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Title: Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants

Abstract

The objective of the project entitled “Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition, and Decision Capabilities for Control of Advanced Energy Plants” is to develop algorithms and methodologies for designing a biomimetic control system for optimal control of advanced energy plants. Traditionally, control systems have been designed based on assumed a priori knowledge of the process system. A number of variables are manipulated to accomplish disturbance rejection and/or servo control performance. While it is possible to adapt the process model based on available data, the control structure and the controllers rarely change. Thus, the knowledge learnt during process operation is lost or remains underutilized. In addition, the business models of today, especially for power plants, are rapidly changing. The power plant operators are being pushed to the edge due to the penetration of renewable energy derived power to the grid, the demand for high efficiency, ever-tightening environmental regulations, and requirements for increased plant availability. Thus, power plant operations need to be agile and should adapt quickly to the dynamic requirements. A number of characteristics distinguish biological systems from the traditional process control systems. Self-organization, distributed intelligence, adaptability, intelligent monitoring, cognition, and decision capabilities are some of the powerfulmore » 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, this project seeks to develop methodologies and algorithms to accomplish: (i) self-organization of the control structure for maximizing the plant’s operating profit by mimicking the function of the cortical areas in the human brain, (ii) distributed and adaptive controllers that mimic the rule of pursuit present in ants, (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 development and implementation of novel biomimetic methodologies for the control system design will contribute to the overall goal of establishing a biomimetic control framework for all levels in advanced power generation systems. With respect to the current state of the art, the specific outcomes of this project are: (i) algorithms for self-organizing control structure selection by mimicking the functioning of the cortical areas; (ii) novel distributed controller design that mimics the ant colony; (iii) transformative algorithms that increase robustness and autonomy of power systems through continuous adaptation powered by intelligent monitoring, cognition, and decision capabilities that mimics the immune system; (iv) integrated and comprehensive intelligent multi-agent optimization framework for autonomous operation of the entire system that mimics the CNS; (v) implementation and testing of the developed biomimetic control system design approach on an IGCC plant with CO 2 capture at an extended scale never attempted before.« less

Authors:
 [1];  [2];  [1];  [1];  [3];  [1];  [1];  [1];  [3]
  1. West Virginia University
  2. West Virginia Univ., Morgantown, WV (United States)
  3. Vishwamitra Research Institute
Publication Date:
Research Org.:
West Virginia Univ., Morgantown, WV (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1546598
Report Number(s):
DOE-WVURC-0012451
DOE Contract Number:  
FE0012451
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; Biomimetic, Controlled Variable, Control, Adaptive, multi-agent, IGCC, CO2 capture,

Citation Formats

Bhattacharyya, Debangsu, Lima, Fernando, Perhinschi, Mario, Turton, Richard, Diwekar, Urmila, Bankole, Temitayo, Al-Sinbol, Ghassan, Mirlekar, Gaurav, and Gebreslassie, Berhane. Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants. United States: N. p., 2018. Web. doi:10.2172/1546598.
Bhattacharyya, Debangsu, Lima, Fernando, Perhinschi, Mario, Turton, Richard, Diwekar, Urmila, Bankole, Temitayo, Al-Sinbol, Ghassan, Mirlekar, Gaurav, & Gebreslassie, Berhane. Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants. United States. doi:10.2172/1546598.
Bhattacharyya, Debangsu, Lima, Fernando, Perhinschi, Mario, Turton, Richard, Diwekar, Urmila, Bankole, Temitayo, Al-Sinbol, Ghassan, Mirlekar, Gaurav, and Gebreslassie, Berhane. Thu . "Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants". United States. doi:10.2172/1546598. https://www.osti.gov/servlets/purl/1546598.
@article{osti_1546598,
title = {Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants},
author = {Bhattacharyya, Debangsu and Lima, Fernando and Perhinschi, Mario and Turton, Richard and Diwekar, Urmila and Bankole, Temitayo and Al-Sinbol, Ghassan and Mirlekar, Gaurav and Gebreslassie, Berhane},
abstractNote = {The objective of the project entitled “Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition, and Decision Capabilities for Control of Advanced Energy Plants” is to develop algorithms and methodologies for designing a biomimetic control system for optimal control of advanced energy plants. Traditionally, control systems have been designed based on assumed a priori knowledge of the process system. A number of variables are manipulated to accomplish disturbance rejection and/or servo control performance. While it is possible to adapt the process model based on available data, the control structure and the controllers rarely change. Thus, the knowledge learnt during process operation is lost or remains underutilized. In addition, the business models of today, especially for power plants, are rapidly changing. The power plant operators are being pushed to the edge due to the penetration of renewable energy derived power to the grid, the demand for high efficiency, ever-tightening environmental regulations, and requirements for increased plant availability. Thus, power plant operations need to be agile and should adapt quickly to the dynamic requirements. A number of characteristics distinguish biological systems from the traditional process control systems. 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, this project seeks to develop methodologies and algorithms to accomplish: (i) self-organization of the control structure for maximizing the plant’s operating profit by mimicking the function of the cortical areas in the human brain, (ii) distributed and adaptive controllers that mimic the rule of pursuit present in ants, (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 development and implementation of novel biomimetic methodologies for the control system design will contribute to the overall goal of establishing a biomimetic control framework for all levels in advanced power generation systems. With respect to the current state of the art, the specific outcomes of this project are: (i) algorithms for self-organizing control structure selection by mimicking the functioning of the cortical areas; (ii) novel distributed controller design that mimics the ant colony; (iii) transformative algorithms that increase robustness and autonomy of power systems through continuous adaptation powered by intelligent monitoring, cognition, and decision capabilities that mimics the immune system; (iv) integrated and comprehensive intelligent multi-agent optimization framework for autonomous operation of the entire system that mimics the CNS; (v) implementation and testing of the developed biomimetic control system design approach on an IGCC plant with CO2 capture at an extended scale never attempted before.},
doi = {10.2172/1546598},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {2}
}