Introducing AI to coal power plants

One West Virginia University chemical engineer is tapping into artificial intelligence to prolong the ‎lives of power plant boilers.‎

Debangsu Bhattacharyya, GE Plastics Material Engineering professor of chemical and biomedical ‎engineering, received a $2.5 million U.S. Department of Energy grant to develop an online ‎monitoring tool, using AI, for boiler systems at coal-fired and natural gas power plants.‎

Due to frequent and rapid loading, power plants are subjected to excessive creep and fatigue ‎damages, which often lead to the failure of critical boiler components, Bhattacharyya said. This ‎causes power plants to operate inefficiently.‎

Here’s how power plants work: Coal or natural gas is combusted inside to produce high-pressure ‎steam that is then used in a steam turbine to generate electricity. A boiler incorporates a furnace to ‎burn fuel and generate heat, which is transferred to water to make steam.‎

‎“The boiler is at the heart of the power plant,” Bhattacharyya said. “During startup, the boiler is ‎gradually heated up increasing the steam temperature and pressure to their nominal values.”‎

With power plant boilers, there’s a lot of starting up and shutting down.‎

Depending on the length of the idle time before the startup is initiated, startups can be categorized ‎as hot, warm or cold startups. Cold startups can cause significantly more damage to the boiler ‎health in comparison to hot or warm startups. During shutdown, the boiler is gradually cooled and ‎the steam pressure is decreased.‎

Many power plant boilers start up and shut down several hundreds of times a year.‎

This is where AI can play a in role, in predicting the behaviors of the boilers by “learning” the inner ‎workings of the system, Bhattacharyya said.‎

‎“AI models will be used to describe the complex phenomena in the boilers that are time-varying,” ‎he said. “For example, external fouling of boiler tubes by fly ash and slag is an extremely complex ‎phenomenon being affected by various operating conditions such as the gas flow field, coal and ‎ash particle shape and size distribution and hardware design.”‎

A tool to monitor the online health of the boiler can be developed to understand the impacts of ‎load-following and can eventually help plants develop advanced process control strategies for ‎improved flexibility, higher profitability and reduced forced outage without compromising safety or ‎reliability, Bhattacharyya said.‎

‎“As the system learns, it eventually keeps improving the estimation accuracy,” he said.‎

The project is part of a larger initiative from the DOE’s Office of Fossil Energy that allocated $39 ‎million toward a total of 17 research projects aimed at improving the reliability, performance and ‎flexibility of the nation’s existing coal-fired power fleet.‎

Bhattacharyya’s model will be tested at Barry Power Plant, a coal- and natural gas-fired electrical ‎generation facility in Alabama.‎

‎“Even though each boiler is different, the framework proposed can be readily adapted to the ‎monitoring of practically any power plant,” he said. “A key goal of the project is to develop the ‎framework so that it is easy to understand and implement for broader acceptability by and ‎applicability to a large number of power plants.”‎

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