Refine
Document Type
- Conference Proceeding (22) (remove)
Institute
- Forschungszentrum Energie (5)
- Forschungszentrum Business Informatics (4)
- Forschungszentrum Digital Factory Vorarlberg (4)
- Forschungszentrum Human Centred Technologies (4)
- Technik | Engineering & Technology (3)
- Department of Computer Science (Ende 2021 aufgelöst; Integration in die übergeordnete OE Technik) (2)
- Forschung (2)
- Forschungszentrum Mikrotechnik (2)
- Josef Ressel Zentrum für Intelligente Thermische Energiesysteme (2)
- Gestaltung (1)
Has Fulltext
- yes (22) (remove)
Keywords
- Cloud manufacturing (2)
- Ontologies (2)
- Auction-based production planning (1)
- Business analytics (1)
- Controlling (1)
- Creativity (1)
- Design Thinking (1)
- Digital boardgame (1)
- Digital twin (1)
- Distributed manufacturing (1)
Power plant operators increasingly rely on predictive models to diagnose and monitor their systems. Data-driven prediction models are generally simple and can have high precision, making them superior to physics-based or knowledge-based models, especially for complex systems like thermal power plants. However, the accuracy of data-driven predictions depends on (1) the quality of the dataset, (2) a suitable selection of sensor signals, and (3) an appropriate selection of the training period. In some instances, redundancies and irrelevant sensors may even reduce the prediction quality.
We investigate ideal configurations for predicting the live steam production of a solid fuel-burning thermal power plant in the pulp and paper industry for different modes of operation. To this end, we benchmark four machine learning algorithms on two feature sets and two training sets to predict steam production. Our results indicate that with the best possible configuration, a coefficient of determination of R^2 = 0.95 and a mean absolute error of MAE=1.2 t/h with an average steam production of 35.1 t/h is reached. On average, using a dynamic dataset for training lowers MAE by 32% compared to a static dataset for training. A feature set based on expert knowledge lowers MAE by an additional 32 %, compared to a simple feature set representing the fuel inputs. We can conclude that based on the static training set and the basic feature set, machine learning algorithms can identify long-term changes. When using a dynamic dataset the performance parameters of thermal power plants are predicted with high accuracy and allow for detecting short-term problems.
Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
Bubble column humidifiers (BCHs) are frequently used for the humidification of air in various water treatment applications. A potential but not yet profoundly investigated application of such devices is the treatment of oily wastewater. To evaluate this application, the accumulation of an oil-water emulsion using a BCH is experimentally analyzed. The amount of evaporating water vapor can be evaluated by measuring the humidity ratio of the outlet air. However, humidity measurements are difficult in close to saturated conditions, as the formation of liquid droplets on the sensor impacts the measurement accuracy. We use a heating section after the humidifier, such that no liquid droplets are formed on the sensor. This enables us a more accurate humidity measurement. Two batch measurement runs are conducted with (1) tap water and (2) an oil-water emulsion as the respective liquid phase. The humidity measurement in high humidity conditions is highly accurate with an error margin of below 3 % and can be used to predict the oil concentration of the remaining liquid during operation. The measured humidity ratio corresponds with the removed amount of water vapor for both tap water and the accumulation of an oil-water emulsion. Our measurements show that the residual water content
in the oil-water emulsion is below 4 %.
With the digitalisation, and the increased connectivity between manufacturing systems emerging in this context, manufacturing is shifting towards decentralised, distributed concepts. Still, for manufacturing scenarios manual input or augmentation of data is required at system boundaries. Especially in distributed manufacturing environments, like Cloud Manufacturing (CMfg) systems, constant changes to the available manufacturing resources and products pose challenges for establishing connections between them. We propose a feature-oriented representation of concepts, especially from the manufacturing domain, which serves as the basis for (semi-) automatically linking, e.g., manufacturing resources and products. This linking methodologies, as well as knowledge inferred using it, is then used to support distributed manufacturing, especially in CMfg environments, and enhance product development. The concepts and methodologies are to be evaluated in a real world learning factory.
Business Analytics zählt zu den Zukunftsthemen im Controlling. In der Controllinglehre spielt Analytics bisher aber nur eine untergeordnete Rolle. Der Beitrag beschreibt ein innovatives Lehrprojekt, das Studierende im Masterstudium Accounting, Controlling & Finance an der FH Vorarlberg befähigt, controllingrelevante Fragestellungen im Kontext von Business Analytics eigenständig zu beantworten. Gleichzeitig erlernen die Studierenden den Umgang mit der Open-Source-Software R.
The increasing digitalisation of daily routines confronts people with frequent privacy decisions. However, obscure data processing often leads to tedious decision-making and results in unreflective choices that unduly compromise privacy. Serious Games could be applied to encourage teenagers and young adults to make more thoughtful privacy decisions. Creating a Serious Game (SG) that promotes privacy awareness while maintaining an engaging gameplay requires, however, a carefully balanced game concept. This study explores the benefits of an online role-playing boardgame as a co-designing activity for creating SGs about privacy. In a between-subjects trial, student groups and educator/researcher groups were taking the roles of player, teacher, researcher and designer to co-design a balanced privacy SG concept. Using predefined design proposal cards or creating their own, students and educators played the online boardgame during a video conference session to generate game ideas, resolve potential conflicts and balance the different SG aspects. The comparative results of the present study indicate that students and educators alike perceive support from role-playing when ideating and balancing SG concepts and are happy with their playfully co-designed game concepts. Implications for supporting SG design with role-playing in remote collaboration scenarios are conclusively synthesised.
Introducing 3D sub-micrometer technologies based on polymers opened new possibilities of design and fabrication of photonic devices and components in 3D arrangement. 3D laser lithography is direct writing process based on two photon polymerization exhibiting high accuracy and versatility, where numerous resists and even polymer ceramic mixtures can be used. We present design and simulation of polymer based photonic components with a focus on arrayed waveguide gratings (AWG) based on optical multiplexers/demultiplexers and optical splitters. All optical components were designed for 1550 nm operating wavelength, applying two commercial photonics tools. This study creates a basis for the design of optical components in 3D arrangement, which will be fabricated by 3D laser lithography.