(188p) A Biologically-Inspired Optimal Control Framework: Application to the Hybrid Performance (Hyper) System

Optimal control strategies that are inspired by biological systems have captured the attention of researchers in recent years. These approaches have demonstrated unique features for handling process complexities and nonlinearities when compared to traditional control methods^1-3. For example, a Biologically-Inspired Optimal Control Strategy (BIO-CS) has been recently introduced and implemented for various energy systems applications such as a fermentation process for ethanol production and an Integrated Gasification Combined Cycle (IGCC) system for power generation^1-4. The proposed BIO-CS mimics the ant’s rule of pursuit idea in combination with gradient-based optimal control and multi-agent concepts^1-3. In the present work, BIO-CS is further developed to address computational time challenges and coupling effects among different components of a cyber-physical fuel cell-gas turbine hybrid power system, as part of the HYbrid PERformance (Hyper) project at the National Energy Technology Laboratory (NETL).

Specifically, the BIO-CS algorithm is modified by adding a trigger to enable algorithm termination at a suboptimal solution associated with a specific agent, given the fast time scale of the hybrid system. In addition, BIO-CS is redesigned to accommodate a state estimator in the controller framework. This strategy is implemented for set-point tracking and disturbance rejection scenarios considering the Multi-Input Multi-Output (MIMO) control structure defined using transfer function models derived from the Hyper process. BIO-CS is designed to control the cathode airflow and the turbine speed simultaneously at their desired operating points. These variables represent the most critical outputs of the system. The application of advanced control methods to address coupling effects between these outputs without compromising the system performance is critical for the integration of a fuel cell and gas turbine system in a hybrid cycle⁵.

The simulation results demonstrate the successful application of the BIO-CS to the hybrid process considering both the setpoint tracking and disturbance rejection scenarios. For the setpoint tracking case, the effect on turbine speed due to coupling is limited to 0.2% deviation from the nominal operation. For the disturbance rejection case, different state observers and estimators (pole placement, Kalman filter) are investigated to provide state information that is not easily available to the BIO-CS during the dynamic operation of the system. Different sample times for communication between the controller and estimator are also considered as both approaches are coupled in the same control loop. The controller-estimator results show offset reduction and mitigation of the coupling in the system. The closed-loop simulation results that will be discussed also highlight the promising capabilities of the BIO-CS algorithm as well as the challenges encountered for future implementations on hybrid energy systems.

References:

1. Lima F. V., Li S., Mirlekar G. V., Sridhar L. N. and Ruiz-Mercado G. J., “Modeling and advanced control for sustainable process systems”. Sustainability in the Analysis, Synthesis and Design of Chemical Engineering Processes, G. Ruiz-Mercado and H. Cabezas (eds.), Elsevier, 2016.
2. Li S., Mirlekar G. V., Ruiz-Mercado G. J. and Lima F. V., “Development of chemical process design and control for sustainability”. Processes, 4(3):23, 2016.
3. Mirlekar G. V., Li S. and Lima F. V., “Design and implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for chemical process control”. Submitted for publication.
4. Mirlekar G. V. and Lima F. V., “Design and implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for advanced energy systems”. In AIChE Annual Meeting, San Francisco, CA, 2016.
5. Mirlekar G. V., Pezzini P., Bryden M., Tucker D. and Lima F. V., “A Biologically-Inspired Optimal Control Strategy (BIO-CS) for hybrid energy systems”. To appear in Proceedings of 2017 American Control Conference.