8,840 research outputs found

    Redefining Community in the Age of the Internet: Will the Internet of Things (IoT) generate sustainable and equitable community development?

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    There is a problem so immense in our built world that it is often not fully realized. This problem is the disconnection between humanity and the physical world. In an era of limitless data and information at our fingertips, buildings, public spaces, and landscapes are divided from us due to their physical nature. Compared with the intense flow of information from our online world driven by the beating engine of the internet, our physical world is silent. This lack of connection not only has consequences for sustainability but also for how we perceive and communicate with our built environment in the modern age. A possible solution to bridge the gap between our physical and online worlds is a technology known as the Internet of Things (IoT). What is IoT? How does it work? Will IoT change the concept of the built environment for a participant within it, and in doing so enhance the dynamic link between humans and place? And what are the implications of IoT for privacy, security, and data for the public good? Lastly, we will identify the most pressing issues existing in the built environment by conducting and analyzing case studies from Pomona College and California State University, Northridge. By analyzing IoT in the context of case studies we can assess its viability and value as a tool for sustainability and equality in communities across the world

    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

    Delivering district energy for a net zero society

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    District energy systems have been hailed as the cornerstone of any Net Zero carbon energy system, yet there are still distinct operating and design challenges in implementing an efficient and economic system. The novelty of this thesis therefore lies in attempting to providing routes to efficient district energy systems. Many dwellings will be uneconomical to connect to a heat network without significant investment to improve building fabric. This is demonstrated using dynamic modelling of common UK building stock. Transient System Simulation Tool (TRNSYS) is used to demonstrate the criticality of good building fabric on the potential to reduce operating temperatures in district energy networks, and therefore improve the overall system efficiency. It was shown that improving building conditions alone could offer a 30% reduction in space heating energy consumption, while building improvements and heat pumps could see a 70% reductions 5th generation energy networks are considered. Detailed building energy simulation modelling is given to identify indicative heating and cooling profiles of common building types which are then programmed to a linear optimization to identify the benefits of an energy sharing network. Key performance indicators are identified. This is of increasing importance as network designers begin to grapple with energy sharing network design considerations. The work showed the potential to reduce the levelised cost of energy by 69%, and carbon emissions by 13%. The critical finding however, was that thermal energy storage has the largest impact on energy sharing capability. To further validate the key performance indicator concepts, a more detailed non-linear optimization is given which discusses in greater detail the role of operating temperatures and flowrates on the system design. It was shown that traditional metrics become disconnected from ambient loop networks (e.g. linear demand density). The overall conclusions of the thesis show that although heat networks have suffered poor performance in the past, there are clear paths to improve this. However, this depends on choosing the correct connections to the network and understanding how to optimize for retrofit demands

    Unlocking the potential for thermal energy storage in the UK

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    Rapid and deep energy system decarbonisation is essential to a safe future. Thermal energy storage may hold the key to significant carbon reduction of the heating, cooling and electricity sectors, but the UK remains largely locked in to a fossil-fuel based heating regime. Global urbanisation trends mean cities are crucial to the net-zero transition. This thesis provides a sociotechnical analysis of current and future thermal storage deployment, recognising that fundamental change is complex and involves individuals and companies, supply chains, infrastructures, markets, policy and regulation, norms and traditions. I explore this through the overarching research question: How can cities unlock the potential for thermal energy storage to support the UK’s net-zero transition? The work is presented through three empirical chapters. A pilot study used a survey, thematic analysis, and pre-existing sociotechnical frameworks to explore the current state of UK thermal storage deployment and how sociotechnical characteristics are shaping current and future deployment prospects. A case study of a particular storage approach known as geoexchange analyses the results of interviews with geoexchange practitioners using sociotechnical frameworks, and proposes a new critical success factors framework. Finally, a comparative case study of two UK cities explores the specific role of local authorities to use powers at their disposal within a common planning framework to support the deployment of urban shared ground heat exchange in residential and mixed-use developments. Based on this study, a framework for local policy, support and enforcement activities is proposed. Applied contributions are provided through new knowledge on sociotechnical factors shaping the prospects for TES to support the net-zero transition, the first sociotechnical analysis of UK geoexchange deployment, and policy and practice proposals to support city-based shared ground heat exchange. Theory is advanced through application, testing and development of several existing frameworks for understanding sociotechnical change. Based on empirical evidence, two novel frameworks are proposed to support deployment of geoexchange and shared ground heat exchange

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    Equity research - EDP Renováveis S.A. : the value of financial flexibility

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    Mestrado Bolonha em FinançasThe present document is the report of an Equity Research of EDP Renováveis, S.A. (EDPR). EDPR is a leading global renewable energy company, that develops, constructs, and operates onshore wind farms, solar energy plants, and offshore wind projects to deliver clean energy to its customers in over 20 countries. This report applies a DCF FCFF Sum-of-the-Parts approach to capture all the characteristics and risks of each segment. We issue a buy recommendation for EDPR, with a 2022YE price target of €24.7/share. This valuation implies a 24% upside potential from the January 12th, 2022 closing price of €19.9, with medium-low risk. Other valuation approaches were used to confirm the robustness of the analysis. Additionally, the valuation was subject to multiple sensitivity analyses to address its risk. Following the original research, a complementary approach was carried out to analyse EDPR’s Financial Flexibility. By using a Real Options approach with the BlackScholes-Merton model, we value the benefits of staying underlevered and provide a framework to support management’s decisions on the design of the capital structure. Our analysis shows that companies with certain characteristics may benefit a lot from financial flexibility. Specifically, EDPR distinguishes itself from its peers by maintaining flexibility in a current scenario of growth and uncertainty. The value of financial flexibility is not usually accounted for explicitly in the stock price. Thus, we update our recommendation to capture EDPR’s unique strategy in its industry. The report was finalized on January 16th, 2022. By March 2022, an updated snapshot was created, to reflect the impacts of the military invasion of Ukraine.O presente documento consiste num relatório de Equity Research sobre a EDP Renováveis, S.A. (EDPR). A EDPR é uma empresa líder global de energia renovável, que desenvolve, constrói e opera parques eólicos onshore, parques de energia solar e projetos eólicos offshore para fornecer energia limpa aos seus clientes. Neste relatório é aplicada uma abordagem DCF FCFF Soma das Partes de modo a capturar todas as características e riscos de cada segmento. Emitimos uma recomendação de compra para a EDPR, com um preço-alvo de €24.7/ação no final do ano de 2022. Esta avaliação implica um potencial de valorização de 24%, face ao preço de fecho a 12 de Janeiro de 2022 de €19.9, com um nível de risco médio-baixo. Outros métodos de avaliação foram usados para confirmar a robustez da análise. Adicionalmente, a avaliação foi sujeita a várias análises de sensibilidade a fim de abordar o seu risco. Foi realizada uma abordagem complementar para analisar a Flexibilidade Financeira da EDPR. Usando uma abordagem de Real Options com o modelo Black-ScholesMerton, avaliamos os benefícios de permanecer sob-alavancado e fornecemos uma estrutura para apoiar as decisões da gestão no que diz respeito à estrutura de capital. A nossa análise mostra que empresas com certas características podem beneficiar bastante ao mostrar flexibilidade financeira. Especificamente, a EDPR distingue-se dos seus peers ao manter flexibilidade num cenário atual de crescimento e incerteza. Normalmente, o valor da flexibilidade financeira não é incorporado explicitamente no preço da ação. Logo, atualizamos a nossa recomendação de modo a capturar a estratégia diferenciadora da EDPR na sua indústria. Este relatório foi finalizado no dia 16 de janeiro de 2022. Em março de 2022, foi criado um snapshot atualizado, para refletir os impactos da invasão militar à Ucrânia.info:eu-repo/semantics/publishedVersio

    POSSIBLE USAGE OF VARIABLE REFRIGERANT FLOW IN ARID CLIMATE: TECHNICAL AND MANAGEMENT PERSPECTIVE

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    All over the world, there is a call to encourage sustainable energy thinking and implementation. In the heating, ventilation and air conditioning field, the rise of the variable refrigerant flow systems has made a big progress throughout the years. This study presents a life-cycle cost analysis to evaluate the economic feasibility of constant refrigerant flow (CRF) in particular the conventional ducted unit air conditioning system that is widely used in Qatar and the variable refrigerant flow (VRF) system. Detailed cooling load profiles will be used for the existing units and the new VRF model in addition to initial, operating, and maintenance costs. Two operating hours scenarios are utilized to consider 12 and 24 operating hours and the present-worth value technique for life-cycle cost analysis is applied to an existing office building located in Qatar which can be conditioned by CRF and VRF systems. The results indicate that although the initial cost of the VRF system is higher than that of the CRF system, the present-worth cost of the VRF system is lower than that of the CRF system at the end of the lifetime due to lower operating costs. The implementation of these results on a national scale will promote the use of sustainable energy technologies such as the variable refrigerant flow system to reduce the energy consumption in Qatar and to improve the national power grid utilization, efficiency, and expansion in the coming years

    Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

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    In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances
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