Gas Turbine Power Plant Benchmarking and Optimization with Machine Learning in Industrial Internet Environment
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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.