110 research outputs found
Late Precambrian U-Pb titanite age for peak regional metamorphism and deformation (Knoydartian orogeny) in the western Moine, Scotland
No full textThere has been controversy over the number and timing of orogenies in the Precambrian Moine block in the Scottish Caledonides since the earliest radiometric dating in the 1960s. This work challenges a recent hypothesis, that this sector of the Laurentian margin was subjected to continuous crustal extension between greater than 900 and 470 Ma. U-Pb dating (thermal ionization mass spectrometry) of titanite from a calcsilicate pod in the Moine (Morar Group) of the western Highlands gives an age of 737 ñ 5 Ma. The titanite grew from Fe-Ti-bearing detrital minerals during the main progressive, syn-D2, amphibolite-facies (sillimanite zone) regional metamorphism, thus demonstrating that a Neoproterozoic contractional tectonothermal event (Knoydartian orogeny) affected the Moine block following the rift-related emplacement of the West Highland granite gneiss at 873 Ma. We conclude that the Sgurr Beag Thrust, a major tectonic break separating the Morar and Glenfinnan groups of the Moine, is mainly of Neoproterozoic, not Caledonian, age. The early tectonothermal event was succeeded by the Grampian Phase (Caledonian orogeny) at 460-470 Ma
Interstratal dewatering origin for polygonal patterns of sand-filled cracks: a case study from late Proterozoic metasediments of Islay, Scotland
No full textSand-filled cracks from the Lower Fine-grained Quartzite of Dalradian (late Proterozoic) age on the Island of Islay, western Scotland, may be divided into two main types, both of which
form orthogonal and non-orthogonal closed patterns on bedding surfaces. Type 1 cracks are short and lenticular in cross-section, contain sand which had been injected downwards, and are found on the
bottoms of cross-laminated sandstone beds. Type 2 cracks cut several beds and preserve evidence of upward flow of water-saturated sand. Both types of crack developed through the interstratal
intrusion of water-saturated sand into shrinkage cracks in mud or muddy sand, not, as previously thought, as a result of sub-aerial desiccation, or sub-aqueous cracking of the sediment surface
(synaeresis). These cracks likely resulted from layer-parallel contraction caused by compaction of mudstone layers during burial. Seismic shock may have provided the trigger for the preferential
development of polygonal crack patterns in these layers instead of the more usual small-scale dewatering structures. From a detailed comparison with published descriptions of filled cracks from a
number of different geological environments, it is concluded that interstratal cracking is a mechanism which rivals sub-aerial desiccation in importance, and is more common in the geological record
than is currently realized
Testing for the presence of a terrane boundary within Neoproterozoic (Dalradian) to Cambrian siliceous turbidites at Callander, Perthshire, Scotland
No full textThe Southern Highland Group (Dalradian) and Keltie Water Grit Formation, which includes the Lower Cambrian Leny Limestone, form an inverted, 1.4 km thick, largely arenaceous, sequence at Callander. The grits have the same detrital mineralogy throughout, mainly quartz, plagioclase (An(1-3)), muscovite, and biotite. Chlorite formed from detrital biotite during low-grade regional metamorphism (T less than 270 °C). There are some vertical changes in major element (but not trace element) chemistry of the grits, and detrital muscovites have a wide, but comparable, range in composition throughout, apart from an influx of Na-rich micas in the Keltie Water Grits. 40Ar/39Ar laser fusion dating of detrital muscovites yields an age spectrum with a peak at 1600-1800 Ma in the Dalradian rocks; similar old ages occur in the Keltie Water Grits but are diluted by ages of 507 - 886 Ma. We interpret these new data as showing that the rocks were most likely deposited as a single sequence, possibly with a disconformity, in Neoproterozoic to Early Cambrian times, before the onset of Grampian orogenesis in the Early Palaeozoic. No major structural or straitigraphical breaks have been identified and there is no direct evidence for the presence of two separate terranes
Turbidite sequences on South Georgia, South Atlantic: their structural relationship and provenance
No full textGeology of Shag Rocks, part of a continental block on the north Scotia Ridge, and possible regional correlations
No full textLoad-shifting in a new perspective: Smart scheduling of smart household appliances using an Agent-Bsaed Modelling Approach
No full textThe electricity demand of households in the Netherlands has been growing rapidly for the last decades and will continue to grow in the near future. This is specifically the case during peak periods. High peak loads could exceed the available capacity, resulting in overloaded network components (assets) which lead to an excessive reduction in life expectancy of these assets. The present aging distribution network will not have the capacity to cope with these future peak loads. The increase of electricity demand by the end-users therefore seriously reduces the reliability and safety of the electricity distribution. This poses an important problem for the Distribution Network Operators, who are responsible for the transport of electricity, maintenance and management of the regional electricity distribution networks. The traditional method to cope with capacity availability during peak periods is to invest heavily in placing more electricity cables. However, Demand-Side Management programs using load-shifting techniques also show good potential for reducing the peak loads in the network demand pattern. Load-shifting focuses on scheduling smart household appliances from peak load periods to off-peak periods. The aim of Demand-Side Management is to increase the efficiency of the system by bringing both demand and supply to the best possible low value. To measure the effectiveness of Demand-Side Management, the Key Performance Indicators (KPIs) “Levelling Effect” (LE) and “Height of Peak loads” (HP) are used. LE measures for a day the deviation of loads from the average load of the network. HP measures the highest load (in W) that occurs during a day in the network. Both the traditional way of improving the network, and a Demand-Side Management approach will require high investments. To ensure that such investments are economically viable, DNOs should now the extent to which Demand-side Management of households will affect these KPIs. Assessing load-shifting potential by scheduling smart appliances: Because load-shifting takes place through the individual scheduling of household appliances, the focus lies on a the household’s appliances level. On this level, the irregularities of the demand pattern are important, which are caused by the simultaneous usage of household appliance. We therefore constructed a simulation model using an Agent-Based Modelling approach, which takes into account these aspects. This simulation model represents a low-voltage network with one hundred households connected to it. Each household owns appliances, which build-up the electricity demand of the household. Smart appliances are modelled as individual agents to allow the scheduling of these appliances. Non-smart appliances are combined and generate the “other-loads”. The scheduler uses a “lowest-point” principle for the scheduling of smart appliances. Furthermore, all appliances are always scheduled and they cannot be rescheduled. The model simulates the demand pattern on the network during one working. External influences (e.g. weather) are ignored. From the literature and the available data, we made a trade-off between the required accuracy and computation, and opted for a time step of 15 minutes. Simulation results: As expected, the introduction of a smart system to the network was found to level the demand pattern and lower the peaks by a maximum of 13%. In this simulation, 16% of the total demand could be shifted. Non-cooling appliances (dishwashers, washing machines and tumble dryers) represent about 8% of the total load, and cooling appliance (refrigerators and freezers) the other 8%. The rescheduling of appliances did however increase the number of excessive peak loads, which in real life could form a serious risk for overloading the network. The scheduler does not take into account the profile of the smart appliances when scheduling. Appliances with a low start demand could therefore be scheduled to a low load timeslot while the load on subsequent timeslots could increase to peak loads when the appliances reach their full demand. More advanced scheduling algorithms that also take into account the appliance demand profile should resolve this. Apart from the above mentioned aspect, a closer examination of the smartness variable also showed some additional interesting developments. As expected, better forecasting and longer operational horizons will give better results. Unexpectedly however, was the lack of effect of the scheduling schemes. This is most likely because appliances are always scheduled, which results in a lack of advantage of being first in the schedulers queue. More advanced scheduling algorithms that allow appliances not to be scheduled may resolve this. The sequencing of the cooling appliances created a layer of smart cooling load that absorbs all the small irregularities in the demand pattern. Because of their short operational time, cooling appliances may therefore successfully be used for smoothing of the network demand pattern. Scheduling appliances using a “lowest-point” principle proved to work very well, but only for non-cooling smart appliances. The multiple usages of the smart cooling appliances caused them to turn on less in low-peak periods but more on the slopes towards peak loads. This scheduling artefact is caused by the time-step of 15 minutes and a too simplistic scheduling algorithm. Too few timeslots were available for effective scheduling of these appliances. Using a smaller time step in combination with a more advanced scheduling algorithm should improve the scheduling of smart cooling appliances. However, a smaller time-step would not necessarily increase the quality of the result, this also applies to a better representation of the non-smart “other-loads” profiles. A smaller time-step would increase the variation in irregularities on the demand pattern, but the overall network demand pattern would stay the same. Although the scheduler does take into account these small variations on the demand pattern, the general network demand pattern determines the areas were the smart appliances are scheduled to. Conclusion of research Our study has shown that load shifting by scheduling smart appliances is likely to produce more levelled demand pattern. Peaks in the network demand pattern can be reduced by 13% and the gaps are filled resulting in a more levelled demand pattern. A time step of 15 minutes works well for non-cooling appliances, but it limits the effective scheduling of (the present) smart cooling appliances. But a shorter time step would not necessarily have produced better results. The overall network demand patterns, and thus the overall scheduling places of the smart appliances, will most likely stay the same. What potentially could make a difference is a more advanced scheduler. Allowing rescheduling and the possibility for smart appliance not to be scheduled could result in a higher effectiveness of the scheduling schemes and more optimal scheduling of the smart appliances.SEPAM / Multi Actor SystemsPolicy AnalysisTechnology, Policy and Managemen
Designing optimal investment trajectories for the energy transition of industrial clusters in the Netherlands
No full textBecause of the climate crisis the world is facing, all sectors must move towards a more sustainable future. In the Netherlands, the industry sector emits large amounts of CO2 because of the heavy reliance on fossil-fuels and large electricity demand. The Paris agreement and the Green Deal forces the industry sector to rapidly transition towards more sustainable practices, which can be achieved if the correct synergies are established. The multi-actor nature and the technical and operational dependencies that industrial clusters have, makes optimal decision-making extremely important in working towards that net zero future. Innovations that re- duce the emissions are limited, but they exist and provide (intermediary) solutions to adhere with the imposed regulations. Furthermore, the industrial clusters are subject to numerous different factors that influences the behaviour of actors within the cluster. The issue faced is to understand how the transition of an industrial cluster is influenced by exogenous factors and how actors in such an integrated environment should invest, while keeping in mind that both the industrial clusters and the individual actors have to remain profitable and have differing investment behaviour. Current studies fall short in identifying, analysing and understanding how multi-actors in an institutional en- vironment relate to the technical options and the exogenous factors engaging with that system in an industrial setting. Identifying an optimal investment trajectory that such an industrial cluster should follow to adhere with the regulations whilst staying profitable with a multi-actor configuration requires different integrated methods and other tools. This thesis will address that problem through the following main research question: What is the effect of multiple exogenous factors on the optimal investment trajectories of indus- trial clusters in the Netherlands with multiple investment options? This question has been answered through exploratory research combined with a modelling approach. The first step in the exploratory research required attaining insights in what the current state of is multi-actor invest- ments. This in combination with looking at how they are structured contractually has been the main foundation for the literature review. A case study analysis has been conducted of an industrial cluster located in Geleen, called Chemelot. This allowed for analysing decarbonizing investment options and looking at what exogenous factors influence the industrial cluster. The investment options attempt to move Chemelot away from fossil-fuels as energy source and move towards lower emitting sources such as electricity. The information gathered served as an input to put together a methodology which captured the exogenous factors, interactions in the industrial cluster and the sustainable investment options in an optimization model. Using this, the optimization model was constructed using Linny-R, a Mixed Integer Linear Programming op- timization software developed by Dr. P.W.G. Bots. The model produced quantifiable results as well as serve as a proof of concept for the methodology that is presented to incorporate exogenous factors and sustainable investment options in the energy transition. The model also gives insights in what effect the exogenous factors have on the investment behaviour of actors within industrial clusters by showing cash flows, both individually and collectively and through an investment curve for the cluster. The economic performance is considered to be the leading metric in this research. Finally, using the results, the implications of implementing exogenous factors and sustainable investment options in an optimization model are discussed. The research outcomes show that CCS and electrification are favourable investments for Chemelot to remain profitable and cope with increasing prices of commodities and CO2. The model results has provided insights in the effect that exogenous factors have on the energy transition of an industrial cluster. More specifically, it showed that a cap on CO2 and increasing the price of CO2 emitted really accelerates the rate at which actors make investments in sustainable options. Added to that, increasing the price of other commodities such as natural gas and naphtha also forces actors to move away from those commodities and look for alternatives that provide the same product or energy without having to compensate the CO2 emissions related to them. Other factors such as limited infrastructure restrict industrial clusters in the amount of products they can produce and restrict them from innovating towards less emitting processes. Electrification of certain processes is possible, but increases the electricity demand with enormous amounts. With limited electricity infrastructure to provide those amounts, the innovation cannot be realized and therefore, halting the transition for an industrial cluster. The findings suggest that because of these exogenous factors, the actors will move towards electricity demanding innovations that do not make use of fossil-fuels and invest in CCS options. However, the methodology presented should be used as directing future research in looking at how industrial clusters should engage in the energy transition. The dependencies existing between actors in an industrial clusters and how different investments II may adjust these dependencies are identifiable using the methodology presented in this research. Ending with a general conclusion, the investment behaviour of industrial cluster changes because of multiple exogenous factors that influence the system. The increase in commodity prices combined with CO2 capping reg- ulations cause actors to invest more quickly into sustainable investment options to adhere with the regulations, but it also causes them to compromise their production levels in some scenarios to remain profitable. Actors downstream are dependent on the investments made by actors upstream since they rely on the output of those upstream actors to develop the final products that they sale. This combination generates valuable insights from a systems perspective but also from a multi-actor perspective.Complex Systems Engineering and Management (CoSEM
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