Improving the Efficiency and Reliability of Coal-burning Power Plants

Researchers at NETL have tapped big data to expedite the development of stronger boilers, turbine components and other metallic equipment to improve the efficiency and reliability of coal-burning power plants.

Results of a recent study by a team of NETL researchers and their collaborators at Case Western Reserve University show that an approach called materials data analytics can be used to significantly shorten the development time of specialized alloys needed for next generation energy applications.

Materials data analytics (MDA) organizes raw data to identify patterns and draw conclusions, as well as establish statistical models and develop software tools that can predict materials with superior mechanical properties. Creation of large datasets in materials science has transformed the way research is done by providing opportunities to identify complex relationships and to extract information that will enable new discoveries.

Using MDA, researchers can better understand how various components degrade under extreme levels of heat and pressure. NETL researchers developed a novel MDA method to evaluate a family of steels commonly used in energy applications, with the goal of accelerating the design of stronger alloys for power plant equipment. The results of this work were recently published in Statistical Analysis and Data Mining: The ASA Data Science Journal.

“Through data analysis, we can build computational models to design alloys to perform at higher operating temperatures and stress, which will be needed to achieve efficiencies in the next generation of coal power plants and during cycling (the process of turning off and on a plant in response to system load or demand),” said Vyacheslav Romanov, a researcher with NETL’s Geo-Analysis and Monitoring Team who leads data science work at the Lab.

“Cycling creates large thermal and pressure stresses, which can damage power plant components and lead to plant shutdowns. Our work can help design the right materials to withstand these stresses,” Romanov added.

The overarching goal of the NETL research is to accelerate the design process to produce better alloys, and reduce the time and expense associated with qualification testing of new alloys for fossil energy applications.

“Our study showed materials data analytics has the potential to shorten the time-consuming process of designing metallic materials for use in elevated-temperature environments such as power plants and even jet engines,” Romanov said.

According to Romanov, NETL engineers in Albany, Oregon, will receive production parameters to process experimental alloys in 2020.

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