VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Kaasuturbiinilaitoksen vertailu ja optimointi käyttäen koneoppimista teollisen internetin ympäristössä

For past five to ten years, the industry has been investing more and more in Industrial Internet. Industrial Internet is changing the whole industrial segment and it creates new opportunities for companies to grow their business. Industrial Internet allows users to combine multiple plants into one big ecosystem where the plants can exploit the information provided by the other plants. This thesis combines gas turbine domain, machine learning and Industrial Internet together. Aim of this thesis was to develop a machine learning model and deploy it to Industrial Internet environment. The thesis is a proof of concept and it works as a base for developing the future applications. The machine learning model predicts temperature corrected power output of a gas turbine. With the model, it is possible to point out a performance decrease in the turbine. The model was developed using stepwise regression method. The model was trained to work only on a base load. The whole process from integrating data to the visualizations for the end user was implemented in this thesis. The work was implemented in Valmet Industrial Internet platform. In the thesis, there were data from two plants both having two gas turbines. All the turbines are the same model so benchmarking the turbines between each other is reasonable. The created model calculates predictions of temperature corrected power output of the turbine and returns the predictions to the database. The data is visualized. As a result, a user can examine the performance of the turbines. The user interface provides a general view where the user can look overall performance figures of the day. User interface also provides more detailed data view where the user can look the data from a chosen hour

Bioenergy and Minigrids for Sustainable Human Development

Human-caused climate change and deep disparities in human development imperil a prosperous and just future for our planet and the people who live on it. Transforming our society to mitigate global warming offers an opportunity to rebuild energy systems to the benefit of those who are harmed by global inequality today. I examine this opportunity through the lens of two sustainable energy technologies: bioenergy and miniature electricity grids (minigrids). Bioenergy requires land to produce biomass and is inextricably connected to the surrounding environment, agricultural livelihoods, and food system. I apply data science tools to study aspects of land use and food security that may intersect with increasing bioenergy production. I assess the potential to use over one billion hectares of grazing land more intensively with an empirical yield gap analysis technique called climate binning. To clarify how agricultural and socioeconomic characteristics relate to national food security, I study the relative importance of several drivers using simple linear regressions with cross validation and random sampling techniques. Minigrids can supply clean, reliable electricity to un- and under-served communities, but small and hard-to-predict customer loads hamper their financial viability. To improve predictions of daily electricity demand of prospective customers, I test a data-driven approach using customer demographic surveys and machine learning models. I also investigate opportunities to grow loads by stimulating income-generating uses of minigrid electricity in twelve Nigerian agricultural value chains. I conclude by emphasizing the fundamental complementarity of energy and agriculture as change levers for human development, especially in rural communities with low energy access and high poverty. I also provide recommendations to support the effective use of energy to solve pressing agricultural problems and drive multiplicative human development benefits

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi