Refine
Year of publication
- 2018 (252) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (67)
- Fachbereich Elektrotechnik und Informationstechnik (43)
- IfB - Institut für Bioengineering (39)
- INB - Institut für Nano- und Biotechnologien (25)
- Fachbereich Maschinenbau und Mechatronik (24)
- Fachbereich Luft- und Raumfahrttechnik (23)
- Fachbereich Chemie und Biotechnologie (22)
- Fachbereich Energietechnik (22)
- Fachbereich Wirtschaftswissenschaften (20)
- Fachbereich Bauingenieurwesen (16)
Has Fulltext
- no (252) (remove)
Document Type
- Article (125)
- Conference Proceeding (74)
- Part of a Book (31)
- Book (11)
- Conference: Meeting Abstract (2)
- Doctoral Thesis (2)
- Patent (2)
- Working Paper (2)
- Conference Poster (1)
- Other (1)
Keywords
- Datenschutz (2)
- Digitale Transformation (2)
- Energy efficiency (2)
- Engineering optimization (2)
- Literaturanalyse (2)
- MINLP (2)
- Pump System (2)
- Serious Game (2)
- Water (2)
- Agility (1)
Die Batterie ist eine der absolut zentralen Komponenten des Elektrofahrzeugs. Die serielle Entwicklung und Produktion dieser Batterien und die Verbesserung der Leistungen wird entscheidend für den Erfolg der Elektromobilität sein. Die Batterie ist jedoch nicht das einzige elektrofahrzeugspezifische System, das neu entwickelt, umkonzipiert oder verbessert werden muss. So sind ebenso die Entwicklung der neuen Fahrzeugstruktur sowie des elektrifizierten Antriebsstranges Teil dieses Kapitels. Weiterhin wird ein Blick auf das bedeutende Thema des Thermomanagements geworfen.
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
Around 60% of the paper worldwide is made from recovered paper. Especially adhesive contaminants, so called stickies, reduce paper quality. To remove stickies but at the same time keep as many valuable fibers as possible, multi-stage screening systems with several interconnected pressure screens are used. When planning such systems, suitable screens have to be selected and their interconnection as well as operational parameters have to be defined considering multiple conflicting objectives. In this contribution, we present a Mixed-Integer Nonlinear Program to optimize system layout, component selection and operation to find a suitable trade-off between output quality and yield.
Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the
system’s resilience can be engineered
To increase pressure to supply all floors of high buildings with water, booster stations, normally consisting of several parallel pumps in the basement, are used. In this work, we demonstrate the potential of a decentralized pump topology regarding energy savings in water supply systems of skyscrapers. We present an approach, based on Mixed-Integer Nonlinear Programming, that allows to choose an optimal network topology and optimal pumps from a predefined construction kit comprising different pump types. Using domain-specific scaling laws and Latin Hypercube Sampling, we generate different input sets of pump types and compare their impact on the efficiency and cost of the total system design. As a realistic application example, we consider a hotel building with 325 rooms, 12 floors and up to four pressure zones.