(188c) Development of Biomimetic Approaches for Intelligent Control System Design, Monitoring and Optimization of Advanced Energy Systems
Biological systems differ from the traditional process control systems in distinct ways. For example, self-organization, distributed intelligence, adaptability, intelligent monitoring, cognition, and decision capabilities are some of the powerful characteristics of the biological world that can be effectively utilized in process control. At the top, the central nervous system (CNS) integrates the information from and coordinates the activities of all parts of the bodies (for bilaterian animals). Inspired by these distinct characteristics, a novel biomimetic approach to control system design has been developed. In particular, a suite of methodologies and algorithms has been developed to accomplish: (i) self-organization of the control structure for maximizing the plantâs operating profit by mimicking the function of the cortical areas in the human brain, (ii) design of distributed, agent-based and adaptive controllers that mimic the antâs rule of pursuit combined with artificial neural network ideas, (iii) intelligent monitoring of the controllers powered with cognition and decision capabilities that mimic the artificial immune systems, and (iv) seamless coordination and integration in the control system that mimics the CNS.
The developed methodologies and algorithms are tested in a large-scale, plant-wide model of an Integrated Gasification Combined Cycle (IGCC) plant with CO2 capture. The present work shows that the biomimetic approaches can offer superior control system performance in comparison to the existing control system design techniques.
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