One West Virginia University chemical engineer is tapping into artificial intelligence to prolong the \u200elives of power plant boilers.\u200e<\/p>\n
Debangsu Bhattacharyya, GE Plastics Material Engineering professor of chemical and biomedical \u200eengineering, received a $2.5 million U.S. Department of Energy grant to develop an online \u200emonitoring tool, using AI, for boiler systems at coal-fired and natural gas power plants.\u200e<\/p>\n
Due to frequent and rapid loading, power plants are subjected to excessive creep and fatigue \u200edamages, which often lead to the failure of critical boiler components, Bhattacharyya said. This \u200ecauses power plants to operate inefficiently.\u200e<\/p>\n
Here\u2019s how power plants work: Coal or natural gas is combusted inside to produce high-pressure \u200esteam that is then used in a steam turbine to generate electricity. A boiler incorporates a furnace to \u200eburn fuel and generate heat, which is transferred to water to make steam.\u200e<\/p>\n
\u200e\u201cThe boiler is at the heart of the power plant,\u201d Bhattacharyya said. \u201cDuring startup, the boiler is \u200egradually heated up increasing the steam temperature and pressure to their nominal values.\u201d\u200e<\/p>\n
With power plant boilers, there\u2019s a lot of starting up and shutting down.\u200e<\/p>\n
Depending on the length of the idle time before the startup is initiated, startups can be categorized \u200eas hot, warm or cold startups. Cold startups can cause significantly more damage to the boiler \u200ehealth in comparison to hot or warm startups. During shutdown, the boiler is gradually cooled and \u200ethe steam pressure is decreased.\u200e<\/p>\n
Many power plant boilers start up and shut down several hundreds of times a year.\u200e<\/p>\n
This is where AI can play a in role, in predicting the behaviors of the boilers by \u201clearning\u201d the inner \u200eworkings of the system, Bhattacharyya said.\u200e<\/p>\n
\u200e\u201cAI models will be used to describe the complex phenomena in the boilers that are time-varying,\u201d \u200ehe said. \u201cFor example, external fouling of boiler tubes by fly ash and slag is an extremely complex \u200ephenomenon being affected by various operating conditions such as the gas flow field, coal and \u200eash particle shape and size distribution and hardware design.\u201d\u200e<\/p>\n
A tool to monitor the online health of the boiler can be developed to understand the impacts of \u200eload-following and can eventually help plants develop advanced process control strategies for \u200eimproved flexibility, higher profitability and reduced forced outage without compromising safety or \u200ereliability, Bhattacharyya said.\u200e<\/p>\n
\u200e\u201cAs the system learns, it eventually keeps improving the estimation accuracy,\u201d he said.\u200e<\/p>\n
The project is part of a larger initiative from the DOE\u2019s Office of Fossil Energy that allocated $39 \u200emillion toward a total of 17 research projects aimed at improving the reliability, performance and \u200eflexibility of the nation\u2019s existing coal-fired power fleet.\u200e<\/p>\n
Bhattacharyya\u2019s model will be tested at Barry Power Plant, a coal- and natural gas-fired electrical \u200egeneration facility in Alabama.\u200e<\/p>\n
\u200e\u201cEven though each boiler is different, the framework proposed can be readily adapted to the \u200emonitoring of practically any power plant,\u201d he said. \u201cA key goal of the project is to develop the \u200eframework so that it is easy to understand and implement for broader acceptability by and \u200eapplicability to a large number of power plants.\u201d\u200e<\/p>\n","protected":false},"excerpt":{"rendered":"
One West Virginia University chemical engineer is tapping into artificial intelligence to prolong the \u200elives of power plant boilers.\u200e Debangsu Bhattacharyya, GE Plastics Material Engineering professor of chemical and biomedical \u200eengineering, received a $2.5 million U.S. Department of Energy grant to develop an online \u200emonitoring tool, using AI, for boiler systems at coal-fired and natural […]<\/p>\n","protected":false},"author":1,"featured_media":287,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[11],"tags":[],"_links":{"self":[{"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/posts\/286"}],"collection":[{"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/burnhancer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=286"}],"version-history":[{"count":1,"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/posts\/286\/revisions"}],"predecessor-version":[{"id":288,"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/posts\/286\/revisions\/288"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/burnhancer.com\/index.php?rest_route=\/wp\/v2\/media\/287"}],"wp:attachment":[{"href":"http:\/\/burnhancer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=286"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/burnhancer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=286"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/burnhancer.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=286"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}