Projects

Here, the PhDs present their research projects - from control and automation engineering in a newly emerging smart energy lab to digitalization in the wood or steel industries to optimization and fault detection in the field of electrical power generation and distribution - our topics are very different and yet very closely related. All projects can be summarized under the umbrella of Smart Industrial Concepts and deal with small but important steps towards a sustainable future in the field of industrial energy systems.

Overview

PhD #1 will work on the advancement of models for real-time optimization (MI(N)LP) and development of simulation models via white-, black- and grey-box modeling. They will test these models and algorithms during operation and use them as decision-making support tools in thermal energy systems.

PhD #1 and PhD #2 make a significant contribution to building the new intelligently distributed energy laboratory of TU Wien’s Institute of Energy Systems and Thermodynamics.

PhD #1 works in close collaboration with TU Wien’s Institute of Mechanics and Mechantronics.

PhD #2 will work on knowledge engineering in the field of industrial energy systems for the development of an information model for a functional Digital Twin. For this purpose, the implementation of Machine Learning algorithms (Neural Networks, Naive Bayes, Support Vector Machines, Genetic Algorithms, etc.) for fault prediction and decision support in thermal energy systems will be researched and applied.

PhD #1 and PhD #2 make a significant contribution to building the new intelligently distributed energy laboratory of TU Wien’s Institute of Energy Systems and Thermodynamics.

PhD #2 cooperates closely with the research area Automation Systems of the Institute of Computer Engineering at TU Wien.

Robot hand, between index finger and thumb a piece of a ironpolywood

© Freepik by rawpixel; BY-SA 4.0

Description of the initial situation

Many industrial processes would be virtually inconceivable today without digital tools. Digitization brings optimization and increased efficiency and thus a reduction in the consumption of energy and raw materials. Thus, digitization is also in the spirit of sustainability.

The combination of established production processes with modern, highly networked technologies is expected to bring about the fourth industrial revolution and enable decentralized, flexible, customizable and efficient manufacturing in all sectors [1]. One of the concepts that combines connectivity and (artificial) intelligence is the digital twin - the virtual, computer-controlled counterpart of a real, physical entity [2]. 

Engineered wood products such as particleboard or fiberboard have long been used for their customizable material properties and also have economic and environmental advantages over other building materials [4,5]. Coating wood-based panels with melamine-formaldehyde resin-impregnated decorative papers brings a variety of different aesthetics, as well as protection against mechanical and chemical damage and good hygienic properties [6].

Back-cooling presses with long press times are commonly used for this coating, although faster short-cycle presses are also available. Process knowledge about short cycle presses is mainly empirical; physical and chemical processes in short cycle pressing are only known phenomenologically. This means that finding the cause of quality deviations is difficult, lengthy and unsustainable. A digital, physical model in the sense of a Digital Model according to [2] with the possibility of extension to a Digital Shadow or Digital Twin should expand process knowledge and store it sustainably for the project partner.

The quality of the coated surfaces depends mainly on the pressing conditions (temperature, pressure, humidity) and the curing behavior of the resins [6]. These parameters will be systematically modeled and experimentally investigated to find correlations with quality deviations.

The digital model will be used to pave the way towards a Digital Twin for the short-cycle press. The use of Digital Twins in manufacturing has been proofen well suited to optimize entire processes and increase productivity [2,7]. Data analysis in production can find causes of errors, optimize supply chains and increase manufacturing efficiency [8].

Intention and goals

The aim of the dissertation is to use a mathematical digital model to better understand the processes in the short-cycle press for coating wood-based panels, which have so far been known purely empirically, in order to ensure optimum operation of the plant and to be able to quickly identify the causes of defects and errors.

First, it is necessary to understand the short-cycle press process and how it fits into the production line. To this end, background knowledge on the materials, parameters, processes and production steps used is collected and processed. This includes extensive literature search as well as the analysis of process data and records available at the project partner.

The modeling, especially of the central pressing process, is initially done manually. The model is to start as a simple, one-dimensional representation of the temperature curve and be extended in complexity in further steps as required.

In order to be able to make predictions that are as close to reality as possible, all parameters required for the model are to be analyzed in detail. Once a model is sufficiently mature, it should be validated with the aid of real process data. At points where no data are currently being recorded in the pressing process, it should be checked whether one-off experimental measurements or permanently installed measuring equipment can significantly improve the predictions of the model.

Technical inadequacies of the plant as well as the associated measuring devices could arise in the course of the dissertation and complicate the modeling and optimization. In order to make the digitization of the short-cycle press plant sustainable, the measuring devices, digital model and process parameters should be brought into line.

Publications in this context

[1]   Lasi H, Fettke P, Kemper H-G, Feld T, Hoffmann M. Industry 4.0. Bus Inf Syst Eng 2014;6(4):239–42.
[2]   Kritzinger W, Karner M, Traar G, Henjes J, Sihn W. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 2018;51(11):1016–22.
[3]   WKO. Branchenbericht Holzindustrie Österreich 2019/2020.
[4]   Winchester N, Reilly JM. The economic and emissions benefits of engineered wood products in a low-carbon future; 2020.
[5]   Badila M, Jocham C, Zhang W, Schmidt T, Wuzella G, Müller U et al. Powder coating of veneered particle board surfaces by hot pressing. Progress in Organic Coatings 2014;77(10):1547–53.
[6]   Kandelbauer A, Wuzella G, Mahendran A, Taudes I, Widsten P. Using isoconversional kinetic analysis of liquid melamine-formaldehyde resin curing to predict laminate surface properties. J. Appl. Polym. Sci. 2009;113(4):2649–60.
[7]   Bazaz SM, Lohtander M, Varis J. 5-Dimensional Definition for a Manufacturing Digital Twin. Procedia Manufacturing 2019;38:1705–12.
[8]   Tao F, Zhang H, Liu A, Nee AYC. Digital Twin in Industry: State-of-the-Art. IEEE Trans. Ind. Inf. 2019;15(4):2405–15.

Description of the initial situation

As the transformation of the energy system, triggered by renewable, decentralized energy generation, raises numerous challenges, means of coping with these are needed. The grid becomes more difficult to operate and monitor in many cases leading to voltage band violations. Therefore, distribution system operators (DSO) need to surveil the correct operation of grid connected devices, such as inverters on the low voltage level, in order to ensure the network to be reliable and to work within the specified limits. Therefore, the project DeMaDs within the framework of the PhD thesis “Data driven detection of malfunctioning devices in power distribution systems” aims to find a solution to this issue. Grid data is in many cases not updated regularly or often faulty. Therefore, an approach using operational data is of advantage. Even if smart meter data is becoming available in theory, monitoring is required to be performed remotely using as little data as possible. Therefore, surveillance on the distribution transformer level, where also high-resolution data is available, is preferable. This is also the case due to legal and operational constraints. On the one hand grid operators need the permission of customers to utilize smart meter data sampled every 15 minutes, which cannot be taken as a given. On the other hand, cost intensive bandwidth upgrades of communications infrastructures, which processing big amounts of local data centrally would require, ought to be avoided. Both limit the usability of local smart meter data for the purposes of necessary monitoring and make a central solution the only viable one.

Intention and goals

As goals, means of monitoring should be developed to detect and distinguish faults as short circuits, transformer malfunctions, voltage dips and surges or wrongly parameterized photovoltaic (PV) inverters. The innovation, which is a new central data driven approach, is recommendable as it keeps the required knowledge about network components characteristics to a minimum. The current state of the art is surpassed by the development of a complete framework for monitoring of grid connected devices that includes data mining, processing and validation of the results.

Schematic representation of how the error detection works

The main insights comprise of which measurements, such as voltage and current magnitudes or active and reactive power flows, are best suited for this task, as well as how high a resolution of these measurements is required. The framework will be able to detect malfunctioning devices and classify them in specific categories as well as disaggregate load profiles for data mining. These functionalities are the most important outcomes.

Publications in this context

[1]    E. Brown, J. M. P. Cloke, and J. P. Harrison, “Governance, decentralization and energy: a critical review of the key issues,” 2015.
[2]    J. von Appen, M. Braun, T. Stetz, K. Diwold, and D. Geibel, “Time in the sun: The challenge of high pv penetration in the german electric grid,” IEEE Power and Energy Magazine, vol. 11, no. 2, pp. 55–64, March 2013.
[3]    N. Mahmud and A. Zahedi, “Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation,” Renewable and Sustainable Energy Reviews, vol. 64, pp. 582 – 595, 2016.
[4]    P. Vergara Barrios, T. Mai, A. Burstein, and P. Nguyen, “Feasibility and performance assessment of commercial pv inverters operating with droop control for providing voltage supports services,” in 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT) Europe.
[5]    E-Control, “Technische und organisatorische Regeln für Betreiber und Benutzer von Netzen,” E-Control, Tech. Rep., 2019. [Online]. Available: https://www.e-control.at/en/bereich-recht/tor, opens an external URL in a new window

Description of the initial situation

Industry accounts for about 30% of Austria’s final energy consumption and is thus a major energy consumer. The path towards deep decarbonization of the industrial sector is still a problem with many unsolved questions. The use of green hydrogen and related derivates as energy carrier (e.g. gas, fuel) in industrial processes constitutes one of the most promising solutions. For these, development of optimized and coupled technical PV-H2 solutions, the investigation of conditions and parameters impacting reliable operation (related to electric power production and failure) and the prediction models of energy production are of fundamental necessity for the secureness of investment and planned decarbonization.

Intention and goals

The main activities of the thesis will be around performance modeling of coupled H2 and PV systems and their optimization, depending on the industrial end-user energy consumption goals. The following main points will be addressed in the thesis:

  • Combined performance models for coupled H2 production via electrolysis and photovoltaic electricity production for use in industrial energy systems
  • Developing a combined model describing coupled PV-H2 systems in their production profile, performance and partial load behavior
  • Identifying and modelling PV and H2 plants design options for optimized consumption in different industrial sectors with different load profiles (e.g. paper, bakery, meat, metal, transport…)

Implementation of a metrics for analysis of PV and H2 plant operation risks will allow to evaluate the impact of coupled H2-PV systems on power and energy production reliability (e.g. by event tree analysis).

Methodological approach of the thesis: Development of system models, mathematical optimization and validation with real data

Methodological approach

The optimization strategy is based on performance models of the PV power plants, which are further developed based on existing models, and H2 electrolysis modelling. The central research object is to implement combined performance models for joint system design as well as a metric for identifying and measuring operational risks (e.g. component failure). For the evaluation of system risks a double approach will be to combine statistical data evaluation form existing plants of different age as well developing decent risk evaluation models as event tree analysis and error trees for coupled H2-PV systems. Finally, the correlation between optimized reliable operation (i.e. risk-minimized) and performance will be analyzed for industrial H2-PV systems.

References

  • Belluardo G., Ingenhoven P., Sparber W., Wagner J., Weihs P., Moser D., Novel method for the improvement in the evaluation of outdoor performance loss rate in different PV technologies and comparison with two other methods, Solar Energy 117, p139-152 (2015)

  • Chaves A., Bahill A.T., Risk Analysis for Incorporating Photovoltaic Solar Panels into a Commercial Electric Power Grid, Systems and Industrial Engineering, University of Arizona (2010)

  • Clarke R.E., Giddey S., Ciacchi F.T., Badwal S.P.S., Paul B., Andrews J., Direct coupling of an elextrolyser to a solar PV system for generating hydrogen, Hydrogen Energy 34, p2531-2542 (2009)

  • Halwachs M., Neumaier L., Vollert N., Maul L., Dimitriadis S., Voronko Y., Eder G.C., Omazic A., Mühleisen W., Hirschl C., Schwark M., Berger K.A., Ebner R., Statistical evaluation of PV system performance and failure data among different climate zones, Renewable Energy 139, p1040-1060 (2019)

 

Fire and smoke raising from a electric arc furnace for metal melting

Description of the initial situation

The amount of steel produced worldwide exceeds that of any other metal [1]. With 569 TWh in Europe (2018), the iron and steel industry is one of the largest energy consumers and responsible for approximately 4-7% of anthropogenic CO2 emissions. In addition to the traditional manufacturing route via blast furnace and converter, the second most common route via recycled steel scrap and electric arc furnaces is becoming increasingly important (accounting for approximately 41.4% of all steel produced in Europe in 2019) [2] [3]. The production of steel in electric arc furnaces has several advantages: on the one hand, scrap steel is used as a raw material, which enables a more resource-efficient steel production. On the other hand, there is the potential to integrate part of the required electrical energy (mainly for the electric arc furnace as the most energy-intensive unit) from renewable energy sources [4].

In order to implement measures to increase energy efficiency and flexibility, integrate renewable energy sources and reduce greenhouse gas emissions, it is necessary to consider, model and optimise the steel plant as an overall system. One possibility for implementing these measures is the use of demand side management (DSM). In industry, DSM covers all consumer-side measures that influence type and level of energy demand. The concept of DSM is not new and certain key technologies have already been developed. Why DSM is now becoming the focus of scientific interest is related, among other things, to the accelerated expansion and increased integration of volatile, renewable energies. But also the advancing digitalization of the industry and the ever faster changing market conditions enable and demand solutions at the same time.

The realisations of DSM are diverse and include the improvement of energy efficiency as a permanent application up to time-dependent ones (e.g. time of use, market demand response, spinning reserve) with different operation horizons [5] [6].

Intention and goals

The aim of the dissertation is to make the energy consumption of an electric steel plant more flexible with the help of operational optimization through demand side management – and that online. The following work steps are necessary for the realisation:

  1. Process analysis, data extraction, processing and analysis
    As a basis for the subsequent modelling, a profound understanding of the processes in the steel plant is necessary. This includes domain knowledge about the process parameters, the individual aggregates and the logistical relationships between them. In addition, process data is prepared, analyzed and used as a basis for (data-driven) modelling.
  2. Process modelling
    The process modelling of the most important aggregates of the steel plant provides a forecast of the energy demand for a certain time horizon and forms the basis of the subsequent operation optimization. The modelling methods include stochastic and data-driven approaches.
  3. Operational optimization
    In the area of optimization, the focus is on the development of a DSM application that takes into account short-term changes in market conditions (market demand response) and ongoing process events in real time.
  4. Implementation
    After the development of the DSM tools, they are tested at a trial site in the iron and steel industry and their effectiveness is evaluated in real operations. In addition, an assessment will be made as to which other industrial sectors these DSM applications can be applied to.

Publications in this context

[1]       AZADEH, A. ; NESHAT, N. ; MARDAN, E. ; SABERI, M.: Optimization of steel demand forecasting with complex and uncertain economic inputs by an integrated neural network–fuzzy mathematical programming approach. In: The International Journal of Advanced Manufacturing Technology 65 (2013), 5-8, S. 833–841
[2]       DOCK, Johannes ; JANZ, Daniel ; WEISS, Jakob ; MARSCHNIG, Aaron ; KIENBERGER, Thomas: Time- and component-resolved energy system model of an electric steel mill. In: Cleaner Engineering and Technology 4 (2021), October, S. 100223. URL https://pure.unileoben.ac.at/portal/de/publications/time-and-componentresolved-energy-system-model-of-an-electric-steel-mill(45eda8ec-64be-4cf5-a953-ca2e3ed264f2)/export.html, opens an external URL in a new window
[3]       CARLSSON, Leo S. ; SAMUELSSON, Peter B. ; JÖNSSON, Pär G.: Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling. In: metals (2019), Nr. 9
[4]       M. KOVAČIČ ; KLEMEN STOPAR ; R. VERTNIK ; B. ŠARLER: Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study. In: energies (2019). URL https://www.semanticscholar.org/paper/Comprehensive-Electric-Arc-Furnace-Electric-Energy-Kova%C4%8Di%C4%8D-Stopar/7c4e523c0a40fa2378ad184a82a7449f5f8984c2, opens an external URL in a new window
[5]       STRBAC, Goran: Demand side management: Benefits and challenges. In: Energy Policy 36 (2008), Nr. 12, S. 4419–4426. URL https://www.sciencedirect.com/science/article/pii/S0301421508004606, opens an external URL in a new window
[6]       PALENSKY, Peter ; DIETRICH, Dietmar: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. In: IEEE Transactions on Industrial Informatics 7 (2011), Nr. 3, S. 381–388

exemplary representation of a node/edge model on a cellular basis

In view of the upcoming energy transition, a drastic increase in energy generation from renewable but intermittent sources is intended. Therefore, balancing energy provision and energy demand temporally and spatially will become more difficult, thus providing a challenge for future electricity‑, gas- and heat grids.

Tools for modelling multi energy systems play an important role for assessing the effect of these changes on the current grid infrastructure. This is why the software tool “HyFlow” was created at the chair of energy network technology in Leoben. The term multi energy systems thereby describes energy systems with more than one type of energy carriers, such as electricity or natural gas.

The aim of this PhD project is to further develop HyFlow. In the future, HyFlow should not only be used to calculate power flows in different energy grids, but also to determine optimal operation strategies for power plants, consumers and storage units. Thus, it would be possible to evaluate security of supply in multi energy systems and to specify new strategies for expanding and maintaining such systems.