6,030 research outputs found

    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

    The Reputations of Sir Francis Burdett

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    Managing Technology Transfer Challenges in the Renewable Energy Sector within the European Union

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    The use of fossil fuels to generate energy is often associated with serious negative effects on the environment. The greenhouse gas emissions resulting from burning these fuels destroy the ozone layer and lead to global warming. As a strategic approach to the solution of this problem, calls for research and development, as well as the implementation of technologies associated with renewable energy sources within the European Union (EU), have intensified in recent years. One of the keys to a successful outcome from this intensified effort is to identify the challenges associated with the transfer of both intellectual property and technology rights in the renewable energy sector within the EU. The present paper contributes towards this direction. Firstly, data from the literature were used to identify contemporary trends within the European Union with regards to technology transfer and intellectual property within the sector of renewable energy. Then, a statistical analysis utilising an ordinary least squares (OLS) model was conducted to establish a correlation between renewable energy innovations (research and development) and the level of investment associated with renewable energy technologies. Finally, this correlation, along with the associated challenges, was then critically explored for four of the most popular renewable energy sources (namely solar energy, biomass, wind energy, and marine renewable energy), and conclusions are reporte

    ‘Twenty hearts beating as none’: primary education in Ireland, 1899-1922

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    At the dawn of the twentieth century, the Irish national school system catered for the educational needs of almost 800,000 children in 8,500 schools. Despite its manifest numerical success and its agency in the near elimination of illiteracy, issues such as clerical management, the payment by results system, inferior school conditions, the proliferation of small schools, the restricted curriculum, the teaching of Irish and the reorganisation of the inspectorate generated a confluence of challenging circumstances for all participants. This was the scenario presented to Dr William Starkie, academic and classical scholar, who was appointed Resident Commissioner of Education in 1899. This study charts the fortunes of the national school system from 1899 to 1922, a period roughly coinciding with the tenure of Dr W.J.M. Starkie as Resident Commissioner of National Education. This commenced with an active programme of curricular and administrative reform that served to modernise primary education in Ireland, which had lagged behind systems elsewhere. Parallel with this programme of change, there were strong intimations that the British government harboured plans to reform Irish education and its administration along the de facto lines recently pursued in England. As the primary education system in Ireland had evolved into a denominational one, financed by government but clerically managed, the various Churches were in the main generally satisfied. As a result, every suggestion that schools be financed by rates and under local control was stoutly resisted. Successive chief secretaries failed to progress this policy. Furthermore, Starkie’s energetic approach to administrative reform not only encountered opposition, it generated additional problems. The new system of pay, increments and promotion for teachers, introduced in tandem with the Revised Curriculum, and combined with a changed inspectoral remit proved problematic, with the result that although curricular reform was successfully introduced, progress was disrupted by financial and organisational issues. Two vice-regal inquiries, in 1913 and 1918, delved minutely into primary education provision under the National Board. These highlighted the scale of the deficiencies of the existing system and provided the impetus, had it been fully grasped, for further organisational and administrative change. The outbreak of the First World War in 1914 ensured the matter was put on the back burner for the duration, and when it was taken up again, in its immediate aftermath, it was too late. A final attempt was made in 1918 20 to address the structural deficiencies of the Irish educational system. Had this been achieved, it would have resulted in the replacement of the National Board, which was no longer fit for purpose, by a state Department of Education in the manner of that already in place in Great Britain. This was not possible in Ireland because of political and ideological developments that heralded the breakup of the Union. The rise of cultural nationalism, and with it the Gaelic League, had brought increasingly exigent calls for the introduction of a bilingual programme of education. These were addressed at first by curricular accommodation, but the 1916 Rising raised nationalist aspirations. When it came to education provision, nationalists and the Catholic Church increasingly found common cause in the late 1910s and, as a new political disposition beckoned, the alliance forged was a hallmark f or the future in which the churches and the Catholic Church in particular were permitted to retain their ascendant position in the provision of education and the state acceded to an essentially subordinate, administrative position

    Techno-economic analysis for local hydrogen production for energy storage and services

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    The energy industry is quickly changing, with more renewable energy technologies emerging and sustainable sources growing in their capacities, which is slowly reducing the need for fossil fuel sourced energy supply. But with it come challenges, with energy storage becoming increasingly more important to help balance the gap between the energy supply and demand. The interest in hydrogen has accelerated in recent years as it can be used for several end uses, for example power-to-power, power-to-gas and power-to-fuel. It could therefore potentially decarbonise several industries, not just the energy sector. For hydrogen produced by renewables through water electrolysis to become competitive, the issues of low roundtrip efficiencies, high costs and the need of scaling up a new infrastructure needs to be addressed. This research project is a collaboration between University of Edinburgh and Bright Green Hydrogen (BGH). BGH is a non-for-profit company that created and launched the Levenmouth Community Energy Project (LCEP) in 2014 (operational from 2017) to explore electrolytic hydrogen’s ability to decarbonise energy supplies. The LCEP consists of: 750 kW wind turbine, 48 kW roof PV, 112 kW ground PV, 250 kW PEM electrolyser, 100 kW PEM fuel cell, two 60 kW hydrogen refuellers and a total of 17 hydrogen vehicles of three different models. This project used the data, information and observations from the LCEP to build an energy system model that included hydrogen with real-world aspects. The model was used to explore different ways that the economics and self-reliance for energy of small-scale hydrogen systems can be improved by conducting a techno-economic analysis on a number of alterations. The electrolyser control system was improved to help the electrolyser behave more energy efficiently, components were changed in sizing and a Lithium-ion battery was added into the model to help optimising the main electrolyser’s performance. The first novelty of this work was a new electrolyser model that was developed specifically to account for energy consumption and hydrogen production at low load, which appeared frequent and significant in this type of system. The model was found to represent the plant data better than existing ones. One general conclusion from this work was the impact of operation at low load, which is difficult to avoid at all times and yet should be minimised for good technical and economic performance. The second contribution to knowledge in this work is the methods and findings of the technoeconomic assessment. Several possible improvements were explored to find a balance in techno-economic performance of the small-scale hydrogen production facility. It was found that a control system that made adequate use of forecast weather and energy supply data was critical for effective and efficient use of the electrolyser, without excessive shutdown time and parasitic loss at times of low energy supply. In addition, changes in the respective capacities of the components (electrolyser, storage, solar energy supply) for the same demand could result in significant improvements in economic performance, and so could the incorporation of batteries within the system in support of the electrolyser. Batteries helped both electrolyser standby load (to help with grid independence) and hydrogen production (to improve electrolyser’s output). However, there is a balance between battery storage size and system benefits. In the particular case of the LCEP as built, the system struggled to perform well while it had two end uses (energy storage for buildings and fuel for vehicles) without more energy and hydrogen supply. Also, the main electrolyser was oversized for its needs, resulting in poor capacity utilization and high parasitic load. But a significantly smaller electrolyser with sufficient storage had a notable technical benefit to the system. Finally, there were several adjustments that could lead to a technically well-performing smallscale hydrogen system, but none that made it economically feasible. Capital costs, operating costs, maintenance costs, major replacement costs and durability of components are still major factors that need to be addressed for hydrogen at this scale to be feasible. However, this work clearly identified required areas of progress to achieve economic viability without subsidies, in particular, improving the longevity of the electrolyser and fuel cell stacks would alone enable a positive Net Present Value. In addition, recent and ambitious policy decisions and more widely deployed demonstration projects can stimulate volumes of productions of these components, and the significant cost reductions that these would allow

    Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results

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    We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors

    Optimal demand-supply energy management in smart grids

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    Everything goes down if you do not have power: the financial sector, refineries and water. The grid underlies the rest of the country’s critical infrastructure. This thesis focuses on four specific problems to balance demand-supply gap with higher reliability, efficiency and economical operation of the modern power grid. The first part investigates the economic dispatch problem with uncertain power sources. The classic economic dispatch problems seek thermal power generation to meet the demand most efficiently. However, this project exploits two different power sources such as wind and solar power generation into the standard optimal power flow framework. The stochastic nature of renewable energy sources (RES) is modeled using Weibull and Lognormal probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. The calculation of best power dispatch is proposed using a cost function. The second part investigates demand-side management (DSM) strategies to minimize energy wastage by changing the time pattern and magnitude of utility load at the consumer side. The main objective of DSM is to flatten the demand curve by encouraging end-users to shift energy consumption to off-peak hours or to consume less power during peak times. It is more appropriate to follow the generation pattern in many cases instead of flattening the demand curve. It becomes more challenging when the future grid accommodates the penetration of distributed energy resources in a greater manner. In both scenarios, there is an ultimate need to control energy consumption. Effective DSM strategies would help to get an accurate balance between both ends, i.e., the supply-side and demand-side, effectively reducing power demand peaks and more efficient operation of the whole system. The gap between power demand and supply can be balanced if power peak loads are minimized. The third part of the thesis then focuses on modeling the consumption behavior of end-users. For this purpose, a novel artificial intelligence and machine learning-based forecasting model is developed to analyze big data in the smart grid. Three modules namely feature selection, feature extraction and classification are proposed to solve big data problems such as feature redundancy and high dimensionality to generate quality data for classifier training and better prediction results. The last part of this thesis investigates the problem of electricity theft to minimize non technical losses and power disruptions in the power grid. Electricity theft with its many facets usually has an enormous cost to utilities compared to non-payment because of energy wastage and power quality problems. With the recognition of the internet of things (IoT) technologies and data-driven approaches, power utilities have enough tools to combat electricity theft and fraud. An integrated framework is proposed that combines three distinct modules such as data preprocessing, data class balancing and final classification to make accurate electrical consumption theft predictions in smart grids. The result of our solution to balance the electricity demand-supply gap can provide helpful information to grid planners seeking to improve the resilience of the power grid to outages and disturbances. All parts of this thesis include extensive experimental results on case studies, including realistic large-scale instances

    The Dudley estate : its rise and decline between 1774 and 1947

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    As a result of national and local agricultural and industrial trends during the latter half of the eighteenth century, the potential income from the Dudley estate in south Staffordshire and north Worcestershire was increased, because of its location in relation to the emerging Black Country, its mineral wealth, and the lordship of various local manors which Lord Dudley inherited. This potential, particularly from mineral development, was exploited to the full by the second viscount, 1774-88, who pursued a vigorous policy of enclosure, transport improvements, granting industrial leases, and mineral enterprise. This first period of development witnessed the rapid expansion of a wide variety of economic interests by the estate and a considerable increase in industrial, mineral, cottage, and land rents as the Dudley estate established a predominant position in the local economy before 1800. By 1833, the profits from early expansion were declining through inefficient management and an absence of long-term planning. This year was a major turning point because of the directives for the establishment of the Dudley trust, 1833-45, contained in the Will of the first earl, who died in 1833. Every sector of estate enterprise was reorganised and expanded. Agricultural properties were improved, rents and farm units rationalised, and specific leases were introduced. In order to take advantage of the expansion of the local iron trade, several new leased ironworks were established and the estate began to manufacture pig iron on its own account. Mineral enterprise expanded more than any other sector: reserves were exploited to the full by lessees and by the estate, which remained unusual in the extent to which it exploited its own minerals. Considerable capital was invested in the iron and mineral industries, and in further transport improvements: notably railways and canals. Estate profits were also invested in government stocks and the purchase of landed property. As a result, the Dudley estate was more than doubled in area by the purchase of valuable agricultural and sporting estates in Wales, Worcestershire, and Scotland. Complementary to this expansion in the scale and range of estate interests was the radical reorganisation of management and administration, undertaken by the trustees with the appointment of professional agents and the introduction of modern business practices. The last phase of development, 1845-1947, was a period of unprecedented prosperity for the estate, despite the decline in the local economy after 1860 and the pressures and restrictions placed upon the aristocratic landed interest from the 1870s. onwards. Income from the iron and mineral trades in particular rose to a peak, in spite of the collapse in the wrought iron trade. As the local economy became transformed and diversified by 1900, the estate also adapted to change but, instead of developing an entrepreneurial role in new industries, the estate reduced its own activities and gradually became a supplier of capital after 1897. Ultimately, it reacted to other trends and pressures by a total restructuring of estate interests in 1926, and began the systematic disposal of landed property. This, together with the nationalisation of coal and steel, finally severed the close, traditional link between the Dudley estate and local industry. Throughout the period 1774-1947, the estate played a beneficial and constructive role which served the interests of the area as a whole. Economically, the estate facilitated and helped maintain the prosperity of the area and, as benevolent employers of labour and, on occasions, as spokesmen for social reform, the Lords Dudley were a force for the good. In general, the development of the estate reflects the changing fortunes of the area and the landed aristocracy: in particular, because of its predominant position, the development of the area reflects the history of the Dudley estate during this period

    Modelling and optimisation of integrated urban energy systems for both heating and power

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    Taking into account the rapid increase of renewable energy power generation in the UK, the electrified heating represents an attractive solution for decarbonisation of heat in the long term. However, this will significantly increase the peak power demand in winter and bring further challenges to the grid. Therefore, this work aims to model and optimise a district-level multi-vector integrated energy system for both heating and power through technical and market analysis of using a variety of local renewable energy resources for electricity and heat. In this thesis, the integrated urban energy system is modelled and optimised in multi processes. As a target system, the heating and electricity demand of the University of Glasgow is used as a case study. In order to model the heating and electricity demand under different weather profiles, the heat demand of the buildings is modelled in an engineering model and a statistical model respectively to predict the hourly heat demand according to weather conditions; while the electricity demand is modelled considering both the building baseload and occupancy rate. In heat demand modelling, in order to distinguish the heat demand of each building from the data of whole campus provided by the Energy Center when the detailed building parameters are unknown, this work uses a bottom-up building energy model, which uses physical process of heat transfer to simulate the space heating of buildings, and proposes a Bayesian-based calibration method to calibrate the building parameters in the model. The results show that the Bayesian approach-based emulator performs better with fewer calibration times to find the optimal point, which is relable and efficient to calibrate the thermal parameters in building energy models. Due to the complexity of building a bottom-up building energy model, it is not easy to expand the model to larger areas or add more building samples in the model. Therefore, this work also builds a more general statistical model that can predict the heat demand of different types of buildings simply by giving weather conditions and building characteristics. This work uses artificial neural networks (ANN) technology to simulate the nonlinear relationship between weather conditions, building characteristics and heat demand. In order to improve the training efficiency of ANN, a new sensitivity analysis method is proposed to analyse the correlation between input variables and detect and remove the variables with low importance and the variables that have high importance but contain duplicated features. The result shows the proposed method can re duce training time by around 20% while achieving the same training and prediction performance compared with the traditional sensitivity analysis method. In the electricity demand modelling, the impact of randomness of occupants’ activity on power demand forecasting for buildings has been a difficult problem. In order to solve this problem, this work proposes two approaches for fitting and predicting the electricity demand of office buildings by splitting the time horizon for different occupancy rates. The first proposed approach splits the electricity demand data into fixed time periods and using linear regression approach to fit the building baseload and occupancy rate. The second proposed approach uses the ANN and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. The result shows that the proposed methods reduce the prediction error of electricity demand by 30% and 55% compared with the conventional ANN approach. To study the impact of electrified heating on buildings and the grid, an Integrated Energy Network (IEN) is established that includes the heat and electric demands of buildings, as well as the generation of local renewable resources and energy storage techniques. In order to rationally plan this new type of IEN based on electric heat pump (HP), this work studies and develops a particle swarm optimisation (PSO) algorithm-based optimisation size method to maximize the decarbonisation on building heating under limited investment cost. According to different source of electric driven, the IEN can be designed as a grid powered HP based heating system and a grid independent renewable heating system (RHS). For the grid powered IEN, this work formulates an operating scheme based on different electricity tariffs to reduce the operational cost of grid power. For the grid independent RHS, this work uses the PSO algorithm to optimise the size of local renewable resources, heat pumps and storage equipment based on the annual investment cost to minimise the total CO2 emission and reduce the operational cost of natural gas. This work provides a feasible solution for how to invest in RHS to replace the existing gas boiler/CHP based heating system. In summary, the significance of this study is to use of local renewable energy sources in electric heating taking into account the local weather conditions and the demand of heat and electricity to reduce carbon emissions in heating and electricity supply

    Rendimiento de combustibles bajos en carbono en motores de combustión interna

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    RESUMEN Los combustibles bajos en carbono son clave para la transición hacia un sistema energético sostenible con menos emisiones contaminantes y menos dependiente de los combustibles fósiles. El hidrógeno como vector energético permite una amplia implementación de las fuentes de energía renovables, con una generación de energía limpia más distribuida en las diferentes regiones del mundo. Además, el hidrógeno puede utilizarse en muchos sectores, como el marítimo, con una importante contribución al transporte de mercancías y pasajeros. Asimismo, hay corrientes residuales industriales con un elevado porcentaje de hidrógeno y alto contenido energético, permitiendo su recuperación con motores de combustión interna, una tecnología empleada mundialmente. La primera parte de esta tesis se enfoca en el análisis de un sistema energético europeo en 2050 basado en hidrógeno orientado a barcos, mientras que en la segunda parte se realiza el estudio del gas de coque en un motor, tanto experimentalmente como en un modelo CFD.ABSTRACT Low carbon fuels are key to the transition to a sustainable energy system with lower pollutant emissions and less dependence on fossil fuels. Hydrogen as an energy vector allows a wide implementation of renewable energy sources, with a more distributed clean energy generation across the world. In addition, hydrogen can be used in many sectors, such as the maritime sector, with an important contribution to freight and passengers transport. Likewise, there are industrial waste streams with a high percentage of hydrogen and high energy content, allowing its recovery with internal combustion engines, a worldwide deployed technology. The first part of this thesis focuses on the analysis of a European energy system in 2050 based on hydrogen oriented to ships, while in the second part the study of coke oven gas in an engine is carried out, both experimentally and in a CFD model.This thesis has been financially supported by the European Regional Development Fund within the framework of the Interreg Atlantic Program through the project “HYLANTIC” – EAPA_204/2016 and within the framework of the Interreg SUDOE Program through the project PEMFC-SUDOE (SOE1/P1/E0293 – INTERREG SUDOE/FEDER, UE), “Energy Sustainability at the SUDOE Region: Red PEMFC-SUDOE”. The three months research stay at the Institute of Energy and Climate Research – Techno-Economic Systems analysis (IEK-3) in Forschungszentrum Jülich, Germany, under the supervision of Martin Robinius has been also financed through the “HYLANTIC” project
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