(12f) Development of a Biologically-Inspired Approach for Advanced Adaptive Control of Clean Energy Systems
For this study, a Multi-Input-Multi-Output (MIMO) system from the IGCC-AGR process in DYNSIM (software used for dynamic simulations of chemical processes) is chosen. In particular, CO2/H2S absorption units of the IGCC-AGR process are analyzed for the control structure selection. Simplified dynamic models are derived for BIO-CS employing system identification techniques, such as the autoregressive model with exogenous inputs (ARX). The optimal control trajectories are then computed by BIO-CS using the simplified model to maintain multiple outputs at their desired setpoints. The controlled/output variables considered in this implementation are the compositionsof the outgoing stream and the temperature of the incoming solvent related to the CO2 absorption unit. The manipulated/input variables are the flowrates of the recycled solvent and the refrigerant associated with the same unit. Similar variables are selected for the H2S absorption system, thus resulting in an overall high-dimensional system with multiple control islands. The proposed controller framework is designed in MATLAB and the control laws are communicated to the IGCC-AGR process simulation in DYNSIM by employing a MATLAB-DYNSIM link. This application represents the realistic scenario of the inherent mismatch between the plant and the model used by the controller. To mitigate this mismatch, an ANN-based adaptive component that has online learning capabilities is incorporated into the BIO-CS formulation. Using the information of the tracking errors, outputs, and available states, the adaptive BIO-CS brings the system back to the desired operating point. Preliminary results on the implementation of the proposed framework for the CO2 capture island show promising capabilities in terms of maintaining the system at the required level of carbon capture. In addition, these results demonstrate the potential of the BIO-CS with adaptive component framework to tackle multiple challenges, such as nonlinearities, high dimensionality and plant-model mismatches that are commonly encountered in the process control industries.
- Lima F. V., Li S., Mirlekar G. V., Sridhar L. N. and Ruiz-Mercado G. J., âModeling and advanced control for sustainable process systemsâ. Sustainability in the Analysis, Synthesis and Design of Chemical Engineering Processes, G. Ruiz-Mercado and H. Cabezas (eds.), Elsevier, 2016.
- Li S., Mirlekar G. V., Ruiz-Mercado G. J. and Lima F. V., âDevelopment of chemical process design and control for sustainabilityâ. Processes, 4(3):23, 2016.
- Mirlekar G. V., Li S. and Lima F. V., âDesign and implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for chemical process controlâ. Submitted for publication.
- Mirlekar G. V., Pezzini P., Bryden M., Tucker D. and Lima F. V., âA Biologically-Inspired Optimal Control Strategy (BIO-CS) for hybrid energy systemsâ. To appear in Proceedings of 2017 American Control Conference.
- Mirlekar G. V. and Lima F. V., âDesign and implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for advanced energy systemsâ. In AIChE Annual Meeting, San Francisco, CA, 2016.