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Advances in Nonlinear Model Predictive Control and Their Applications in Chemical Engineering- [electronic resource]
Advances in Nonlinear Model Predictive Control and Their Applications in Chemical Engineering- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101640
- ISBN
- 9798380100694
- DDC
- 660
- 저자명
- Lin, Kuan-Han.
- 서명/저자
- Advances in Nonlinear Model Predictive Control and Their Applications in Chemical Engineering - [electronic resource]
- 발행사항
- [S.l.]: : Carnegie Mellon University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(174 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
- 주기사항
- Advisor: Biegler, Lorenz.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Model Predictive Control (MPC) has emerged as a promising optimization-based controller in various industrial applications because of its nature of coping with variable bounds and multiple-input-multiple-output (MIMO) dynamic processes. Nonlinear MPC (NMPC) is the nonlinear branch of MPC that makes use of the nonlinear model and constraints to achieve higher accuracy for systems with complicated dynamics. However, the performance of NMPC is influenced by process uncertainty and computational delays. In addition, it faces stability challenges when considering economically oriented objectives. This thesis aims to enhance the performance of NMPC by developing advanced features that improve robustness, stability, and economic efficiency while maintaining reasonable online computation by leveraging both control and optimization theory.First, we consider the well pumping period in hydraulic fracturing and propose a robust control strategy aimed at addressing the constraint violations on operating pressure and terminal requirements resulted from the uncertainty in the rock layer. A comprehensive dynamic model that captures the process is constructed and incorporated into the predictive model of the robust multistage NMPC, which uses a scenario tree to depict the evolution of states with respect to uncertain parameters. The results demonstrate the promising robustness of the controller, as it satisfies all constraints in the face of the rock uncertainty that changes in time. Next, we develop a strategy to alleviate the online computational burden associated with solving Moving Horizon Estimation (MHE) problems, which is essential for NMPC when the process information is incomplete. We propose to solve an extended horizon MHE within a specified number of delayed sampling steps. This approach uses predicted future measurements in background and nonlinear programming (NLP) sensitivity to execute online corrections once the true measurements are available. The proposed algorithm is applied to a large-scale distillation column to show satisfactory estimation performance with negligible online computational effort.Finally, we propose two stable economic NMPC (eNMPC) formulations that achieve dual objectives of optimizing the economic goal and ensuring closed-loop stability. The proposed formulations track the optimality conditions of the real-time optimization problem instead of the exact setpoint, which eliminates the requirement of solving for the new setpoint when updating parameters related to the economic objective or system. We demonstrate the developed controllers on benchmark examples from the literature, including a continuous stirred-tank reactor and the aforementioned distillation column with improved economic results and guaranteed stability.
- 일반주제명
- Chemical engineering.
- 일반주제명
- Computer engineering.
- 일반주제명
- Computer science.
- 키워드
- Process control
- 키워드
- Nonlinear MPC
- 기타저자
- Carnegie Mellon University Chemical Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016934663
■00520240214101640
■006m o d
■007cr#unu||||||||
■020 ▼a9798380100694
■035 ▼a(MiAaPQ)AAI30632410
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aLin, Kuan-Han.
■24510▼aAdvances in Nonlinear Model Predictive Control and Their Applications in Chemical Engineering▼h[electronic resource]
■260 ▼a[S.l.]:▼bCarnegie Mellon University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(174 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-02, Section: B.
■500 ▼aAdvisor: Biegler, Lorenz.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aModel Predictive Control (MPC) has emerged as a promising optimization-based controller in various industrial applications because of its nature of coping with variable bounds and multiple-input-multiple-output (MIMO) dynamic processes. Nonlinear MPC (NMPC) is the nonlinear branch of MPC that makes use of the nonlinear model and constraints to achieve higher accuracy for systems with complicated dynamics. However, the performance of NMPC is influenced by process uncertainty and computational delays. In addition, it faces stability challenges when considering economically oriented objectives. This thesis aims to enhance the performance of NMPC by developing advanced features that improve robustness, stability, and economic efficiency while maintaining reasonable online computation by leveraging both control and optimization theory.First, we consider the well pumping period in hydraulic fracturing and propose a robust control strategy aimed at addressing the constraint violations on operating pressure and terminal requirements resulted from the uncertainty in the rock layer. A comprehensive dynamic model that captures the process is constructed and incorporated into the predictive model of the robust multistage NMPC, which uses a scenario tree to depict the evolution of states with respect to uncertain parameters. The results demonstrate the promising robustness of the controller, as it satisfies all constraints in the face of the rock uncertainty that changes in time. Next, we develop a strategy to alleviate the online computational burden associated with solving Moving Horizon Estimation (MHE) problems, which is essential for NMPC when the process information is incomplete. We propose to solve an extended horizon MHE within a specified number of delayed sampling steps. This approach uses predicted future measurements in background and nonlinear programming (NLP) sensitivity to execute online corrections once the true measurements are available. The proposed algorithm is applied to a large-scale distillation column to show satisfactory estimation performance with negligible online computational effort.Finally, we propose two stable economic NMPC (eNMPC) formulations that achieve dual objectives of optimizing the economic goal and ensuring closed-loop stability. The proposed formulations track the optimality conditions of the real-time optimization problem instead of the exact setpoint, which eliminates the requirement of solving for the new setpoint when updating parameters related to the economic objective or system. We demonstrate the developed controllers on benchmark examples from the literature, including a continuous stirred-tank reactor and the aforementioned distillation column with improved economic results and guaranteed stability.
■590 ▼aSchool code: 0041.
■650 4▼aChemical engineering.
■650 4▼aComputer engineering.
■650 4▼aComputer science.
■653 ▼aModel Predictive Control
■653 ▼aNonlinear programming
■653 ▼aProcess control
■653 ▼aNonlinear MPC
■653 ▼aMultiple-input-multiple-output
■653 ▼aOnline computation
■690 ▼a0542
■690 ▼a0984
■690 ▼a0464
■71020▼aCarnegie Mellon University▼bChemical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934663▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
