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Pioneer investments kg mbh to btu

Coal and lignite basins in Germany. Source: Geosatellitenbildatlas Deutschland, Figure 2. Lignite mines and mining wastelands in Lower Lusatia in Lignite, extracted from huge open-cast mines in the Leipzig region and in Lower Lusatia, is responsible for the devastation of vast areas of land Fig. Although the end of mining was supposed to bring rehabilitation, recultivation or reforestation to mining wastelands, the priority given to production and the lack of financial resources as of the s reduced the size of rehabilitated areas, which were much smaller than those affected by the development of mining 9.

By the time the communist regime fell in , barely half of the , ha of exploited agricultural or forest land had been rehabilitated. This was due to the strictly productivist logic in force in the GDR, which pushed into the background the need to rebuild post-lignite mining landscapes The high level of strata continuity allowed for the widespread use of transit bridges, such that the open-cast mines in Lower Lusatia expanded at a much faster rate than in the other basins.

Recultivation, however, was unable to keep up with this pace. Photo 1. Table 1. Areas exploited and rehabilitated in Lower Lusatia ha. Figure 3. Actors for remediation projects in Lower Lusatia. This is why they negotiated a compromise with the federal government relating to the financing of the remediation operations.

Finally, local players made up of the municipalities concerned were also asked to propose projects in which remediation and tourism development could be combined. In the early s, however, the municipalities had other priorities, such as modernizing infrastructure, developing trade and preserving or creating new jobs. The brownfields that remained the property of the LMBV were not at all taken into consideration by the municipalities. It was only gradually, from the mids with the first visible successes of remediation, that the first plans integrating brownfields within development projects were born These include the development of business parks and recreation areas, as well as the installation of photovoltaic parks and wind farms, which are all examples of the creation of a new post-mining landscape Agricultural areas, on the other hand, have significantly decreased.

By the late s, the new post-mining landscape, consisting of large lakes in residual pits and new forests planted on rubble plateaus, was beginning to take shape 22 Fig. At the same time, in view of the disappearance of most of the jobs in the lignite sector, 23 there was also the question of the creation of new economic activities, and therefore the use of the rehabilitated areas.

LMBV also aimed to support the reuse of these spaces by seeking investors for new activities, in order to prepare the economic conversion of the region. Figure 4. Boundary of the authorised mining area; 3. Boundary of the planned mining area; 4.

Areas undergoing rehabilitation; 5. Agricultural areas; 6. Reforested areas; 7. New lake in a former mine; 8. Access channel between the new lakes; 9. Urban area, or village; Power plant; Motorway; International border. Source: Wolfram Pflug, , modified. From the visitor centre located in the last remaining house from the days of mining, you can explore a strange landscape the Naturlandschaft Wanninchen of sand dunes and marshes, home to a wide array of flora and fauna.

Photo 2. Photo 3. A vineyard has been planted on the inclined plane below the IBA headquarters See logo. The entrance to the new marina can be seen in the background. Photo 4. Findlingspark botanical garden, created on part of the mining wastelands of the Nochten mine.

The originality of this region is that it has great potential for outdoor holidays, such as beaches, nautical activities and a wide range of sports and recreational activities cycling, skateboarding, horse riding or quad biking , as well as industrial heritage sightseeing the giant excavator of the old Klettwitz mine, or the old lignite-fired power plant in Plessa.

Some of these bodies of water have seen the development of marinas, with catering and accommodation facilities nearby. The objective is to reach 1. The Land of Brandenburg was a pioneer in its desire to develop renewable energy. To achieve its targets, it has focused mainly on biomass and wind energy. However, the Land of Saxony is still lagging, particularly in terms of wind turbines, whose installed capacity is five times lower than that of Brandenburg.

LMBV then drew up a list of suitable areas for the installation of photovoltaic parks and sought out investors. In addition, photovoltaic installations have recently been set up in the car park of the Lusatia race track Eurospeedway Lausitz. The demographic and economic decline of East Germany made it very unlikely that such large areas could be reused for new economic purposes.

Moreover, the installation of wind farms, and more recently photovoltaic parks, has exemplified the potential ways in which these areas can be developed, which until recently remained a big question and contributed to a negative image of the region. Figure 5. Areas undergoing mining operation, or recently closed; 2. Agricultural; 6.

Reforested land; 7. Photovoltaic park; Wind farm; Landmarke, or visitors centre; Marina; Eurospeedway Lausitz race track; Lignite-fired power plant; As such, it appeared necessary to develop a concept to preserve the cultural heritage of the mining region Highly regarded as a genius landscaper, his work has spread to North America and is considered the pinnacle of garden art in Europe Indeed, the IBA took into account the fact that the landscapes will change considerably in the coming decades, as a result of the gradual flooding of former mines.

Photo 5. Photo 6. Photo 7. Floating houses on Lake Geierwalde. The idea of a navigable canal linking the different lakes thus emerged during the IBA. Made of steel with an oxidised red surface, it is nicknamed the Rostiger Nagel the rusty nail. Near this Landmarke , you can also walk along the first part of a floating pontoon which, according to the initial project, was to cross the whole of Lake Sedlitz. Photo 8. Photo 9. The lake and forested landscape provide a representative image of post-mining Lower Lusatia.

The highlight of this original tourist concept is organised tours 36 , during which visitors are invited to share their emotions inspired by the rebirth of nature on the mining wastelands. This new tourist route, which is linked to the European Route of Industrial Heritage ERIH 38 , is a network of ten representative lignite mining and processing sites for energy production. The originality of the route, however, is that it also includes active sites such as the new Schwarze Pumpe lignite-fired power plant and the Welzow lignite mine, where excursions by foot, bicycle and off-road motor vehicle are offered.

This is a reconstruction of a remarkable topographic feature that existed on this site before mining operations began, an ancient moraine marking the maximum extension of an ancient glaciation. The combined lake budget model indicated that re-acidification was dominated by leaching of acidic sulfide mineral weathering products from the Tertiary bank substrates.

High inputs of iron, aluminum, and sulfate were generated by infiltrating rain water, interflow, and groundwater recharge. In contrast, acid loads from surface runoff and soil erosion were minor at this particular site. Based on this work, a methodology is proposed to obtain critical parameters from field and lab investigations and integrate those into hydrogeochemical and physical transport models.

These process-based models offer tools to reliably predict the water quality of mining pit lakes, develop appropriate treatment measures for the rehabilitation period, and plan the requirements for cost-effective lake water conditioning. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Blodau C Groundwater inflow controls acidity fluxes in an iron rich and acidic lake. Acta Hydrochim Hydrobiol — Blodau C A review of acidity generation and consumption in acidic coal mine lakes and their watersheds.

Sci Total Environ — Blodau C, Peiffer S Deposition and transformation of the iron mineral schwertmannite suppress the internal neutralization of lignite mining pit lakes. Bonacina C Lake Orta: the undermining of an ecosystem. J Limnol 60 1 — Google Scholar. LUA Brandenburg ed Open pit mine lakes: water quality and restoration in the sense of water quality management—concepts and operational experiences.

Castendyk DN Conceptual models of pit lakes. Society for Mining, Metallurgy and Exploration, Littleton, pp 61— Chem Geol — Grundwasser—Zeitschrift der Fachsektion. Hydrogeologie — [in German]. Waste Manag — Haude W Determination of evapotranspiration by an approach as simple as possible.

Hoyningen-Huene JV Interception of precipitation in agricultural crops. Gustav Fischer Verlag Jena, Stuttgart. Knorr K-H, Blodau C Experimentally altered groundwater inflow remobilizes acidity from sediments in a iron rich and acidic lake. Environ Sci Technol — Morin KA, Hutt NM On the nonsense of arguing the superiority of an analytical method for neutralization potential. Redfield AC The biological content of chemical factors in the environment. Am Sci — Limnologica — Schmidt J Development and application of a physically founded simulation model of erosion from sloped arable land [in German].

Limnologica — and Erratum, Limnologica Technical manual, V 1. Goldschmidt Conf Abstr Miner Mag 75 3

RATING OF BROKERS FOREX

Individuality, these models can be used to understand the actual performance of the equipment in its operating environment, ultimately providing better forecasts for performance degradation, maintenance workscopes, scheduling, and spare parts.

When done at scale e. A logical extension of Digital Twin is to model the actual customer and provider operations used to generate desired business outcomes and, ideally, optimize the processes to improve either or both customer and provider financial objectives.

Applications built on top of the DTOO analytic framework may then serve the provider's internal customers, such as CPMs, shop planners, finance and new engine commercial operations, and may also be used to deliver customer-facing tools and new service offerings as well. In example embodiments, the DTOO capability fits within an overall decision support structure that centers around modules focused on analytics-based removals ABR , analytics-based workscope ABW , operations efficiency and customer productivity.

In various embodiments, the DTOO drives customer productivity. In various embodiments, engine operating data, contract data, and environment data are outputs from the customer productivity module that are received as inputs at the productivity module for optimizing asset availability e.

For example decision support: if an airline were to expand its routes to include city-pairs with known harsher conditions, what quantitative effects would that have to the number of shop visits, and ultimately to CSA profitability given the specific CSA terms and conditions for that customer?

Or, in another scenario: if the provider were to make a derate recommendation to a customer for a particular flight route, what potential side-effects would we see in fuel burn or engine degradation or shop visit cost or services cash flow from operations? In example embodiments, the model is intended to simulate the ecosystem on the time-scale of months to years to understand the aggregate impact of operating decisions over time; it is not intended to represent day-to-day operations which might include flight routing through airspace, or operational recovery from unplanned events, though those elements are callable by the system's simulation-optimization and vice-versa.

In example embodiments, a core simulation engine sits in the background on a parallelized, high-performance computing environment, serving multiple light-weight apps. The core simulation is a discrete event simulation that models some or all of the provider-customer ecosystem, from airline flights to maintenance and parts management. Depending on the inputs, the simulation can be used to analyze the impact of different potential future scenarios, or can be used to conduct sensitivity analysis on parameters of interest.

This holistic modeling capability allows a user to configure the simulation to address specific questions to identify win-win recommendations for the provider and customers. In this manner, each light-weight app is designed to address a specific question of interest by interacting with the core simulation, post-processing the output and reporting the results in a relevant format for the user to make a decision.

In example embodiments, the simulation includes an orchestration of business-physical subsystems that are called within the application as subroutines or as services that exercise kernels, command line applications on one or distributed computing environments. The disclosed computing system's ability to simulate the provider-customer ecosystem in a single consistent modeling framework and set of assumptions brings a new capability that serve numerous users.

For the provider's Customer Team, a use case is planning and scheduling engine removals and specific on-wing maintenance to minimize CSA costs for specific contract fleets. For the provider's shop planners, the general use case includes determining shop demand functions under different scenarios and assisting with shop-loading via engine assignments to different shops. For the provider's material planners, the general use case may include determining short, medium, and long-term parts demands under different scenarios e.

For the provider's customer advisors, the general use case includes identifying win-win policies or operational changes for airlines. For customers, the general use case may include quantifying impact of operational policies on fuel burn and maintenance burden. In example embodiments, users will interact with the system simulation via the apps for their specific use-case. Applications types may include 1 a detailed scenario analysis application, which enables a user to create a scenario and produce multiple detailed output view of the results in the form of graphs, heat maps, and aggregated values, 2 a comparator application, which allows a user to define a limited number of e.

In example embodiments, described technologies may be used to store the operating history of parts and assets, life experiences e. In practice, complex engineered systems used in industrial processes may have a business context, such as contractual service agreements, capital financing terms and covenants, interconnect policy, regulatory policy, and competitive substitutes in their served markets. Optimizing an industrial ecosystem without consideration of the multitude of objectives and constraints or considering the ecosystem's many existing constraints for the purposes of asset design and operating policy may trap economic or other aspects of value.

In example embodiments, the method considers aspects and silos of economic value for subsets of choices within complex engineered industrial systems and the surrounding business processes and structures. The disclosed system allows for the discovery and control of system design and operating constraints, the co-optimization of multiple objectives that can be achieved with design or operational decision support over an economic or operational interval.

Further, the method allows for the resulting financial risk and returns from the various design and operating policies to be used, or candidates to be used, for specific discount cash flow valuations associated with those investments and operating policies—and therefore, facilitating the allocation of risks and returns to the sponsors of various incremental investments in the industrial system and for responsible stakeholders who chose not to make certain investments or operating policies.

The computing control system's capability to run the virtualized system, its various state estimations for degradation and performance while calculating the interactions of the design and operations choices provides the mechanism for calculating the real option value of those choices. It provides precise sources of and contribution to variation, and identifying and proactively managing system level sensitivity and performance or financial risk abatement.

The method enables the disaggregation of risks such that the discount rates may be replaced by simulated and replicated operational and design scenarios which thusly enumerate the probable financial outcomes, thereby obsoleting the need for gross or average discounting of future cash flows and instead is able to place specific discounts for fixture uncertainties according to the system level variation that results from a given design or operating choice.

Similarly, given the ability to vary any aspect of assumption, design feature or operating policy and then simulate the industrial system's operating and financial performance—specific causality of choice to performance is enabled for the purposes of allocating cash flow. The delivery of the cash flows amongst stakeholders is certainly enabled with traditional debits and credits into financial accounts and may be implemented with crypto-currency mechanisms whose core logic is informed by precise allocation of cashflows.

In example embodiments, the system co-optimizes use of assets e. Cumulative damage models for the assets are orchestrated through time-based simulations and engineering models. The system goes back into history to determine exactly how aircraft engines e. After the current point in time, simulations play out multiple possible futures. Different possible scenarios are enumerated for relevant parameters, including flight schedules, engine assignments, service work scopes, demand for worn-out parts, and so on.

In example embodiments, the analysis is constrained based on contractual term limitations pertaining to the asset. The estimated demand may be used as a demand function for shop scheduling automation, making it possible to automatically make choices about parts inventory and cost calculations pertaining to the engine maintenance e. In this complex system, adjusting a risk and return corresponding to one aspect may require a trade off for another risk and return corresponding to another aspect.

For example, something might be economically optimal for a given engine, but suboptimal for service shop operations. And if a service provider changes something on the engine, such as a part or workscope or sequence of activities, to make the shop operations more efficient, it could be the case that the customer is impaired in some way e.

As another example, a customer may be flying an engine with less efficiency or an impairment that would make the engine more appropriate for assignment to a different route or route sequence. Coordination with the service provider for engine assignment or a different service schedule may allow efficiencies to be realized by both the provider and the customer.

In example embodiments, the efficiencies are discovered through modeling of combinations of factors e. The following sections describe some of the benefits and features of the described system. In example embodiments, these benefits are in areas including services scope, life cycle cost, design, system modeling, and asset operations decision support. In example embodiments, we select the scope, timing, or risk level of maintenance activities.

The risk of an asset e. The risk is not deterministic; instead, the disclosed system control changes the industrial system's operation variation which is further transformed into financial variation. It is recognized that the very actions taken with the asset, such as how, where or when it can be operated, add to or take away from the risk of getting to the desired outage time and scope.

In various embodiment any combination of settings or parameters, such as remaining life, operations, maintenance tasks, scope on those tasks, and timing decision variables to be co-optimized, along with other factors spanning revenue from different duty cycles, regulatory limits, service contract limits, market dynamics, may be included in the physical, operational or financial risk calculation.

Industrial systems are comprised of many subsystems or sets of assets. The overall performance relies on the sub systems interactions, which example embodiments control. How these subsystems can fail together is calculated and their probability. The current asset's joint probability estimate of a failure occurring is updated, instead of depending on observations of joint failure that are observable, recorded, and thus available. In example embodiments, the systems within the industrial and business ecosystem are simulated, exposing them to usage which is made a component of the decision space and tracking the life consumption and probabilities by subsystem so that both the duty cycle of the ecosystem and the repair scope and repair timing are made to be endogenous choices.

In example embodiments, the practical reality is considered of changed duty cycles that occur in an industrial system's economic life resulting from competitive forces, regulations, and comparative changes in fuel costs. The example embodiments, system parameter sensitivities are quantified e. Neither the design nor the duty cycle and asset configuration is assumed to be fixed e. The system seeks to bring these endogenously into the decision space at both the design of the asset and during its life so as to beneficially change its design or line-up or configuration or its load profile.

Constraints may be imposed by a governing service contract on one or more assets comprising the engineered industrial system, including regulatory or interconnect or capital constraints. The terms of operation are modeled within the digital twin operations optimization. Instead of treating risk as the inevitable result of a changed instrument reading or exogenous factor, or considered that the risk threshold is a fixed value or that the only risk considered is financial, negative variances in other dimensions of the risk calculation are enabled with the control system, for example, the risk threshold is not a deterministic level with no guidance as to its proper set point or comparative relationship to the potential returns at the said risk limit.

In various embodiments, the level of risk is informed as a comparative value with respect to returns on one or more dimensions of value. In example embodiments, sub system models are orchestrated through simulation time, exposing said simulated system to exogenous factors, operational choices, contracted services terms, capital structure covenants and calculating probabilistic responses for physical, financial and other metrics of value, such as emissions with a temporal consistency, that enforces the lifecycle performance and operating paths resulting from decisions made at any point of the simulated life.

In example embodiments a given power asset and its subsystems are focused on, and not a portfolio of assets, though the methods and systems disclosed herein may be used in a meta model or portfolio or higher order systems of various assets with an overarching financial objective. In example embodiments, a discrete event simulation and replications are used to build probability density functions of interactions, not Bayesian methods.

The currently disclosed systems and methods may overcome many inaccuracies introduced into analysis by including the ability to change how the assets would be operated, dispatched, or modified over multiple time periods. In example embodiments, a service provider may enable or provide a customer with modules for deployment in a client machine, the modules configuring one or more client machines to access modelling tools described herein e. Using the client tools, the customers may be able to experiment with what-if scenarios pertaining to operations of industrial assets e.

In example embodiments, the customer may pay a license foe to the provider in order to access the disclosed system's data and tools used in the forecasting and dynamical optimization of designs, operating policies, schedules, workscopes, and so on. In example embodiments, a provider may use forecasts generated by the tools described herein to underwrite or reunderwrite performance-based, contracts e. Based on forecasts of asset operations and maintenance, a provider may identify under-rewarded risk.

For example, a provider may be able to focus on data pertaining specific engines in order to de-risk existing contracts that are based on average engine assumptions e. In example embodiments, calculations pertaining to underwriting and risk for a contract will depend on particular terms in the contract pertaining to use of the engines.

For example, for a certain engine platform e. However, a customer may have a fleet of 20 such aircraft and put three of those 20 on one-hour city hops, exposing the engines to frequent high temperature excursions which differentially consume life vs average life, whereas the customer may put the other aircraft on transatlantic flights, where the ratio of metal temperature excursions are low per flight hour, extending the life of those engines beyond the average.

A contract may be generated for a customer that includes terms pertaining to the risks associated with the most important identified variables pertaining to a specific use of a particular asset or particular subsystem of the asset. Thus, a portfolio of such contracts between a provider and a contractor would not be based on averages, but instead on modelling of each individual asset based on planned uses and active control of that asset and industrial eco-system with the disclosed system.

In example embodiments, a provider may forecast economic consequences of purchasing new engines or upgrading a set of existing engines. Using the modelling tools, a prospective customer may be able to determine, for example, based on an asset portfolio of aircraft, engines, and 15 spare engines, that a particular upgrade e.

In example embodiments, we consider two cases wherein a first case is that the only data pertaining to maintenance of a first asset manufactured or serviced by a first provider makes up the average data and that the data pertaining to a second asset manufactured or serviced by a second provider is specific historical data pertaining to the specific asset and the planned use of that specific asset by the customer e.

For the second asset, the actual usage of the engine by the customer is simulated over the lifetime of the asset, including all service shop visits and service repairs, based on specific data pertaining to the planned use of the asset including operation policies of the engines, weather through which the engines will be flying, and so on. The second case beneficially draws upon the realized results of the first case and uses asset and duty specific wear.

The operations and service risks that may be forecasted using empirical evidence and first principal models using observed data as a basis for assumptions that the customer doesn't wish to manage may be moved over to the provider's balance sheet in exchange for compensation in the contract to mitigate those risks.

Thus, not only would a customer be more likely to purchase an asset from the second provider, but also the cost of capital associated with the purchase may be reduced based on the increased precision in the risk calculation and thus the enablement of lower risk capital provisions. Such relationships may include, for example, relationships between asset duty assignment, resultant system and sub-component life consumption state, system and sub-component reliability and their effects on service timing, work scope and service operations, and so on.

In various embodiments, the framework may be implemented as a distributed multi-agent physical system control to dynamically optimize risk and value for one or more stakeholders with simulation and optimization, hi example embodiments, operational paths are actively controlled to create different physical states of a plurality of components, assets, and operations associated with a one or more assets comprising an industrial system, so as to achieve the multiple criteria of operators, service providers and investors over a specified time interval, above a specified probability of the one or more outcomes.

In particular, the framework depicts, at FIG. In addition, an alternate life consumption path arises from a different use as compared to the second operational path The scenario path may be an average life consumption path, such as for a single or plurality or portfolio of assets, or it may be a reference design or operating path used to characterize expected asset design performance and operations service levels, in reality, industrial assets may be exposed to unique duty cycles or purposefully operated by the disclosed system to have a non-standard operation so as to cause one or more of the assets' subsystems to achieve a desired physical or operational state probability at one or more points in time and thus a unique path through their economic life.

Generally, component parts and subsystems of the example industrial system may operate over time such that the useful life of those components is consumed during that time. Accordingly, the industrial system needs maintenance from time to time to replace or rebuild the component parts or the entirety of the industrial system. It is beneficially not limited in operation to fully consume useful life, but certain maintenance events and operating control choices can be made at any point in time to benefit one or more objectives of one or more stakeholders at one or more interval s of time.

More specifically with respect to FIG. Exogenous conditions are, for example, those input factors the industrial system is exposed to which cannot be directly controlled by the system in a given design configuration such as, for example relative humidity, barometric pressure, temperature, air pollutants or suspended aerosols.

A duty cycle is, for example, a changed rate of work output, starts and stops of an asset, a location and rate of change of location. In some embodiments the operational path may also be a base case plan to which other plans of operating the industrial system may be compared to. Relative to the first operational path , the second operational path results in a variable power output rate over time , with periods of operation matching a load point whose power output rate is the plan or design operating point for the physical system, a steady-state rate segment during which the system consumes differentially less useful life , and a rate segment which consumes differentially more useful life rate and a shut off rate during which the system does not consume any useful life of the overall asset or for a targeted subsystem , such as a life-limiting apparatus or subsystem component s of the asset.

The design and controlled point of physical state may be at a subsystem of the asset, such as one or more of the pans and subsystems of the asset or at a higher level s than the asset such as, for example, the aircraft and fleets or subsets of a fleet of aircraft. The useful life consumption path , , represents the life consumption for the entire system resulting from the limiting subsystem or part within the system , for example, an engine's combustor.

In some examples, subsystems of the system or asset may be controlled so that the useful life of each subsystem may be managed by identifying upgrades, performing asset configurations e. At the system level, some compensation for physical stress avoidance can be made, such as by setting a control configuration that preserves life of the asset but sacrifices efficiency or life consumption, such as during useful life segment Such configurations, as well as other operations, control, maintenance work scope, and other physical and business aspects of the asset, may help manage economic aspects or other aspects of value related to the engine or its use.

For example, managing operations, control, and maintenance work scope so that the useful life segment is at a desired reliability probability at a certain time is probabilistically accomplished. This control accounts for the historical path up to the present time and calculate the physical states of the tracked and managed subsystems of digital twin state estimation , and then be simulated through candidate operating scenarios , , to a future time at which point the control system is seeking to achieve a physical state of the entire asset or one or more of its subsystems or parts of digital twin to be at a specified damage, life or efficiency or reliability state at that time The targeted states in time may be set by the system to achieve desired operations points such as asset utilization, parts inventory, cash position, as operations examples.

The state condition refers to one or more of physical apparatus damage, remaining useful life, efficiency, reliability, shape, surface roughness measurement, crack length or depth or other physical aspect of an industrial system in whole or in part. The state at any interval of time between time zero to the present or future time , or any incremental segment of time e. The emulation and simulation system is used to calculate the current , future , or any interval of time state condition for any aspect of the system from a specific part or subsystem of a given asset or to the specific asset as a whole or to classes of assets such as, for example, an engine type that is defined as having certain parts or vintage or service history or service contract or other type classification.

The emulation and simulation based control system is used to calculate the state of plane types, specific states of assets assigned to a specific higher level asset such as a given aircraft. At each operation or dispatch or assignment the asset is controlled in a determined manner to achieve the change in state , at a point or interval of time These one or more degradations or repairs are calculated for each increment of time for each tracked subsystem and part for one or more life consumption mode via one or more state estimation engine s , , which accumulate , the specified state conditions and through time , In the historical emulated mode, the actual state estimation causal factors and exogenous conditions are sequenced through time intervals either continuously or in discrete time segments or by event triggers.

To simulate the state estimation into the fixture or over an interval of time that may include operations not physically occurring as yet , the exogenous factors , , in example embodiments, are first simulated in a random walk and then, as the system simulator indexes through future time, value for these damage factors are written to the inputs of the one or more state estimation engines , , , , , the state change is calculated and then accumulated shown, for example, as useful life.

Historical events such as, for example maintenance work, inspections, damage and so on are also managed by the emulator-simulator and consumed into the state estimations In future time , events may be caused to happen as a function of a schedule e. Returning to the state estimations of the asset and its parts and subsystems , , the states may be represented as numerical values or in one embodiment as a color, for example red, yellow and green.

Other indicators may be used such as colors or a change in comparative shape so as to enable the system operators to visually understand the state of the physical asset. In FIG. In various embodiments, the graph of FIG. Depending upon the measures or bounds of state estimation such as described R-Y-G in three summary states, other logic is enabled which consumes one or more of the system state estimations and triggers control actions in response.

For example, a shop visit would be called, if five or more of certain subsystem or parts states transitioned from a yellow state summary to a red state summary. The logical combinations of numerical values, summary states, different aspects and subcomponents of the assets may be as simple or complex as is needed for the triggering control of the system.

In example embodiments, on the vertical, there is some rate of work e. Along the horizontal is time. The dotted lines represent the possible life plans for the engine. The digital twins and represent the application of cumulative damage models to determine the states of different parts or subsystems within the engine at different points in time.

The connotation is that if a customer doesn't stress the engine, the customer is on the lower curve e. If the customer stresses the engine e. At some point, the customer is going to hit a reliability e. Alternatively, the system adjusts duty patterns or maintenance events or work scope to achieve an objective other than maximum life. In example embodiments, a simulation incorporating the digital twins and wraps the cumulative data models to orchestrate where the engines are being flown, how the engines are being flown, how they're coupled to a particular aircraft, the weather conditions in which the engines are to be flown, aspects of how the engines are being controlled, and so on.

The information is fed into the cumulative damage models for each subsystem, which drives the changing in state overtime of the estimated state of the subsystems within each digital twin. Time may be an important element in some embodiments. The physical state estimations are made, for example by one or more cumulative damage models , through time, as a function of how the assets were utilized , and how they will be used in the future as the system is simulated forward or any combination of past and future The individual parts and subsystems may be tracked individually, such as by part serial number or a subsystem clustering of parts or the asset in its entirety.

The asset in its entirety may be characterized by logic which consumes as an input the individual state estimations of the tracked parts and subsystems. These states are dynamic through time as a function of the operating choices made in actual operations and by those choices which are automatically made by the logic of the disclosed control system such as, for example scheduling the asset to a location and subjecting it to local ambient conditions, necessary power output and so on, assigning events to occur such as maintenance, assigning rates of change of power output through time such as for various duty cycles and assigning assets to other assets These states may trigger responses for the asset in its totality, such as a schedule or for a subsystem or part These states may trigger responses directly or be biased or controlled by the physical-business system multi-stakeholder, multi-criteria, multi-preferences optimizer Airlines or customers may have operating policies , such as taxiing out on the runway using one engine to save fuel, using a certain percentage of engine thrust at takeoff, d-rate e.

In example embodiments, the modeling and simulation methods described herein may allow an operator to optimize such operating policies. Assignments to routes may be controlled to happen in future time according to routines for scheduling that are called. Assets may be assigned to higher level systems such as an engine s to a given aircraft. Asset internal operation, control settings and subsystem assignments may be controlled by the simulator and its system optimizer Candidate operating points and physical design may be orchestrated by the Simulation in a design of experiments or full enumeration mode so as to calculate comparative performance between candidate design, operations or control policy.

The physical and business system is emulated, simulated and optimized in this measurement and control system. For example, a service contract's terms, price, risk may be tested and optimized Similarly, other operational aspects such as the number of operating spares, inventory stocking, operating cost or cash flow is enabled.

A given system , , or subsystem , may be optimized for a set of preferences over one or more objectives The simulation also allocates risk, returns and calculates balances by stakeholders related to the operations of assets and their subsystems. Each stakeholder has its own objectives The objectives may be shared, independent or compete against one another.

For example, service provider wants to maximize its service revenue, whereas customer wants to minimize the service provider's revenues. Given these independent objectives, the system probabilistically calculates at higher-level system optimizations that may benefit both stakeholders. Each stakeholder may assign its own conditions or weight to each objective Thus, for example, Objective 1 may have conditions or weightings assigned by customer as well as different conditions or weightings assigned by provider Customer costs and service provider costs are identified and provided as inputs into the optimizer For example, the service provider may acknowledge that a particular customer , such as an airline, makes its money flying passengers and thus is focused on keeping operating costs, including fuel costs, low.

Thus, for example, weightings assigned to operating costs by customer may be higher than ratings assigned to other costs by the customer. The customer may acknowledge that service provider makes it money by providing hardware e. The optimizer looks for an economic operating point at which both stakeholders and can benefit. In example embodiments, the system uses simulations to analyze risk and return preferences and generate a comparison of net present value against variance of the cash flows creating the net present value on an absolute basis or with respect to a plan.

Multiple inputs of the system are orchestrated into the simulation and calculations, for example, the customer's flight schedule , exogenous factors , physical part data , and so on. Thus, for example, the system is configured to determine that, for a chosen set of aircraft, with specific engines, and flown these specific engines with particular loading of the aircraft, running at specific work rates or changes in rates per unit time , at specific locations, running at specific temperatures in particular weather conditions, how the each specific engine will degrade over time, or burn more fuel, or become more unreliable is calculated.

The service provider can thus use the simulation calculations to assist the customer in using the customer's assets more optimally or take on and manage operational risk directly. The system may recommend an intervention in scheduling. For example, assume a customer has an engine that is less efficient on average in a fleet of engines, each having different remaining useful lifes.

Such engines may each have multiple e. Both the service provider and the customer may have an economic interest in keeping the least efficient engines e. The routes that are best suited may be those that wear the engines in a particular way such that their fuel burn is minimized and their need for service is more predictable and can be preplanned or coordinated with the service provider's maintenance shops to minimize maintenance costs for both the service provider and the customer.

Based on simulations, the service provider may better predict when an engine is coming for maintenance, and what kind of maintenance will need to be performed e. Thus, based on the simulations, the service provider's shops may have more advance notice of maintenance needs of each of the specific assets of its customers, allowing it to streamline its capacity use or inventory, such as inventory of expensive e.

For example, the optimizer may identify that a e. The optimizer may identify particular costs e. Based on the identification of the costs and an analysis of the flight schedules of a fleet of aircraft of the customer, the optimizer may identify that a better use for the would be to have it fly across an intercontinental route e. The system may then recommend the better use e. For example, projected savings of customer costs e. Similarly, a projected savings of provider costs e.

Additionally, by coordinating airline schedules across multiple customers, the provider costs associated with airline maintenance may be coordinated and optimized at a particular shop, such that, for example, parts having longer useful lifes in one aircraft engine are swapped with parts in another aircraft engine such that the useful lifes of both engines are maximized. In example embodiments, a service shop may be mixing and matching engine parts, repairing parts, and installing new parts.

The service provider may have a limited amount of time e. If a proper inventory of serviceable used or repaired parts is not available for swapping into an engine e. The cost difference between using a used and new part may be significant. Thus, the service provider may have a strongly weighted objective of having appropriate used or repaired parts available in inventory when an engine arrives in a shop. Suppose a service provider has five years remaining under a contractual agreement to service a customer's engine.

The service provider would incur significant unnecessary costs if it had to use a new part with a useful of 20 years to perform its maintenance duties on such on engine. An appropriate part to use in this case would be a part having a remaining useful life that corresponds with the remaining useful life of the engine as a whole.

Thus, in example embodiments, the optimizer may identify various factors that affect service provider cost with respect to its contractual obligations to a particular customer or a group of customers. Such factors may include flight schedules, asset utilization, inventory, and so on, as depicted in FIGS.

The optimizer can thus provide guidance for getting the right material into the right asset at the right time as a function of the service that the provider is obligated to provide to the customer or group of customers. Such a graph may be presented in a user interface to demonstrate the result of the analyzing of all of the factors depicted in FIGS.

In various embodiments, the pareto frontier represents, for every risk level or variance , the best economic choice that can be made given particular inputs. In at least some embodiments, these and other aspects of the asset may be managed concurrently, such as commercial operations, lineups, maintenance, service work scopes, upgrades, and control points. An example system, described in greater detail below in conjunction with FIG.

The life paths may be for either a new unit or a repaired unit whose state is known certain at a point in time such as when new or from specific inspection or maintenance for any subsystem being tracked. The physical state probabilistic estimations of one or more parts and subsystems are updated with these assessment points and activities. At an operational segment , the corresponding life consumption may be extremely high, yet may be reduced by a control setting which lowers system efficiency, such as allowing more internal airflow or reduced surface temperatures and thus increased specific fuel consumption, in exchange for a reduced life consumption rate for the same power output or less power output but longer duty cycle or other tradeoff.

The operational segment described may also, under other beneficial embodiments, serve as an example of how interaction between operations, maintenance, design, and various business considerations may be employed to automatically change the design parameters and operating characteristics of parts and subsystems to achieve physical states through time that enables the resulting useful life segment to be lower than it otherwise would have been under the same circumstances.

In the industrial system or specific asset being simulated, a scenario may be investigated that beneficially takes advantage of, for example, a peak demand pricing opportunity and specifies the commercial market schedule for the associated extra load The system may also execute a different set of scenario runs using, for example, a catalog of available design modifications, updates, and operational decision policy changes.

Consequently, the scenarios which install a certain control system upgrade which allowed the useful life segment to be reduced, even though subsequently causing more fuel to be consumed, may provide a higher return on investment over the simulated economic lifecycle than other alternatives. A possible base-case scenario is that the asset employs the first operational path to arrive at a specified maintenance event scheduled for time with remaining useful life RUL , which represents a chosen reliability probability e.

As in all tradeoffs being described as examples of the system, the risk and return preferences of one or more stakeholders are automatically optimized Similarly, the service support may consist of one or more repair shops and within those shops are operations such as assigning assets to available capacity, setting work scope, ordering parts , tooling, people and sequencing a plurality of demand streams, such as, for example, the services activities for multiple airlines or the extent of specified maintenance in a given service event.

The shop capacity is modeled with respect to its operations capacity overtime Left uncontrolled by the disclosed system, the shop demand quantity of assets and extent of work scopes may be highly variable and thus prone to periods of underutilization and over subscription. Underutilization squanders variable expense. Too much demand, creates overtime and service level delays in operations which are governed by performance based services contracts, there are typically clauses that govern turnaround time or service level or asset utilization or availability and thus it is highly beneficial to control the demand function, service TAT, Service Level Agreement guarantees SLA and shop cost.

The disclosed system simulates the use of assets to estimate the asset life of efficiency and enables asset sequencing to be optimally set to maximize the benefit of asset use and control the sequence of the shop demand as a function of operations and time and orchestrates shop operations model such as, for example, a discrete event simulation and scheduler so that some or all assets can be assigned by the optimization in such a way as to co-optimize the end user's costs and service providers cost or other stake holder or objective's preference An example objective is to lower total customer and service provider cost.

A stakeholder may be an airline , a service provider or an investor with a financial stake in an aspect of the system. Repair inventory on hand to maintain industrial assets consumes capital when stocked. As the shop demand is controlled, parts ordering is optimized over the forecast interval to minimize inventory count and carrying value. Which parts are used for repairs is also beneficially controlled by the disclosed. As an example, if it is known that an asset will have an assigned duty cycle , whose operations will control a given life consumption , according to a controlled assignment , , over an interval of time , for example a contracted time period, then the ordered parts may be differentiated by their cost and life estimation so as to lower the service producers cost without exceeding their or other stakeholder preferences , and objectives with respect to risk and return The assigned shop is optimally controlled with respect to available machine capacity or skill or used or new inventory or consumables at the one or more repair facilities in the repair value chain.

In example embodiments, the design of the physical system may be changed such as with an available modification and the optimization routine re-run through , , , , , , , , , A stakeholder may be a service provider with a contract that specifies a performance level for another stakeholder such as an operator and the provider requires an upgrade or repair to be made which needs an expenditure to be mad now so as to achieve a risk and return through a time interval of point in future time The requisite investment is made now at by a stakeholder that specifies a certain return and risk level at one or more points of time The system incorporates that investor's preferences in the optimization objective function such that by operations optimization choices , , , , , , , , the cash flows from operations, subject to the performance contract terms, are more assured lower risk return are provide via the deliberately controlled attainment of physical and operational states through time for one or more assets.

In example embodiments, even the current state of the asset e. For example, the last physical inspection of an asset or part or subsystem of the asset may already be in the past. Thus, there may be multiple layers or dimensions of stochasticity. For example, a first layer may be a distribution around the real condition of the engine at T Now ; a second one may be a prediction of the events to which the engine is actually subjected to in the future. Thus, the length tail of the T Now distribution may affect when a customer should schedule an inspection of the asset e.

The simulations may thus not only aid customers and providers in determine how to optimize operation of the asset, but also inspections e. In example embodiments, images are taken of various parts of the engine, such as the blades. The images are captured and scored against certain engineering criteria and compared with CDMs simulations e. The scores may represent the significance of detected surface cracking, root cracking, spallation, dirt, pits, and so on. Thus, upon physical inspection, the simulation data may be confirmed with a certain percentage of accuracy and the CDMs may be updated to account for any differences in actual state versus predicted, state at the time of the inspection.

A causal driver of shop repair delays may be the availability of certain inventory. The pre-order planning control receives component , state information , for specific engines and provides the quantity of components in various stages of remaining useful life, through time to the service shop supply chain for their production estimation purposes with respect to time to produce finished goods which can be made available to the service shop inventory replenishment by part type The pre-order planning may contain the supply chain production model that in turn may optionally contain current production quantities such as through an MES or ERP system.

Said model of the supplier production process may be unable to provide finished goods inventory to replenish service shop inventory for a forecasted demand The airline operations may be biased such that certain engines which would otherwise require shop services be operated via assignment or control set point to lower the rate of life consumption and thus delay the service event time to match the part availability.

The reliability probability may be derived or simulated from engineering models and observation of fielded units, and corrected for climates, load cycles, transient dynamics, repair cycles, subcomponent vendor sources, metal temperatures, and other indicators and drivers that characterize the RUL Further, as depicted in FIGS. Variation in RUL forecasts may result from many factors, operations hours being one such causal variable as depicted in graph Additional factors indicative of RUL , such as repair records, original equipment manufacturer OEM manufacturing and inspection data, or OEM or supplier lot, may be operationally tracked and employed in the simulation or its post processing.

Each major subsystem that is important to the overall asset or system reliability or efficiency probability may be monitored and tracked. RUL , expressed as a probability of two subsystems and , is depicted in graph As illustrated therein, for all RUL estimates, the subsystem is less reliable that subsystem , and thus the subsystem is most likely to be the limiting component in the probability of life reliability probability for the overall asset.

The simulation based state control system is centered on the physical asset as the asset is dispatched and serviced throughout its economic life , The physical asset is comprised of subsystems and parts which accumulate wear, as depicted in FIG. The physical degradation is a function of the operation of the asset and the factors it was exposed to during those operations, for example temperatures, pressures, humidity, suspended aerosols in the ambient air, percent of full load, temperature of metal surfaces, chemistries in ambient air, repair history, inspection results, cycles and rates of change of work output by the asset and other causal factors The simulation hosts one or more damage models for one or more subsystems and parts in the physical asset These degradations may be explained with engineering models, data driven models that use historical observations to train over, accelerated life testing, designs of experiments and regressions of the resultant data, rules, arithmetic relationships or other analytical means as may exist or be conceived.

The only requirement is that the system simulator is able to host the algorithm or lookup table or rules which describe degradation and can pass the assumption data to them for an interval of time such as the simulation clock increment and that those assumption data exist in the historical record and can be emulated to replay a part or asset system's operating life experience and that the future simulation is able to calculate and then pass the life model s assumptions.

For industrial systems, such as, for example, aircraft engines, power turbines, reciprocating engines and common industrial assets—these life consuming causes are well known and the exogenous elements , , temperature, humidity, and so on are able to be simulated along with the specific operating scenarios such as flight schedules , plant dispatch , loads, torque, maintenance scope, topologies, duty cycles and other aspects of their operations. The state of components within the asset , are thus updated through emulated or simulated time as exposure and duty occurs.

The state of subcomponents and the resultant reliability , are determined, by one or more hosted model Failure or degradation state change is calculated by one or more models describing one or more failure modes The means to aggregate wear or failure or performance models may, for example, be from logical rules, joint Monte-Carlo simulation or a use of a rate of change that is most severe over a time interval or weighted average or draw from a stratified life characterization such as a Weibull distribution or any other means as may be available in the art to correlate exposure to degradation , The threshold of reliability risk from point e.

In the illustrative example of FIGS. This set point e. A comparison over a forecast interval of physical state or operating performance resulting from differential usage, service, and design or operating policy is compared over a time interval and an economic difference between the two scenarios is calculated. In a similar way, the resulting FCF of a user of the assets such as an airline or power generation or rail locomotion customer do change depending upon the change in the system's efficiency, reliability, and service outage time, workscope flexibility to be dispatched or scheduled or used in any other way.

The FCF is probabilistic and is calculated by the simulation system for a scenario design and operations and then replicated to account for exogenous condition variability , , Variances of the absolute value of cash flows or differences of cash flows to business plans or absolute values are also tabulated for scenarios and replications. NPV and variances are related and different stakeholders in the industrial system's performance have different preferences for the relationship between risk variance and return IRR, NPV, PV, or real option value.

Multiple objectives may be present and solved for by the simulation-optimization based control system being disclosed. The control tradeoffs to achieve a physical state at a point in time is achieved by jointly changing the operating decisions related to scheduling the duty pattern or schedule of assets , managing intervention events such as maintenance and inspections , assigning higher level assets to a duty such as an aircraft to a schedule, assigning assets to higher level assets such as engines to an aircraft Triggering control set points and maintenance of a system and its subcomponent parts and subsystems, changing the design or configuration of industrial systems, assigning services , ordering service inventory arrivals, setting or modifying the commercial contract terms or price for services As an example, consider the cumulative work corresponding to an output through time of an aircraft engine, such as thrust.

The plan rate of work and cumulative work for operating an engine at an amount of thrust over time, subject to the average planned severity of an average flight where the engine will be flown, and within specified operating policies, there is a probability of failure For example, if the asset is on planned line at time , there is no probability of engine disruption based on useful life degradation i.

However, when the asset is on at time , there is a probability of failure, which rises as time progresses and the asset is utilized such that the operating plan to time to intersection point , where the probability of failure is represented at point On average, an engine might be exposed to factors that differentially consume life such as a certain set of dirty air, relative humidity, or temperature, but, in the specific e.

Thus, for example, a specific engine may be off of the planned line and on the specific life path At specific life path , new instructions have been provided by the computing control system and implemented for operations of the engine based on recommendations of the optimizer. For example, the aircraft has been moved from a short hop biased route structure to a transocean route bias, is flying with less takeoff power, is flying through less harsh environments, is using runways more efficiently.

At , the engine is scheduled to come in for service; however, after time , the engine is below the planned line Thus, at , the operating instructions may be changed again for the engine, and it is deployed in conditions in which its useful life is degraded more quickly. Thus, based on the simulation based control system, it is purposely operated such that its probability of failure may reach point by time Another specific engine path shows that an engine has been operating well below is planned line Given this data, at particular points in time, the system may recommend the swapping of the engine on specific life paths , , , , with the engine on specific life paths , , , , Thus, both engines may be utilized more optimally with respect to their useful lifes and maintenance schedules.

The determinations of engine swaps may be made by the system such that the average specific life paths of engines across a fleet of aircraft come closer to planned line Power, energy and environmental technology 2. Refinancing facility for AKA 4. Additional global refinancing facility for AKA 6.

Total loan and grant commitments. Loan and guarantee amounts mostly to small and medium-sized German companies for which commercial banks assume primary liability to the KfW Group 4. Commercial risk to the KfW Group after valuation of collateral and other security arrangements 5. Schuldscheindarlehen 3. Hans W. Reich official spokesman. Loans and advances to banks 2. Loans and advances to customers 2 3. Bonds and other fixed-rate securities 2.

Shares and other non-fixed rate securities. Investments and investments and shares in affiliated enterprises. Compensation claims on public authorities 4. Balance Sheet Total. Liabilities to customers 1 2. Securitized liabilities bonds. Funds borrowed from customers 1. Funds borrowed from customers:. Bearer bonds including medium-term notes. Less outstanding contributions. In accordance with Art. General administrative expenses 4. Other operating income and expenses.

Income from current operations before risk provisions and valuations. Income from current operations. Social security contributions. Expenditure on pensions and support. Total expenditure on personnel. Other administrative expenses. Depreciation and value adjustments on intangible and tangible assets. Total expenditure on material and equipment. Total administrative expenses. Balance with Deutsche Bundesbank. Balance in postal giro accounts. Loans and advances to customers.

Bonds and other fixed-rate securities. Bonds and debentures of public and other issuers 3. Shares in affiliated enterprises. Loans on a trust basis and other trust business. Compensation claims on public authorities including bonds from their conversion. Prepaid expenses and deferred charges. Special loss account consisting of provisions under Art. With agreed terms or periods of notice. Accrued estimated liabilities. Compensation liabilities under Art. On bills discounted and charged.

Liability under collateral provided for third party liabilities. Repurchase obligations on non-genuine repurchase transactions. Placing and underwriting obligations. From lending and money market. From fixed-interest securities and debt register claims. From shares and other fixed rate securities. From shares in affiliated enterprises.

Commissions and similar service charges earned. Net earnings on financial transactions. Earnings on allocations of investments, shares in affiliated enterprises and securities treated as fixed assets. Commissions and similar service charges payable.

Net loss on financial transactions. General administrative expenses. Social security contributions and expenditure on pensions and support. Write-downs of and value adjustments on loans and certain securities and increases of allowances for possible loan losses 2. Write-downs of and value adjustments on investments, shares in affiliated enterprises and securities treated as fixed assets. Write-downs on the special loss account under Art.

Investments in affiliated enterprises. Compensation claims on public authorities, including bonds from their conversion. Cash flows from operating activities. Net income before income tax. Non-cash positions in net income and adjustments to reconcile net income to net cash provided by operating activities. Write-downs, depreciation and adjustments.

Change in other non-cash positions. Other non-cash income and expenses. Profit from the sale of financial instruments. Profit from the sale of investments, property and equipment. Change in assets and liabilities from operating activities after correction for non-cash components. Securities available for sale. Promissory notes and other liabilities evidenced by paper.

Other liabilities from operating activities. Interest receipts incl. Interest payments incl. Net cash provided by operating activities. Cash flows from investing activities. Proceeds from the sale of investments. Proceeds from the sale of property and equipment.

Payments for the acquisition of investments. Payments for the acquisition of property and equipment. Net cash provided by investing activities. Cash flows from financing activities. Other financing activities net. Net cash provided by financing activities. Cash and cash equivalents at the end of previous period. Effect of exchange rate changes on cash and cash equivalents. Cash and cash equivalents at the end of period. Affiliated enterprises. Enterprises in which KfW holds a participation. Securities treated as fixed assets.

Other borrowed funds with agreed term or period of notice. Funds borrowed from customers. With agreed term or period of notice. Contracts with interest rate risks. Standstill obligations on swap options. Contracts with currency risks. Contracts with other price risks. Share options under standstill agreements. Staff not on collective wage agreements. Staff on collective wage agreements. Acquisitions and Investments.

Export and Project Finance. Financial Cooperation. Advisory and Other Services. Loan Loss Provisions. Board of Management. Notes on Liabilities. Other Notes Required. Funded Debt Outstanding. International Organizations. Key Economic Figures. European Integration. Gross Domestic Product. Sectors of the Economy. Employment and Labor. Social Security Legislation. Deutsche Bundesbank. Background of the ESCB. Money Supply and Prices. External Positions of Banks.

Receipts and Expenditures. Government Participations. Participation in syndicated financing activities outside the EU, the European Economic Area and the countries currently being considered for EU membership, subject to certain conditions, and sole financing activities in countries in which sufficient sources of financing do not exist.

Participation in projects deemed to be in the interest of the EU, such as the trans-European networks in the areas of transport, telecommunications and energy infrastructure. Supporting pioneer investors in new countries and regions; and. Strengthening local capital markets through financial sector development. Loans outstanding.

Investment finance. Export and project finance. Financial cooperation. Loan commitments 1. Commitments represent the volume of funds committed for loans and other business transactions including guarantees in the relevant year, including amounts to be disbursed in future years, and do not include amounts disbursed in the relevant year pursuant to commitments made in prior years. Totals may not add due to rounding. Loan programs in Germany:.

Other structural measures. Project finance in Germany:. Total loans in Germany. Loan programs in Europe:. Environmental protection. Total loans in Europe. Securitization transactions:. Commitments represent the volume of funds committed for loans and other business transactions in the relevant year, including amounts to be disbursed in future years, and do not include amounts disbursed in the relevant year pursuant to commitments made in prior years. Discrepancies in the totals are due to rounding.

CANNON 25 FOREX

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Hydrogeologie — [in German]. Waste Manag — Haude W Determination of evapotranspiration by an approach as simple as possible. Hoyningen-Huene JV Interception of precipitation in agricultural crops. Gustav Fischer Verlag Jena, Stuttgart. Knorr K-H, Blodau C Experimentally altered groundwater inflow remobilizes acidity from sediments in a iron rich and acidic lake.

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Limnologica — and Erratum, Limnologica Technical manual, V 1. Goldschmidt Conf Abstr Miner Mag 75 3 Australian Centre for Geomechanics, WA. Werner F, Bilek F, Luckner L a Implications of predicted hydrologic changes of Lake Senftenberg as calculated using water and reactive mass budgets. Mine Water Environ — Werner F, Bilek F, Luckner L b Impact of regional groundwater flow on the water quality of an old post-mining lake.

Ecol Eng — Download references. This paper is dedicated to Karl-Heinz Pokrandt on the occasion of his retirement from the LMBV mbH; we acknowledge his commitment, persistent cooperation, and helpful support on many projects. The authors are indebted to all associates of the Lausitz and Central-German Mining Admin Company involved in this research. Special thanks are due to Eckhard Scholz, head of the Geotechnics department. We thank Bob Kleinmann and four anonymous reviewers for helpful suggestions on an earlier draft, and Thomas Voltz for linguistic improvements.

Correspondence to Kai-Uwe Ulrich. All papers published in this journal are initally published electronically; all subscribers can freely access the electronic versions, which sometimes contain additional tables and figures, using their membership number. Reprints and Permissions. Ulrich, KU. Mine Water Environ 31, — Download citation.

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Prior to founding Langham Capital, Mr. He is also a Director of various companies. You may be trying to access this site from a secured browser on the server. Please enable scripts and reload this page. Turn on more accessible mode. Turn off more accessible mode. Skip Ribbon Commands. Skip to main content. This Site. Reach Us Contact Us. Invest Online. Prior to joining Pioneer, Mr.

Lin holds a B. During this period, Mr. Mehrotra has also handled varying matters under the Companies Act, and the Income Tax Act, , in addition to handling takeovers, mergers, acquisitions etc. Currently Mr. Mehrotra serves as the official advisor to the High Commission of Malta, New Delhi, in addition to being director on various companies of repute.

He has over two decades of experience in business process outsourcing, investment banking and management consulting. Nevertheless, the deal was criticised at the time for its aggressively low pricing. As Kremer explained at the time, the plan was to place the old Activest master KAG as well as fund administration for third parties and German assets in a new specialised company focusing on back and some middle office functions as well as fund administration.

He stated in May of this year, the firm was still looking for a strategic partner for the entity to be known as Pioneer Investments Fund Services. The KAG really needs to decide and I'm sure they will go in that direction," he said earlier this year. Site powered by Webvision Cloud. Skip to main content Skip to navigation. Pioneer hives off German admin business to SocGen. No comments. That firm has now been sold to SSGS, as the latest arrangement states. No comments yet.

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Markets Weekly Market Monitor. Monthly Market Insights. Quarterly Compass Update. See More. The US business will adopt the Amundi logo. Learn more: MarketsInTransition Russell amundipioneer. In a low real rate environment, we think investors should balance the need for higher yields with the need to focus on quality credit at attractive valuations, all while maintaining sufficient liquidity.

Quality and sustainability are once again key factors for investors. ESG themes, with a focus on social and green, are becoming mainstream. From our November Global Investment Views:. Election uncertainty puts the Biden trade to the test and may boost volatility, healthcare and the US Dollar. Read our first take report on the election:.

Register here: event. Finding the sweet spot: as we transition to a world of higher inflation, how might we change US equity exposure in portfolios? Is it too soon to talk about value? Read here how we believe a portfolio shift could change the story. Turn on more accessible mode. Turn off more accessible mode. Skip Ribbon Commands. Skip to main content.

This Site. Reach Us Contact Us. Invest Online. Prior to joining Pioneer, Mr. Lin holds a B. During this period, Mr. Mehrotra has also handled varying matters under the Companies Act, and the Income Tax Act, , in addition to handling takeovers, mergers, acquisitions etc. Currently Mr. Mehrotra serves as the official advisor to the High Commission of Malta, New Delhi, in addition to being director on various companies of repute.

He has over two decades of experience in business process outsourcing, investment banking and management consulting. He has advised Fortune corporations on their India entry strategy including identifications, negotiations and selection of joint venture partners.

Arora is also the founder director of AR Credit. Arora is a fellow member of the Institute of Chartered Accountants of India. Mutual Fund investments are subject to market risks, read all scheme related documents carefully.