Can Artificial Intelligence be Used to Improve U.S. Coal Plant Profitability?
By Clyde Craig
September 18, 2019 - Can artificial intelligence be employed to boost coal power usage? One chemical engineer at West Virginia University is tapping into artificial intelligence to improve the profitability and flexibility of coal-fired power plants.
Debangsu Bhattacharyya has received a $2.5-million grant from the U.S. Department of Energy to develop an online tool that uses AI to monitor boiler systems at coal-fired and natural gas power plants.
Debangsu Bhattacharyya, GE Plastics Material Engineering Professor of chemical and biomedical engineering at WVU.
Photo Paige Nesbit, WVU
Bhattacharyya is a GE Plastics Material Engineering Professor of chemical and biomedical engineering at the university in Morgantown, West Virginia.
Due to frequent and rapid loading, power plants are subjected to excessive creep and fatigue damages, Bhattacharyya explains. This often leads to the failure of critical boiler components, which causes power plants to operate inefficiently.
Inside most power plants, coal or natural gas is combusted to produce high-pressure steam that is then forced into a turbine to generate electricity.
The 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 says.
“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, he says.
Depending on the length of the idle time before the startup is initiated, startups can be categorized as hot, warm, or cold. Cold startups can cause significantly more damage when compared to hot or warm startups. During the 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, and this is where AI can play a 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, Bhattacharyya said, and can eventually help plants develop advanced process control strategies for improved flexibility, higher profitability and reduced forced outage without compromising safety or reliability.
“As the system learns, it eventually keeps improving the estimation accuracy,” he said.
The project is part of a larger initiative from the Department of Energy’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.”