470331 Design and Implementation of a Biologically-Inspired Optimal Control Strategy (BIO-CS) for Advanced Energy Systems
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.
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