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Download Full PDF Package. Translate PDF. Graduate Theses, Dissertations, and Problem Reports Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling Xin He West Virginia University, xnhe mix. Graduate Theses, mobile robot phd thesis, Dissertations, and Problem Reports.
It has been brought to you by the The Research Repository WVU with permission from the rights-holder s. You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use, mobile robot phd thesis. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository WVU. For more information, please contact researchrepository mail, mobile robot phd thesis.
Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling Xin He Dissertation submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemical Engineering Fernando V. Significant overshooting and oscillatory performance may occur mobile robot phd thesis cycling operations if classical feedback control strategies are employed for plantwide control.
To minimize the impact when power plants are operating away from their designed conditions, model-based optimal control strategies would need to be developed for improved power plant performance during cycling.
In this thesis, model predictive control MPC strategies are designed and implemented for improved power plant cycling. The Mobile robot phd thesis strategies addressed correspond to a dynamic matrix control DMC -based linear MPC, a classical sequential quadratic programming SQP -based nonlinear MPC, mobile robot phd thesis direct transcription-based nonlinear MPC and a proposed modified SQP-based nonlinear MPC, mobile robot phd thesis.
The proposed modified SQP algorithm is based on the backtracking line search framework, which employs a group of relaxed step acceptance conditions for faster convergence. The numerical results for motivating examples, which mobile robot phd thesis selected from literature problem sets, served as proof of concept to verify that the proposed modified SQP has the potential for implementation on high-dimensional systems.
To illustrate the tracking performance and computational efficiency of the developed MPC strategies, three processes of different dimensionalities are addressed. The first process is an integrated gasification combined cycling power plant with a water-gas shift membrane reactor IGCC-MRmobile robot phd thesis, which is represented by a first-principles and simplified systems-level nonlinear model in MATLAB. The second application is an aqueous monoethanolamine MEA -based carbon capture process as part of a supercritical pulverized coal-fired SCPC power plant, whose model is built in Aspen Plus Dynamics.
For this system, mobile robot phd thesis, disturbance rejection scenarios considering a ramp decrease in the flue gas flow rate as well as wind power penetration, and a scenario considering a combination of disturbance rejection and setpoint tracking are addressed.
The third process is the entire SCPC power plant with MEA-based carbon capture SCPC-MEAwhich simulation is also built in Aspen Plus Dynamics. Trajectory tracking and disturbance rejection scenarios associated with wind and solar power penetrations are presented for this process.
The MPC implementations on the three processes for the different scenarios addressed are successful. Acknowledgments I would like to thank many people who impacted and influenced my stay as a Ph. student at West Virginia University.
I would like to express my deepest gratitude to Dr. Fernando V. Lima, who served as my advisor and a great mentor during my stay in WVU. His knowledge and expertise helped me immensely mobile robot phd thesis understanding model predictive control approaches and process control applications. His guidance, mobile robot phd thesis and focus set up a great role model for me. I would also like to thank my thesis committee members: Dr.
Richard Turton, Dr. Debangsu Bhattacharyya, Dr. Stephen Zitney, mobile robot phd thesis, Dr. Parviz Famouri and Dr. Rishi Amrit, for their insightful comments, support and guidance in this research work. I would like to thank U.
Department of Energy for their funding support throughout my Ph, mobile robot phd thesis. research work. I would like to acknowledge Drs. Lima, Turton and Bhattacharyya once again and also Qiang, Yifan, Pushpitha for the useful discussion in the project meetings and insightful comments.
I would also like to express my appreciation for the research collaborators in Tsinghua University, especially Dr. Li, Dr. Liu and Jin. I sincerely thank all the CODES research group members, Shuyun, Rebecca, Vitor, Dr.
Gaurav, Dr. Juan, Jacob, for all their company in research and friendship through the years. I would also like to thank Dr. David, Dr. Bruno, Gilson, Vitor F. Thanks also to my friends Yifan, Qiang, He, Xuan, Lei, Yuan, Qiao, Xiao, Xiaoqian, Xue, Xiaofei, Yu and many others for making my life at Morgantown comfortable and fun.
Finally and most importantly, my family for supporting me with love all these years. My mother, Xiaoyi He, who always ready for supporting me and helped me with important details in my life. My father, Dong He, who encouraged me and believed in me for my dreams.
My gratitude to them is indescribable. ii Acknowledgments iv List of Figures vii Mobile robot phd thesis of Tables ix Nomenclature Research Outputs Thesis Organization Literature Review MPC Role in Hierarchical Planning and Operations Algorithms for Solving NLP for MPC Applications MPC Implementation for Coal-fired Power Plants Literature Review Summary Novel Modified SQP-based Algorithm for MPC Modified SQP Approach Algorithm Convergence Analysis Numerical Results for Motivating Examples MPC Approaches Developed and Implementation Framework Linear MPC Approach Nonlinear MPC Approaches MPC Implementation Framework Power Plant Dynamic Modeling IGCC-MR Process MEA-based Carbon Capture Process Conventional Carbon Capture Process LVC Carbon Capture Process Entire SCPC-MEA Process MPC Applications to Power Plant Cycling MPC Application to IGCC-MR System MPC Application to MEA-based Carbon Capture System MPC Application to Entire SCPC-MEA System Future Recommendations
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