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At NETL, Artificial Intelligence and Machine Learning Play Key Roles in Fossil Energy Research

Director’s Corner

by Brian Anderson, Ph.D.

For decades, mention of fossil energy research conjured images of researchers hard at work with boilers, turbines, electronic sensors, carbon capture mechanisms, coal stockpiles, drilling rigs and a host of other traditional of devices and machinery used to improve efficiency and reduce environmental impacts. Those images are still accurate impressions of NETL’s work. However, these days, the energy research landscape must also include an increasing amount of computerized research activity known as artificial intelligence (AI) and machine learning (ML).

On our web site over the next few weeks, we will be focusing on a few of those 21st Century approaches to discovering, integrating, and maturing technology solutions that enhance the nation’s energy foundation and protect the environment for future generations.
Artificial intelligence or AI refers to software technologies that make a computer act and think like a human, saving time and increasing efficiency. Researchers are discovering how useful AI can be in energy applications. For example, AI is making a significant impact in the way we analyze critical data for decision-making related to operation of power plants. AI innovations are improving condition-based monitoring of operational conditions; facilitating new levels of cybersecurity to protect energy assets; and spearheading innovative diagnostic inspections using AI-enabled robots for automated nondestructive evaluation and repair of power plant boilers.

Meanwhile, working in conjunction with AI, machine learning, or ML, is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions. For example, ML applications are being explored for use in subsurface energy resource recovery because ML can capture the behavior of complex systems through complex but rapid empirical models, capturing knowledge and quickly providing it to decision-makers with minimum labor, dramatically lowering cost of processing monitoring data. In addition, ML, in combination with sensor and control systems, can improve efficiency in reservoir management and improve risk management, creating safer conditions and improving environmental risks.

I join my colleagues at NETL in applauding the Department of Energy’s renewed emphasis on the potential that AI and ML brings to the research table. DOE Secretary Rick Perry recently announced the establishment of the DOE Artificial Intelligence and Technology Office (AITO), which will serve as the coordinating hub for all DOE work being done in AI.

Readers will be learning more about these and other important energy applications for AI and ML and the specifics of NETL research that augments our ongoing mission for America to find new, safer and more efficient ways to use the nation’s most abundant natural energy resources for a brighter economy, a cleaner environment and an energy secure nation.