554 research outputs found

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    An investigation into the environmental sustainability of the South African ornamental horticultural industry

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    The ornamental horticultural industry makes use of natural resources to grow plants and produce allied products to sell to consumers, landscapers, retail garden centres, hardware stores, supermarkets, and government, but at what cost to the environment? The aim of this work was to determine the current environmental awareness of growers and garden centre retailers within the ornamental horticultural industry in South Africa. Followed by an investigation into the current business practices that promote sustainable natural resource use and management as well as the obstacles and challenges that the industry faces with implementing legislation and recommendations of best practices. The study was conducted over an 18-month period and 41 growers and retail garden centres in eight of the provinces in South Africa (Appendix 10) participated in research. In each case, the study participant was asked to complete the questionnaire and where possible, a site visit was conducted and / or a semi-structured interview as well as participatory observations followed to give a comprehensive overview of the sustainability practices of the businesses. These results were then compared to international best practices and similar research conducted globally by the ornamental horticultural industry. A review of international best practices in the ornamental horticultural industry showed six environmental resources namely soil, water, fertilizers, pesticides, energy, and waste. This was seen to be common to most studies involved in the production, growth, maintenance and sales of plants and allied products. This information was used to compile a best management practice manual for South African ornamental horticulture with guidelines and practical examples for conserving and managing natural resource usage and reducing the environmental impacts of the industry. Much research has been done on the exploitation and degradation of resources due to urbanisation, industrial activities, and agricultural practices. The resources are essential to the ornamental horticultural industry but if exploited or misused, can have detrimental effects on the environmental productivity of the industry and ultimately the “Sustainable Development Goals” prescribed by the United Nations. The linking of the relevant sustainable development goals to the 9 key factors of the green economy strategized by the South African government will enable the ornamental horticultural industry to play a greater part in the green and circular economy by providing nature-based solutions to environmental problems that it is facing such as climate change and pollution.Environmental SciencesD. Phil. (Environmental Management

    Accelerating Australia’s electric vehicle uptake: Overcoming socio-technical inertia and bridging the gaps with public policy options designed to transform road transport for a decarbonised future

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    To obviate significant and growing road vehicle greenhouse gas (GHG) emissions contributing to climate change, transitioning to battery electric vehicles (BEV) is urgently required to maximise fleet emissions reductions soonest, deploying the most suitable available technology. Many countries have implemented policies to incentivise electric vehicle (EV) uptake, which have been well studied. This thesis undertakes novel research by employing a case study of New Zealand to examine consumer responses to EV policies implemented in 2016, plus two mooted policies. Questionnaires and interviews surveyed private motorists from a demand perspective, capturing quantitative and qualitative data to assess attitudes, values, and perceptions of EVs, awareness of government policies, and to reveal those most popular. Employing a unique innovation, four motorist groups (segmented by attitude to EVs, which influences adoption rates) were compared. As additional novelty the role of communication channels, including print media, in influencing consumer behaviour was investigated. Results revealed New Zealand’s conventional motorists, in contrast with EV owners, had low policy awareness, confirming international findings. EV Positives, the next-most ‘EV ready’ segment, favoured policies designed to reduce EV purchase price and increase nationwide charger deployment. Concordant with social marketing research, governments should focus on such buyers’ preferences. Furthermore, to improve BEV readiness, disseminating updated information about EVs via multiple communication channels could shift perceptions of EVs from ‘expensive and inconvenient’ to ‘fun and economical’. Thus, two key concepts namely purchase price-parity and charging infrastructure availability, were incorporated into models specifically for Australia, where policies are limited, to investigate the feasibility of transitioning Australia’s road vehicle fleet to electromobility to achieve net-zero emissions by 2050. A national scale, integrated, macro-economic, system dynamics model (iSDG Australia) was used innovatively to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions. Firstly, the model applied numerous ‘adoption target’ scenarios comparing them to Business-as-Usual; secondly, various combinations of policy options were modelled to project potential outcomes and implementation costs. Based on the assumptions, results suggest emissions reductions are maximised by the fastest passenger vehicle fleet transition to BEVs, entailing declining but ongoing transformational government policy support to achieve net-zero by 2050

    Modelling of Maintenance ServiceWorkshop and Inventory Operations for a Short Cycle Operational Region.

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    The North Sea region sees a high operational cycle of tools that require frequent maintenance check-ups, repairs, and preparations for subsequent operations. Given the quick-paced nature of these operations, the availability of spare parts at the maintenance workshop is critical for maintaining minimal flowtime. Adding to the challenge is the practice of sourcing spare parts from best-cost countries, leading to a lead time of approximately one year, thus necessitating an optimal economic order quantity and reorder point. The balancing act between maintaining sufficient inventory at the workshop and managing operational expenses through batch ordering of spare parts is a complex one. Frequent supply requirements contribute to the environmental impacts through increased spare part scrap rates. With these challenges in mind, this thesis aims to develop a simulation model capable of quantifying the costs and benefits associated with reusing repaired spares, as compared to procuring newly built spares from best-cost countries. To achieve this, a case study focusing on a specific maintenance workshop within the North Sea region was carried out. The comprehensive tool repair and spare part supply operations were conceptualized and modeled using a simulation approach. Two operational scenarios were simulated: the first, where the maintenance workshop was completely dependent on newly built spares sourced from best-cost countries, with no inventory stock dedicated for spares re-usage. In the second scenario, the workshop primarily relied on repaired spares, with a safety level of new build stock maintained. The results, guided by the research question probing the impact of implementing a repair-path cycle process within the maintenance process, showed that the enhanced model significantly outperformed the baseline model across several key metrics over a model time run of three years. These include a 78% reduction in lead times, a 116% improvement in worker utilization, a 73% reduction in crowding levels, a 52% reduction in scrap rate, and a potential profit increase of roughly three million NOK (20%). This thesis provides evidence that the enhanced model, with its focus on repaired spares, presents a more sustainable, efficient, and profitable solution to the challenges of inventory management in highcycle operations. It is important to note, however, that the sensitivity of these results is closely tied to the high procurement lead times

    Technology and Management Applied in Construction Engineering Projects

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    This book focuses on fundamental and applied research on construction project management. It presents research papers and practice-oriented papers. The execution of construction projects is specific and particularly difficult because each implementation is a unique, complex, and dynamic process that consists of several or more subprocesses that are related to each other, in which various aspects of the investment process participate. Therefore, there is still a vital need to study, research, and conclude the engineering technology and management applied in construction projects. This book present unanimous research approach is a result of many years of studies, conducted by 35 well experienced authors. The common subject of research concerns the development of methods and tools for modeling multi-criteria processes in construction engineering

    Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

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    One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

    Performance Evaluation of Function Composition in Middlewares supporting FaaS for Serverless computing

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    The concept of Serverless Computing is a new and exciting aspect of cloud computing that involves the deployment of small pieces of software applications and services as serverless functions. Serverless computing architecture enables the cloud provider to fully manage the execution of a server's code, eliminating the need for customers to develop and deploy the traditional underlying infrastructure required for running applications and programs. Even though big tech companies are extensively utilizing serverless computing in their products and investing billions on this novel but affirmed technology, it is affected by various problems still considered an open field in research. In fact, by definition, FaaS architectures are geographically dislocated and consequently subject to event propagation delays that can significantly degrade the overall system performance. What is generally done, is to reduce as much as possible cumulative delays especially if attributable to the infrastructure itself that could determine a greater or lesser competitiveness on the market. The background idea, which becomes the leit motiv throughout this work, is to develop and assess the performance, and thus the validity, of a Message-Oriented Middleware-centric serverless platform architecture promising to enable advanced analytics capabilities and better overall performance, without renouncing the essential characteristic of scalability in the context of distributed systems. Experiments in emulated conditions show that applying the MOM coordination co-locality principle improves the end-to-end delay and data processing performance

    A systems biology understanding of protein constraints in the metabolism of budding yeasts

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    Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society

    Towards a circular building industry through digitalisation

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    This thesis explores the integration of Circular Economy (CE) principles of narrow, slow, close, and regenerate in the social housing practice through digital technologies. Beginning with the examination of the CE implementation in Dutch social housing organisations, the research extends its focus to the broader built environment, introducing the Circular Digital Built Environment Framework and identifying ten enabling technologies. Subsequent chapters explore realworld applications of these digital technologies in circular new built, renovation, maintenance, and demolition projects of forerunner social housing organisations. The thesis includes a comprehensive study of material passports, addressing challenges around data management and proposing a digitally-enabled framework. The thesis concludes with critical reflections on the findings and their implications and provides further recommendations for research and practical applications in advancing circularity in the building industry through digital technologies
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