(390h) Design and Implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for Advanced Energy Systems

Biologically-inspired strategies or biomimetics are the human-made designs that imitate nature. In the last few decades, this area of research has provided ideas for the solutions of various engineering problems. For example, the behavior of natural groups such as ants, bees and swarms has previously inspired the development of control approaches in other engineering fields. In these natural groups, self-organization and cooperation between individual members by following simple rules of interaction can result in a wide range of optimal patterns [1]. In this work, a biomimetic optimal control strategy is proposed for chemical process control. This strategy is inspired by the antâ??s rule of pursuit idea combined with optimal control concepts. In this novel approach, entitled Biologically-Inspired Optimal Control Strategy (BIO-CS), the cooperation of multiple agents (or ants) enables the generation of optimal control trajectories between the current state and the desired setpoint. In particular, starting from an initially feasible trajectory for the leader agent, each follower agent improves its path towards the setpoint by employing optimal control laws. As the number of agent progresses, the trajectories converge to an optimal solution.

To demonstrate the effectiveness of the proposed approach, two chemical and energy systems are addressed for the application of BIO-CS. The first implementation case study corresponds to the setpoint tracking and disturbance rejection associated with a fermentation process for bioethanol production. The challenges in this fermentation system consist of nonlinearities present in the dynamic process model in addition to steady-state multiplicity. The BIO-CS implementation results for this fermentation process show smooth and offset free output trajectories with improved performance when compared to other classical and optimal control approaches [2, 3, 4]. The second application of BIO-CS addressed is the Acid Gas Removal (AGR) unit of an Integrated Gasification Combined Cycle (IGCC) process. In this case, an innovative framework is proposed for the closed-loop implementation of BIO-CS. For this implementation, an IGCC-AGR process model in DYNSIM (software used for dynamic simulations of chemical processes) is employed. As the first step, a subsystem from the IGCC-AGR process simulation is selected to define the control loops for the BIO-CS implementation. Then, a simplified dynamic model for use by the controller is derived employing system identification techniques. Specifically, an autoregressive model with exogenous inputs (ARX) is a developed from input/output data of the IGCC-AGR DYNSIM plant. The optimal control trajectories computed by BIO-CS are implemented online for the selected subsystem considering two different scenarios: (i) BIO-CS controller model and the actual plant model are identical; and (ii) implementation for the actual IGCC-AGR sub-process simulation in DYNSIM. This case presents additional challenges to the controller associated with plant-model mismatch. For the communication between the BIO-CS controller designed in MATLAB and the DYNSIM plant, the MATLAB-DYNSIM link developed at West Virginia University is employed. The optimal control laws computed by the BIO-CS are transmitted as setpoint trajectories for the PID controllers already in place in the DYNSIM plant. The implementation results demonstrate the potential of the BIO-CS for online implementation for processes with different challenges, including nonlinearities, high dimensionality, steady-state multiplicity and plant-model mismatch.

References:

1. Hristu-Varsakelis D. and Shao C., â??A bio-inspired pursuit strategy for optimal control with partially constrained final stateâ?. Automatica 2007;43:1265-1273.
2. Lima F. V., Li S., Mirlekar G. V., Sridhar L. N. and Ruiz-Mercado G. J., â??Modeling 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.
3. Mirlekar G. V., Gebreslassie B. H., Diwekar U. M. and Lima F. V., "Design and implementation of a biomimetic control strategy for chemical processes based on efficient ant colony optimization." Presented at AIChE Annual Meeting, Salt Lake City, Utah, November 2015.
4. Li S., Mirlekar G. V., Ruiz-Mercado G. J., and Lima F. V., â??Development of Chemical Process Design and Control for Sustainabilityâ?. Submitted for publication, 2016.