finally, the economic viability of the integration of direct (air) biomass gasification processes in the pulp and paper industry was also shown, despite still lacking interest to potential investors. In this context, the role of government policies and appropriate economic instruments are of major relevance to increase the implementation of these projects.O consumo excessivo de combustíveis fósseis para garantir as necessidades e interesses da sociedade conduziu à emissão de elevadas quantidades de gases com efeito de estufa nas últimas décadas, contribuindo significativamente para a maior ameaça ambiental do século XXI: Alterações Climáticas. A solução para este desastre de origem humana é de caráter complexo e só pode ser atingida através da cooperação de todos os governos e partes interessadas. Para isto, é obrigatória a criação de uma bioeconomia como base de um futuro mais sustentável, cujas necessidades energéticas e materiais sejam garantidas pelas eternas energias da natureza (e.g., vento, sol). Neste sentido, a biomassa pode ter um papel principal como uma matéria prima ajustável e renovável que permite a substituição de combustíveis fósseis num variado número de aplicações, e a sua conversão através da gasificação pode ser a chave para este propósito. Afinal, na prática, os combustíveis fósseis são apenas biomassa sujeita a elevada temperatura e pressão durante milhões de anos. Além do mais, a gestão eficaz da biomassa é fundamental para a redução dos riscos de incêndio florestal e, como tal, temos o dever de utilizar e valorizar este recurso. Neste trabalho, foi obtido novo conhecimento científico para suporte do desenvolvimento das tecnologias de gasificação direta (ar) de biomassa em leitos fluidizados borbulhantes para produção de gás combustível, com o objetivo da substituição de gás natural em queimadores industriais. Este é o primeiro passo para o desenvolvimento de biorrefinarias de gasificação, uma potencial futura indústria que irá providenciar um variado número de produtos de valor acrescentado através da biomassa e competir com a atual indústria petroquímica. Neste sentido, foram analisadas várias medidas para a melhoria da qualidade do gás produto bruto e dos parâmetros de eficiência do processo. Em primeiro, a adição de vapor sobreaquecido como medida primária permitiu o aumento da concentração de H2 e da razão molar H2/CO no gás produto sem comprometer a estabilidade do processo. No entanto, esta medida somente revelou potencial para a gasificação direta (ar) de biomassa de alta densidade (e.g., pellets) devido à necessidade da acumulação de carbonizados no leito do reator para a ocorrência de reações de reforma com vapor. Em segundo, a mistura de combustíveis derivados de resíduos e biomassa residual florestal permitiu a melhoria dos produtos de gasificação, constituindo desta forma uma estratégia bastante promissora a nível económico e ambiental, devido à elevada abundância e baixo custo dos resíduos urbanos. Contudo, devem ser efetuadas análises técnico-económicas e de ciclo de vida para a completa caraterização do processo. Em terceiro, a aplicação de catalisadores de baixo custo como medida primária demonstrou elevado potencial para a melhoria do gás produto (e.g., concentração de H2 e CO, poder calorífico inferior) e para o incremento dos parâmetros de eficiência do processo; em particular, a aplicação de betão, faialite sintética e carbonizados de pellets de madeira, demonstrou resultados promissores. Finalmente, foi demonstrada a viabilidade económica da integração do processo de gasificação direta (ar) de biomassa na indústria da pasta e papel, apesar dos parâmetros determinados não serem atrativos para potenciais investidores. Neste contexto, a intervenção dos governos e o desenvolvimento de instrumentos de apoio económico é de grande relevância para a implementação destes projetos.Este trabalho foi financiado pela The Navigator Company e por Fundos Nacionais através da Fundação para a Ciência e a Tecnologia (FCT).Programa Doutoral em Engenharia da Refinação, Petroquímica e Químic

    How Sustainable is Machine Learning in Energy Applications? – The Sustainable Machine Learning Balance Sheet

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    Information Systems play a central role in the energy sector for achieving climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research has focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches should be extended to include the application’s energy consumption. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable Machine Learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews

    General Structure of a Digital Control Twin Model for Production and Material Flow

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    This paper is about the general structure of a Digital Control Twin (DCT) Model to regulate production, transportation, and handling of material flow items. At first the basic elements of the general structure and the functionality is described by special terms to achieve a common understanding about the general concept and for a easier discussion of future research. Based on this general approach the elements of a specific DCT-Model for production and material flow processes is explained in more detail. This specific DCT-Model could be a platform for a future ERP-System, especially of material requirement planning as the core module. Then some restrictions and limitations of this specific DCT-Model are treated in short. At the end a short resume and conclusion for further research is made

    Upgrading Urban Services Through BPL: Practical Applications for Smart Cities

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    Current initiatives related to smart cities in LATAM reveal an increasing interest in the improvement of cities and the wellbeing of their citizens. In addition, specific working groups have been created for this purpose. In this sense, the communication technologies set the basis for gathering, transporting, and managing the large amount of data generated in cities to provide a wide range of services. Within the many alternatives available, BPL positions as a promising technology, since smart cities can greatly benefit of its higher data rates and low latency. In addition, since the medium is already deployed and most of the assets and sensors are connected to the same medium, the cost of the communication systems will be reduced in price and simplicity. The work presents four practical applications: smart buildings, urban lighting, energy assets management and broadband access, in which the possibilities and advantages of BPL are further addressed. Finally, some conclusions and key aspects relating BPL to the success of smart cities are identified.Eusko Jaurlaritza IT-1234-19, KK-202
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