AVESTAR control system efforts are focused on development of computational approaches for simulation and advanced controls for energy systems. Power generation technologies are growing more sophisticated and require control strategies and systems to be updated to allow plant owners to take full advantage of their increased capabilities. A well designed control system can provide the ability to hit and maintain setpoints without oscillation for optimum power plant operation. Implementation of complex control systems developed through advanced computational approaches will increase efficiency and reduce emissions.
The AVESTAR team is focusing on the following three areas of process control research: 1) Plant-wide control system design, 2) Advanced regulatory control, and 3) Advanced process control. Process control models, methods, and tools are developed and applied to a wide variety of energy systems ranging from smart plant to smart grid.
Plant-wide Control System Design
At the AVESTAR Center, researchers are developing a systematic approach to plant-wide control system design. The control system design is developed with the objective of optimizing a desired scalar function while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. Various objective functions can be considered for the control system design such as maximization of profit, maximization of the power produced, or minimization of the auxiliary power consumed in the plant.
The AVESTAR team is applying the plant-wide control system design approach to a commercial-scale integrated gasification combined cycle (IGCC) power plant with CO2 capture. This is particularly important since future IGCC plants with CO2 capture must be operated optimally in the face of disturbances without violating operational and environmental constraints. The design of such a control system can make the IGCC plant suitable to play an active role in the smart grid era by enabling operation in the load-following mode as demand for electricity from the grid fluctuates over time. In addition, other penalty functions such as emission penalties for CO2 or other criteria pollutants can be considered in the control system design.
- Jones, D., D. Bhattacharyya, R. Turton, and S.E. Zitney, “Optimal Secondary Controlled Variable Selection: Methodology and its Application in an Acid Gas Removal Unit,” AIChE 2013 Annual Meeting, San Francisco, CA, November 3-8 (2013).
- Jones, D., D. Bhattacharyya, R. Turton, and S.E. Zitney, “Optimal Selection of Primary Controlled Variables for an Acid Gas Removal Unit as part of an IGCC Plant with CO2 Capture,” Proc. of the 2013 American Control Conference, Washington D.C., June 17-19 (2013).
- Jones, D., D. Bhattacharyya, R. Turton, and S.E. Zitney, “Optimal Control System Design for IGCC Power Plants with CO2 Capture,” Proc. of the 29th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, October 15-18 (2012).
Advanced Regulatory Control
Advanced regulatory control (ARC) is applied to adapt, predict and adjust to dynamic changes in complex, multivariable energy systems. Standard regulatory control using PID (proportional, integral, derivative) loops is typically sufficient for relatively simple systems. However, PID control requires repeated manual tuning and has trouble, however, dealing with more complicated systems which may be nonlinear (fluctuating); involve long time delays; are subject to frequent dynamic changes due to system upsets or load changes; involve complex relationships between system variables; or require control of multiple variables which cannot be handled by single-loop PID controllers.
AVESTAR researchers are developing ARC strategies to extend control system capabilities beyond regulatory and sequential control to move energy systems closer to their optimal performance. Typical ARC strategies include cascade control, override control, and combined feedforward-feedback control. Such strategies are implemented to improve operating efficiency and profitability, increase power production, enhance system stability and operability, and better reject routine control loop disturbances.
- Mahapatra, P. and S.E. Zitney, “Advanced Regulatory Control and Coordinated Plant-Wide Control Strategies for IGCC Targeted towards Improving Power Ramp-Rates,” AIChE 2012 Annual Meeting, Pittsburgh, PA, October 28 – November 2 (2012).
- Mahapatra, P. and S.E. Zitney, “Enhanced IGCC Regulatory Control and Coordinate Plant-wide Control Strategies for Improving Power Ramp Rates,” Proc. of the 29th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, October 15-18 (2012).
- Bhattacharyya, D., R. Turton, and S.E. Zitney, “Control System Design for Maintaining CO2 Capture in IGCC Power Plants While Load-Following,” Proc. of the 29th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, October 15-18 (2012).
- Bhattacharyya, D., R. Turton, and S.E. Zitney, “Load-Following Control of an IGCC Plant with CO2 Capture,” Proc. of the 28th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, September 12-15 (2011).
Advanced Process Control
Advanced process control (APC) methods generate and control supervisory set points to optimize overall system 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. 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.
Future power plants with CO2 capture may have to adjust their power output as demand for electricity from the grid fluctuates over time. Such load-following requirements will become far more challenging as power produced by renewable energy is connected to the grid and where seasonal and diurnal change in the load is expected. In view of this, AVESTAR researchers are leveraging dynamic simulators to develop novel model predictive control strategies to improve ramp rates and load-following operation of power plants while satisfying CO2 emission constraints.
- Bhattacharyya, D., R. Turton, and S.E. Zitney, “Model Predictive Control for Load-Following of an Integrated Gasification Combined Cycle (IGCC) Plant with CO2 Capture,” AIChE 2011 Annual Meeting, Minneapolis, MN, October 16-21 (2011).
For IGCC power plants, transient studies show that the air separation unit poses a bottleneck in the entire plant operation by limiting the ramp-rate during load-following operation. As a result, AVESTAR researchers have developed a multiple MPC (MMPC) strategy to provide better composition control at high ramp-rates compared to the MPC approach. This in-turn allows for higher ramp-rates of ASU, without violating the oxygen-purity constraints. The proposed MMPC control strategy provides a better response to the plant-wide IGCC load-following problem compared to conventional PID and MPC approaches, circumventing the need for larger liquid oxygen/air storage requirements.
- Mahapatra, P., S.E. Zitney, and W. Bequette, “Dynamic Maximization of Oxygen Yield in an Elevated-Pressure Air Separation Unit using Multiple Model Predictive Control,” Proc. of 10th IFAC International Symposium on Dynamics and Control of Process Systems (DYCOPS 2013), Mumbai, India, December 18-20 (2013).
- Mahapatra, P., W. Bequette, and S.E. Zitney, “Multiple Model Predictive Control of Air Separation Unit As Part of IGCC Power Plant During Rapid Load Changes," AIChE 2011 Annual Meeting, Minneapolis, MN, October 16-21 (2011).
- Mahapatra, P., W. Bequette, and S.E. Zitney, “Application of Model Predictive Control for Rapid Combined-Cycle Load Following Using Custom Dynamic Models In Process-Simulation Software Environment,” AIChE 2011 Annual Meeting, Minneapolis, MN, October 16-21 (2011).
Looking to the future, power generation plants are composed of multiple processes, characterized by complex dynamics and mutual influences such that local control decisions may have long-range effects throughout the system. Improper control and insufficient coordination of these large-scale systems could result in a hugely suboptimal performance or in serious malfunctions or abnormal situations. Current centralized control design methods cannot deal with large-scale systems due to the tremendous computational complexity of the centralized control task and due to scalability issues and communication bandwidth limitations. Therefore, it is necessary to develop new and efficient methods and algorithms for distributed and hierarchical model predictive control of large-scale networked systems with embedded sensors and controllers.