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Sparse Identification Modeling and Predictive Control of Nonlinear Processes- [electronic resource]
Sparse Identification Modeling and Predictive Control of Nonlinear Processes- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101925
- ISBN
- 9798380845281
- DDC
- 660
- 저자명
- Abdullah, Fahim.
- 서명/저자
- Sparse Identification Modeling and Predictive Control of Nonlinear Processes - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(371 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
- 주기사항
- Advisor: Christofides, Panagiotis D.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Data is widely recognized as a crucial player in the fourth industrial revolution, in which engineers and computers must harness data to enhance the efficiency of industrial processes and their associated control systems. Traditional industrial process control systems rely on linear data-driven models, with parameters fitted to experimental or simulated data. In specific control loops, such as those critical for profit optimization, they may employ first-principles models describing the underlying physico-chemical phenomena but with a few data-derived parameters. Nevertheless, modeling complex, nonlinear processes on a large scale remains an open challenge in process systems engineering. The quality of these models depends on various factors, including model parameter estimation, model uncertainty, the number of assumptions made during model development, model dimensionality, structure, and the computational demands for real-time model solutions. This is especially pertinent as process models are integral to advanced model-based control systems, such as model predictive control (MPC) and economic MPC (EMPC). Designing MPC systems that utilize data-driven modeling techniques to account in real-time for large data sets is a new frontier that will impact the next generation of industrial control systems. While a significant body of research has been dedicated to the use of neural networks for nonlinear process modeling and control, in both the theoretical and practical domains, more computationally efficient models that can directly be used in MPC rather than their linearized counterparts, are still an growing area of research that can lead to the design of more robust and efficient control systems.Motivated by the above considerations, this dissertation presents the use of a computationally efficient data-driven technique known as sparse identification in model predictive control for chemical processes described by nonlinear dynamic models. The motivation and organization of this dissertation are first presented. Then, the use of sparse identification to develop nonlinear dynamic process models to be used in model predictive controllers is presented, specifically addressing the challenges of two-time-scale systems, sensor noise, industrial nonlinearities, and process shifts. The MPC and economic MPC schemes that use sparse identified models are presented in detail with rigorous analysis provided on their closed-loop stability and recursive feasibility properties. Finally, the dissertation closes with an overview of the novelties introduced to overcome the aforementioned challenges and a detailed guide to developing nonlinear process models for complex chemical processes using sparse identification. Throughout the dissertation, the proposed methods are applied to numerical simulations of nonlinear chemical process examples and Aspen Plus simulations of large-scale chemical process networks to demonstrate their effectiveness.
- 일반주제명
- Chemical engineering.
- 일반주제명
- Computer engineering.
- 일반주제명
- Engineering.
- 키워드
- Machine learning
- 기타저자
- University of California, Los Angeles Chemical Engineering 0294
- 기본자료저록
- Dissertations Abstracts International. 85-05B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101925
■006m o d
■007cr#unu||||||||
■020 ▼a9798380845281
■035 ▼a(MiAaPQ)AAI30695578
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aAbdullah, Fahim.
■24510▼aSparse Identification Modeling and Predictive Control of Nonlinear Processes▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Los Angeles. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(371 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-05, Section: B.
■500 ▼aAdvisor: Christofides, Panagiotis D.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aData is widely recognized as a crucial player in the fourth industrial revolution, in which engineers and computers must harness data to enhance the efficiency of industrial processes and their associated control systems. Traditional industrial process control systems rely on linear data-driven models, with parameters fitted to experimental or simulated data. In specific control loops, such as those critical for profit optimization, they may employ first-principles models describing the underlying physico-chemical phenomena but with a few data-derived parameters. Nevertheless, modeling complex, nonlinear processes on a large scale remains an open challenge in process systems engineering. The quality of these models depends on various factors, including model parameter estimation, model uncertainty, the number of assumptions made during model development, model dimensionality, structure, and the computational demands for real-time model solutions. This is especially pertinent as process models are integral to advanced model-based control systems, such as model predictive control (MPC) and economic MPC (EMPC). Designing MPC systems that utilize data-driven modeling techniques to account in real-time for large data sets is a new frontier that will impact the next generation of industrial control systems. While a significant body of research has been dedicated to the use of neural networks for nonlinear process modeling and control, in both the theoretical and practical domains, more computationally efficient models that can directly be used in MPC rather than their linearized counterparts, are still an growing area of research that can lead to the design of more robust and efficient control systems.Motivated by the above considerations, this dissertation presents the use of a computationally efficient data-driven technique known as sparse identification in model predictive control for chemical processes described by nonlinear dynamic models. The motivation and organization of this dissertation are first presented. Then, the use of sparse identification to develop nonlinear dynamic process models to be used in model predictive controllers is presented, specifically addressing the challenges of two-time-scale systems, sensor noise, industrial nonlinearities, and process shifts. The MPC and economic MPC schemes that use sparse identified models are presented in detail with rigorous analysis provided on their closed-loop stability and recursive feasibility properties. Finally, the dissertation closes with an overview of the novelties introduced to overcome the aforementioned challenges and a detailed guide to developing nonlinear process models for complex chemical processes using sparse identification. Throughout the dissertation, the proposed methods are applied to numerical simulations of nonlinear chemical process examples and Aspen Plus simulations of large-scale chemical process networks to demonstrate their effectiveness.
■590 ▼aSchool code: 0031.
■650 4▼aChemical engineering.
■650 4▼aComputer engineering.
■650 4▼aEngineering.
■653 ▼aMachine learning
■653 ▼aModel predictive control
■653 ▼aNonlinear processes
■653 ▼aProcess uncertainties
■653 ▼aSingular perturbations
■653 ▼aSparse identification
■690 ▼a0542
■690 ▼a0464
■690 ▼a0537
■71020▼aUniversity of California, Los Angeles▼bChemical Engineering 0294.
■7730 ▼tDissertations Abstracts International▼g85-05B.
■773 ▼tDissertation Abstract International
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935383▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
