(12f) Development of a Biologically-Inspired Approach for Advanced Adaptive Control of Clean Energy Systems

Mirlekar, G. V. - Presenter, West Virginia University
Al-Sinbol, G., West Virginia University
Perhinschi, M., West Virginia University
Lima, F. V., West Virginia University
Over the last decades, biologically-inspired control techniques have emerged as attractive alternatives for process control applications. For example, a Biologically-Inspired Optimal Control Strategy (BIO-CS) has been recently proposed and illustrated via examples including a nonlinear fermentation process1-3 and a hybrid energy system that integrates different process components4. Also, a low-dimensional system associated with the Acid Gas Removal (AGR) unit of an Integrated Gasification Combined Cycle (IGCC) plant was controlled using BIO-CS considering setpoint tracking cases5. Specifically, BIO-CS is an agent-based control strategy that is inspired by the ant’s rule of pursuit idea in combination with gradient-based optimal control concepts1-5. In previous applications of BIO-CS, the multi-agent aspects of the algorithm were studied in detail and different termination criteria were analyzed to accommodate computational time limitations. However, the existence of plant-model mismatches as well as multiple disturbances in the control of high-dimensional systems has not yet been addressed using BIO-CS. To fill these gaps, in this presentation, BIO-CS is further extended to incorporate an adaptive component based on an Artificial Neural Network (ANN) method into the controller formulation. The proposed framework is then implemented for tackling high-dimensional control structures of the IGCC-AGR process simulation considering more realistic scenarios that could be encountered in practice.

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.


  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., 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.
  5. 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.