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In the past, CSP and PV have been seen as competing technologies. Despite massive reductions in the electricity generation costs of CSP plants, PV power generation is - at least during sunshine hours - significantly cheaper. If electricity is required not only during the daytime, but around the clock, CSP with its inherent thermal energy storage gets an advantage in terms of LEC. There are a few examples of projects in which CSP plants and PV plants have been co-located, meaning that they feed into the same grid connection point and ideally optimize their operation strategy to yield an overall benefit. In the past eight years, TSK Flagsol has developed a plant concept, which merges both solar technologies into one highly Integrated CSP-PV-Hybrid (ICPH) power plant. Here, unlike in simply co-located concepts, as analyzed e.g. in [1] – [4], excess PV power that would have to be dumped is used in electric molten salt heaters to increase the storage temperature, improving storage and conversion efficiency. The authors demonstrate the electricity cost sensitivity to subsystem sizing for various market scenarios, and compare the resulting optimized ICPH plants with co-located hybrid plants. Independent of the three feed-in tariffs that have been assumed, the ICPH plant shows an electricity cost advantage of almost 20% while maintaining a high degree of flexibility in power dispatch as it is characteristic for CSP power plants. As all components of such an innovative concept are well proven, the system is ready for commercial market implementation. A first project is already contracted and in early engineering execution.
Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method. Examples are given for testing goodness-of-fit in a one-sample situation in [1] or for testing marginal homogeneity on the basis of a paired sample in [2]. Here, the test statistics in use can be seen as generalizations of the well-known Cramérvon-Mises test statistics in the one-sample and two-samples case. The treatment of other testing problems is possible as well. By using the theory of U-statistics, for instance, asymptotic null distributions of the test statistics are obtained as the sample size tends to infinity. Standard continuity assumptions ensure the asymptotic exactness of the tests under the null hypothesis and that the tests detect any alternative in the limit. Simulation studies demonstrate size and power of the tests in the finite sample case, confirm the theoretical findings, and are used for the comparison with concurring procedures. A possible application of the general approach is inference for stock market returns, also in high data frequencies. In the field of empirical finance, statistical inference of stock market prices usually takes place on the basis of related log-returns as data. In the classical models for stock prices, i.e., the exponential Lévy model, Black-Scholes model, and Merton model, properties such as independence and stationarity of the increments ensure an independent and identically structure of the data. Specific trends during certain periods of the stock price processes can cause complications in this regard. In fact, our approach can compensate those effects by the treatment of the log-returns as random vectors or even as functional data.
The fourth industrial revolution presents a multitude of challenges for industries, one of which being the increased flexibility required of manufacturing lines as a result of increased consumer demand for individualised products. One solution to tackle this challenge is the digital twin, more specifically the standardised model of a digital twin also known as the asset administration shell. The standardisation of an industry wide communications tool is a critical step in enabling inter-company operations. This paper discusses the current state of asset administration shells, the frameworks used to host them and their problems that need to be addressed. To tackle these issues, we propose an event-based server capable of drastically reducing response times between assets and asset administration shells and a multi-agent system used for the orchestration and deployment of the shells in the field.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.
Process mining gets more and more attention even outside large enterprises and can be a major benefit for small and medium sized enterprises (SMEs) to gain competitive advantages. Applying process mining is challenging, particularly for SMEs because they have less resources and process maturity. So far, IS researchers analyzed process mining challenges with a focus on larger companies. This paper investigates the application of process mining by means of a case study and sheds light into the particular challenges of an IT SME. The results reveal 13 SME process mining challenges and seven guidelines to address them. In this way, the paper contributes to the understanding of process mining application in SME and shows similarities and differences to larger companies.
Digital start-ups are perceived as an engine for innovation and job promotor. While success factors for non-IT start-ups have already been extensively researched, this study sheds light on digital entrepreneurs, whose business model relies primarily on services based on digital technologies. Applying the Grounded Theory method, we identify relevant environmental success factors for digital entrepreneurs. The study’s research contribution is threefold. First, we provide 16 relevant and less relevant environmental success factors, which enables a comparison with prior identified factors. We found out that several prior environmental success factors, such as accessibility to transportation or the availability of land and facilities are less relevant for a digital entrepreneur. Second, we derive and discuss hypotheses for the influence of these factors on digital start-up success. Third, we present a theoretical model that lays the foundation for explaining the environmental influence on digital
entrepreneurship success.
Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures.
The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.
Non-intrusive measuring techniques have attained a lot of interest in relation to both hydraulic modeling and prototype applications. Complimenting acoustic techniques, significant progress has been made for the development of new optical methods. Computer vision techniques can help to extract new information, e. g. high-resolution velocity and depth data, from videos captured with relatively inexpensive, consumer-grade cameras. Depth cameras are sensors providing information on the distance between the camera and observed features. Currently, sensors with different working principles are available. Stereoscopic systems reference physical image features (passive system) from two perspectives; in order to enhance the number of features and improve the results, a sensor may also estimate the disparity from a detected light to its original projection (active stereo system). In the current study, the RGB-D camera Intel RealSense D435, working on such stereo vision principle, is used in different, typical hydraulic modeling applications. All tests have been conducted at the Utah Water Research Laboratory. This paper will demonstrate the performance and limitations of the RGB-D sensor, installed as a single camera and as camera arrays, applied to 1) detect the free surface for highly turbulent, aerated hydraulic jumps, for free-falling jets and for an energy dissipation basin downstream of a labyrinth weir and 2) to monitor local scours upstream and downstream of a Piano Key Weir. It is intended to share the authors’ experiences with respect to camera settings, calibration, lightning conditions and other requirements in order to promote this useful, easily accessible device. Results will be compared to data from classical instrumentation and the literature. It will be shown that even in difficult application, e. g. the detection of a highly turbulent, fluctuating free-surface, the RGB-D sensor may yield similar accuracy as classical, intrusive probes.
The recent amendment to the Ethernet physical layer known as the IEEE 802.3cg specification, allows to connect devices up to a distance of one kilometer and delivers a maximum of 60 watts of power over a twisted pair of wires. This new standard, also known as 10BASE-TIL, promises to overcome the limits of current physical layers used for field devices and bring them a step closer to Ethernet-based applications. The main advantage of 10BASE- TIL is that it can deliver power and data over the same line over a long distance, where traditional solutions (e.g., CAN, IO-Link, HART) fall short and cannot match its 10 Mbps bandwidth. Due to its recentness, IOBASE- TIL is still not integrated into field devices and it has been less than two years since silicon manufacturers released the first Ethernet-PHY chips. In this paper, we present a design proposal on how field devices could be integrated into a IOBASE-TIL smart switch that allows plug-and-play connectivity for sensors and actuators and is compliant with the Industry 4.0 vision. Instead of presenting a new field-level protocol for this work, we have decided to adopt the IO-Link specification which already includes a plug-and-play approach with features such as diagnosis and device configuration. The main objective of this work is to explore how field devices could be integrated into 10BASE-TIL Ethernet, its adaption with a well-known protocol, and its integration with Industry 4.0 technologies.
The development of protype applications with sensors and actuators in the automation industry requires tools that are independent of manufacturer, and are flexible enough to be modified or extended for any specific requirements. Currently, developing prototypes with industrial sensors and actuators is not straightforward. First of all, the exchange of information depends on the industrial protocol that these devices have. Second, a specific configuration and installation is done based on the hardware that is used, such as automation controllers or industrial gateways. This means that the development for a specific industrial protocol, highly depends on the hardware and the software that vendors provide. In this work we propose a rapid-prototyping framework based on Arduino to solve this problem. For this project we have focused to work with the IO-Link protocol. The framework consists of an Arduino shield that acts as the physical layer, and a software that implements the IO-Link Master protocol. The main advantage of such framework is that an application with industrial devices can be rapid-prototyped with ease as its vendor independent, open-source and can be ported easily to other Arduino compatible boards. In comparison, a typical approach requires proprietary hardware, is not easy to port to another system and is closed-source.