Master thesis - Power plant operation optimization using Machine
Siemens / Maskiningenjörsjobb / Finspång
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Organization: Siemens Industrial Turbomachinery AB,
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Master thesis - Power plant operation optimization using Machine Learning
Project Field Experience SIT AB, a large amount of field experience data is continuously generated in form of various reports from maintenance events, component repair and operation history. These reports include detailed information about the turbine operation history as well as its condition and reported damages on individual components. This field experience data, although noisy, invariably portray environmental factors, measurement errors, and loading conditions, or in short, reality. By establishment of a process to collect and maintain this information in a database format, exploration and knowledge discovery using this data became a subject of high interest. Task of operation optimization to maximize the profit has been a challenge for complex productions such as electricity. There are many different aspects that should be considered during the future operation planning. The questions to be answered are how many hours each engine should be operated, what should be the loading level for each engine, how to synchronize the maintenance activities for different equipment to have only one outage, what are the impacts of for example fuel price and the operation environment.
Project description
This Master thesis is a part of efforts done to develop advanced machine learning algorithms to support the operators in their daily life with the decisions they need to take. In this aspect and to achieve this goal, a wide range of sensor data also is available to be used within the development phase of the algorithms.
The goal of the project is to apply machine learning to the sensor data together with the domain knowledge to automate the decision making process for the operators at power plants. The examples of the tasks are "when to wash the gas turbine compressor section", "when to replace the oil", "when to exchange the air inlet filters". The current procedure is based on some simple criteria which are not giving the optimum time for each of these activities. For example, the washing will be performed periodically or every time the performance reduction hits a threshold. Since today we have access to lots of sensors data, the idea is to use machine learning to automate the decision making process and give the feedback regarding the best time for each of them. The project is suitable for one student with good data science skills and an interest in data analytics. Students will work closely with domain experts.
Varaktighet, arbetstid
Heltid Fast anställning tills vidare
Publiceringsdatum2017-11-21ErsättningFast lön
Så ansöker duSista dag att ansöka är 2018-01-16
Klicka på denna länk för att göra din ansökanFöretagSiemens
AdressSiemens
Slottsvägen 2
61231 Finspång
KontorsadressSlottsvägen 2, Finspång
Jobbnummer 3813513
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