Browsing by Author "Ishiyama, Edward"

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    Carbon Brainprint Case Study: optimising defouling schedules for oil- refinerypreheat trains
    (2011-07-31T00:00:00Z) Parsons, David J.; Chatterton, Julia C.; Wilson, Ian; Ishiyama, Edward
    In an oil refinery, crude oil is heated to 360-370°C before entering a distillation columnoperating at atmospheric pressure where the gas fraction and several liquid fractions withdifferent boiling points (e.g. gasoline, kerosene, diesel, gas oil, heavy gas oil) are separated off.The crude oil is heated in two stages. The preheat train - a series of heat exchangers - heats itfrom ambient temperature to about 270°C when it enters the furnace, known as the coil inlettemperature. The furnace then heats the oil to the temperature required for distillation.The purpose of the preheat train is to recover heat from the liquid products extracted in thedistillation column. Without this, 2-3% of the crude oil throughput would be used for heating thefurnace; with the preheat train up to 70% of the required heat is recovered. It also serves tocool the refined products: further cooling normally uses air or water. Over time, fouling reduces the performance of the heat exchangers, increasing the amount ofenergy that has to be supplied. It is possible to bypass units to allow them to be cleaned, withan associated cost and temporary loss of performance. The cleaning schedule thus has animpact on the overall efficiency, cost of operation and emissions. The group at the Department of Chemical Engineering and Biotechnology at Cambridgedeveloped a scheduling algorithm for this non-linear optimisation problem. It yields a good,though not-necessarily optimal, schedule and can handle additional constraints, such as thepresence of desalters with specific temperature requirements within the preheat train. This isnow being developed into a commercial software product. Data from two refineries - one operated by Repsol YPF in Argentina and the Esso FawleyRefinery in the UK - were used to model the systems and test the algorithm. For the Repsol YPF refinery, when compared with current practice and including a constrainton the desalter inlet temperature, the most conservative estimate of the emissions reductionwas 773 t CO2/year. This assumed a furnace efficiency of 90%. The emissions reductionincreased to 927 t CO2/year at 75% efficiency and 1730 t CO2/year at 40%. These were basedon a stoichiometric estimate of the emissions from the furnace. Using a standard emissionfactor increased them by 7.4%. For Esso Fawley, the estimated emission reduction compared to no maintenance was1435 t CO2/year at 90% furnace efficiency. This increased to 1725 t CO2/year at 75% and3225 t CO2/year at 40% efficienc
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    Carbon brainprint – An estimate of the intellectual contribution of research institutions to reducing greenhouse gas emissions
    (Elsevier, 2015-05-07) Chatteron, Julia C.; Parsons, David J.; Nicholls, John R.; Longhurst, Philip J.; Bernon, Mike; Palmer, Andrew; Brennan, Feargal P.; Kolios, Athanasios; Wilson, Ian; Ishiyama, Edward; Clements-Croome, Derek; Elmualim, Abbas; Darby, Howard; Yearly, Tom; Davies, Gareth J.
    Research and innovation have considerable, currently unquantified potential to reduce greenhouse gas emissions by, for example, increasing energy efficiency. Furthermore, the process of knowledge transfer in itself can have a significant impact on reducing emissions, by promoting awareness and behavioural change. The concept of the ‘carbon brainprint’ was proposed to convey the intellectual contribution of higher education institutions to the reduction of greenhouse gas emissions by other parties through research and teaching/training activities. This paper describes an investigation of the feasibility of quantifying the carbon brainprint, through six case studies. The potential brainprint of higher education institutes is shown to be significant: up to 500 kt CO2e/year for one project. The most difficult aspect is attributing the brainprint among multiple participants in joint projects.
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    Carbon Brainprint: final report on HEFCE project LSDHE43
    (2011-07-31T00:00:00Z) Parsons, David J.; Chatterton, Julia C.; Clements-Croome, Derek; Elmualim, A.; Darby, Howard; Yearly, T.; Wilson, I.; Ishiyama, Edward
    The need for organisations to reduce their carbon footprint is now well accepted. HEFCE has recently published its policy (2010/01) requiring universities to set targets to reduce their greenhouse gas emissions and targeting reductions of 34% and 80% across the sector by 2020 and 2050 respectively. Universities, however, also help other organisations to reduce their own carbon footprints, both through providing existing or potential employees with the necessary knowledge and skills and, more directly, though research and consultancy projects. These reductions cannot be offset against the university's footprint, but the intellectual contribution to reducing the carbon footprint of others, termed their "carbon brainprint", is immensely valuable in meeting the challenge of global warming. This project aimed to help quantify the HE sector's Carbon Brainprint. It used a set of case studies from Cranfield, Cambridge and Reading Universities to establish a robust, repeatable method, informed by life cycle analysis methods and PAS2050 for carbon footprinting, for calculating and verifying the contribution of universities to reducing greenhouse gas emissions. This method could be applied across the sector to assess the impact of HE intellectual activities. Guidelines were drawn up at the start of the project and revised as the case studies progressed. These included general principles, based on carbon footprinting standards, appropriate spatial, temporal and conceptual boundaries for brainprint studies, the scope and limits of applicability, appropriate levels of detail, uncertainty analysis and the possible need to attribute the brainprint among project partners. The guidelines set out the main steps in a brainprint assessment: system description, boundary definition, data gathering, assessment of emissions and changes to evaluate the retrospective and prospective brainprint, and uncertainty analysis. The case studies covered * Ceramic thermal barrier coatings for jet engine turbine blades, which help to improve engine efficiency and reduce aircraft fuel consumption. * Novel offshore vertical axis wind turbines that will be able to generate ‘green' electricity using less material for construction than conventional designs. * Improved delivery vehicle logistics to reduce delivery vehicle fuel use in the food sector. * Training for landfill gas inspectors to capture emissions of methane from landfill sites. * Intelligent buildings to reduce fuel consumption by both behavioural change and advanced monitoring and control. * Optimising defouling schedules for oil-refinery preheat trains, to maintain efficiency and reduce the consumption of oil within the refinery. These included developments that were already implemented in practice, including some where data on the results were available, and others that have yet to be used. All demonstrated the positive effects of research, consultancy or teaching in reducing greenhouse gas emissions, although the scale of the effect varied considerably. The largest totals came from the jet engine thermal barrier coatings, due to the large quantities of fuel consumed by aircraft engines, and the training of landfill gas inspectors, due to substantial changes in the emissions of a highly potent greenhouse gas. In other cases the unit reductions were smaller, but the potential total effects are large if they are widely adopted. On the basis of these studies, it seems likely that a relatively small number of projects focussed on applications with high energy or greenhouse gas flows will represent the majority of the brainprint of most institutions. Those where good monitoring data from full-scale application are available will normally be comparatively simple to assess and provide clear results. The project has demonstrated that it is possible to begin to quantify the impact that universities have on society's greenhouse gas emissions, and that this impact is large. The current annual brainprint of the four projects assessed at Cranfield University is over 50 times the university's own annual carbon footprint.
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    Carbon Brainprint: quantifying the impact of universities on carbon footprint reduction
    (Cranfield University, 2019-02-13 11:37) Parsons, David; Chatterton, Julia; Clements-Croome, Derek; Elmualim, Abbas; Darby, Howard; Yearly, Tom; Davies, Gareth; Wilson, Ian; Ishiyama, Edward
    Universities make tremendous intellectual and technical advances that help other organisations and individuals reduce their own carbon footprints. This is the universities€™ carbon brainprint, and measuring this allows universities to quantify the impact of their research, innovation and knowledge transfer activities on cutting global GHG emissions. It provides further endorsement of the value of investing in universities such as Cranfield to address the challenge of global warming. The initial project developed a set of approaches to estimating the carbon brainprint of an activity, such as research, development, consultancy or training. These were applied to six case studies from Cranfield, Cambridge and Reading Universities, which demonstrated the large impact that higher education institutions can have. A summary of the final report is attached, as well as a summary of each of the six case studies: 1. Ceramic coatings for jet engine turbine blades, Cranfield University and Rolls-Royce. Summary pdf attached; read full case study. 2. Novel offshore vertical axis wind turbines, Cranfield University and Energy Technologies Institute. Summary pdf attached; read full case study. 3. Improved delivery vehicle logistics, Cranfield University and Defra. Summary pdf attached; read full case study. 4. Training for landfill gas inspectors, Cranfield University and Environment Agency. Summary pdf attached; read full case study. 5. Intelligent buildings, University of Reading and HEFCE. Summary pdf attached; read full case study. 6. Optimising defouling schedules for oil-refinery preheat trains, University of Cambridge and EPSRC. Summary pdf attached; read full case study. Additional project outputs are: - The final report at http://dspace.lib.cranfield.ac.uk/handle/1826/6805. - The guidance on calculating brainprints at http://dspace.lib.cranfield.ac.uk/handle/1826/8236. - The 2015 paper €˜Carbon brainprint €“ An estimate of the intellectual contribution of research institutions to reducing greenhouse gas emissions€™ published in Process Safety and Environmental Protection 96, 74€“81. Available at https://doi.org/10.1016/j.psep.2015.04.008. - The 5min35 summary video of the project at https://youtu.be/9GSjDaWO9dQ. The Carbon Brainprint project was highly commended at the 2011 Green Gown Awards in the research category.