Research & Development

The AVESTAR™ Center offers unique collaborative R&D opportunities with leading experts in the operation and control of clean energy systems.  Key areas of research include high-fidelity dynamic process modeling, advanced process control, and 3D virtual plant simulation.  Objectives and goals are aimed at safe and effective operation of power generation systems for optimal efficiency, while protecting the environment. 

Key benefits for collaborating partners include:

• Collaboration with energy experts and research leaders in dynamics and control
• Access to real-time, high-fidelity dynamic simulators and immersive training systems
• Leveraging of world-class training and research facilities

R&D collaboration can be conducted through cooperative research and development agreements (CRADAs) to form partnerships with the private sector, academia, and other government entities.  

Dynamics

The AVESTAR™ team is pursuing research on the dynamic modeling and simulation of clean energy systems.  Dynamic simulation tools provide a continuous view of a plant in action by calculating the transient behavior of the plant over time.  Typical real-time applications include plant startup, upset, shutdown, and load following.  Dynamic simulation can also be used to determine key equipment response times and to investigate interactions between major plant sections, including power generation and CO₂ capture and compression.  By using on-line, dynamic models either as a guide to operators for process monitoring, or directly for rigorous nonlinear model-predictive control, it is possible to generate significant increases in operating profit.  In addition, dynamic simulations can be used in an off-line mode to evaluate alternative control strategies without the expense and the unexpected hazards of plant experimentation.  This capability is essential for dealing with complex energy plants or with new carbon capture technologies for which little or no operating experience is available.   At the AVESTAR™ Center, researchers are applying dynamic simulations to a range of clean energy systems including IGCC systems with pre-combustion, carbon capture, supercritical pulverized coal plants with post-combustion capture, oxy-combustion systems, and fuel cell/gas turbine hybrids.

The AVESTAR™ research team is developing high-fidelity, distributed-parameter equipment models, including those based on partial differential equations (PDEs).  PDE-based dynamic models are typically too computationally expensive for use in plant-wide dynamic applications, especially if real-time simulation is to be achieved.  The use of process control with these high-fidelity PDE-based dynamic models is also beyond current technology.  As a result, AVESTAR™ researchers are developing and applying innovative methods for reducing the complexity of dynamic PDE-based models, while preserving input-output behavior.  The dynamic reduced-order models (D-ROMs) approximate the high-order, high-fidelity, PDE-based models, thereby offering a tradeoff between accuracy, range of applicability, and computational cost.  Typically the goal is to achieve a D-ROM as accurate as possible at a reasonable cost.   Properly implemented model reductions and nonlinear transformations allow the generation of D-ROMs without compromising validity or scope.  The PDE model can be viewed as an input/output system, where inlet boundary/stream conditions and operating conditions/parameters represent the inputs and outlet streams and calculated parameters represent the outputs.  A D-ROM can be developed which reproduces the output behavior of the PDE model over a limited range of input conditions.  The range of validity of the D-ROM is determined by the specifics of the model order reduction technique.  Work is underway at the AVESTAR™ Center on the development of D-ROMs for gasifiers and CO₂ capture devices.

The AVESTAR™ Center is interested in forming collaborations for continued R&D on the dynamic modeling and simulation of clean energy plants.

Control

The AVESTAR™ team is conducting research on advanced regulatory control (ARC) and advanced process control (APC) strategies for clean energy systems.  ARC and APC systems are applied to adapt, predict and adjust to dynamic changes in complex, multivariable processes.  Standard regulatory control using PID (proportional, integral, derivative) loops is typically sufficient for relatively simple processes.  However, PID control requires repeated manual tuning and has trouble, however, dealing with more complicated processes which may be nonlinear (fluctuating); involve long time delays; are subject to frequent dynamic changes due to plant upsets or load changes; involve complex relationships between process variables; or require control of multivariables which cannot be handled by single-loop PID controllers.

Advanced process control (APC) generates and controls supervisory set points to optimize plant performance.  AVESTAR™ researchers are developing APC strategies based on model predictive control (MPC) for application to complex energy processes.  At the core of MPC technology is a mathematical model of the process that is used to predict future process behavior.  Using this predictive model the controller is able to calculate an optimum set of process control moves that minimize the error between actual and desired process behavior subject to process constraints, thereby reducing process variability and driving the process closer to its optimum performance.

In recent years, MPC solutions have been applied to advanced energy plants including combined cycle systems, IGCC plants, and hybrid fuel cell/ gas turbine systems.  Considerable research challenges and opportunities exist in the development and application of advanced MPC strategies for advanced energy systems with carbon capture.  For example, MPC strategies are required for driving power production to satisfy load demands while meeting energy plant integration, performance, and environmental objectives, including CO₂ capture.

The AVESTAR™ Center is interested in collaborative R&D opportunities on advanced process control for advanced energy plants with carbon capture.

3D Virtual

The AVESTAR™ Center is advancing and applying 3D virtual reality (VR) technology that can be coupled with real-time dynamic simulation to add another dimension of realism for clean energy systems.  A full immersive training system (ITS) solution combines the real-time dynamic simulator with a photorealistic 3D virtual model, virtual reality engines, 3D interactive content, and VR devices.  The ITS extends the training scope to both control room and field operators, enabling them to work together on plant operations, off-line evaluations of procedures, training for safety-critical tasks, and emergency response training.

Immersive 3D virtual reality software is now available for the process and energy industries.  The AVESTAR™ team is using commercial VR software technology, EYESIM™ from Invensys Operations Management, for its IGCC immersive training system.  The team is also conducting R&D in this area using the open-source virtual engineering software toolkit, VE-Suite from Ames Laboratory.  VE-Suite integrates dynamic process simulation data into an immersive and interactive 3D virtual plant environment containing computer-aided design (CAD) data and many other engineering data sources.  For real-time dynamics, the Virtual Engineering Process Simulator Interface (VE-PSI) in VE-Suite has been extended to handle dynamic process data and provide run-time control for dynamic process simulators.

The AVESTAR™ Center is interested in forming collaborations for continued R&D on 3D virtual reality technology for application to the real-time dynamic simulation of clean energy systems.