Performance Improvements in Fossil Fuel Power Plants with Multivariable Model Predictive Controls and Artificial Intelligence

Digital Acceleration

Power Generation operations are constantly challenged with improving efficiency and increasing ramp rates while decreasing emissions. Real-time combustion optimization using multivariable model predictive control technology can help address these challenges by utilizing the full process capabilities consistently. Heat rate improvements of up to 1%, ramp rate increases of up to 5x and up to 30% reduction in NOx emissions have been achieved with real-time combustion optimization. Also, the Affordable Clean Energy Rule requires evaluation of this technology for coal fired power plants. The same techniques may be used for natural gas fired, combined cycle power plants, which are a flexible and growing portion of the power generation fleet.

However, often the velocity of applying the technology as well as the sustenance of these significant benefits has been hampered by the usability of the software and adaptability of the applications. Recent technology enhancements have improved the usability and adaptability of advanced process control including:

  • modern and intuitive user interfaces,
  • customizable models including Artificial Intelligence to support unique process behavior,
  • fast and flexible OPC connection tools,
  • automated model building,
  • integration with first principles dynamic models and
  • improved performance monitoring.

This presentation will discuss the new developments that have improved the usability and adaptability of real-time combustion optimization and how this technology has been applied successfully to many power generation plants.

Learning outcomes - viewers will learn about the following:

  • What is Multivariable Model Predictive Control?
  • How has Multivariable Model Predictive Control been applied to Power Generation Plants?
  • What are the benefits of Multivariable Model Predictive Control with Artificial Intelligence?
  • How is Multivariable Model Predictive Control with Artificial Intelligence used to meet Affordable Clean Energy Rule requirements for Neural Networks / Intelligent Soot Blowing?
  • How to determine the potential benefits of Multivariable Model Predictive Control with Artificial Intelligence for Power Generation Plants?
  • How are Multivariable Model Predictive Control systems sustained?

Who should watch?

  • Vice Presidents of Generation
  • Plant Managers
  • Environmental Managers and Engineers
  • Operations Managers
  • Maintenance Managers
  • Performance Engineers
  • Control Systems Engineers

Duration: One Hour

Register now to watch the on-demand webinar!


Bill Poe

Technical Advisor, AVEVA

Bill Poe is a Technical Advisor at AVEVA with over 35 years of experience in operations, implementation and management of advanced process control, optimization and real-time performance monitoring projects in a variety of industries. Prior to AVEVA, Bill held positions at Shell Oil Company, GE and Schneider Electric. He has several patents, achieved a President’s Award at Shell Oil, received the GE Innovators Award and is widely published. Bill earned a Bachelor’s Degree from Georgia Institute of Technology and a Master of Science from Columbia University both in Chemical Engineering.

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