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An Optimization Framework for Kinetic Model Building from Concentration and Spectroscopic Data
An Optimization Framework for Kinetic Model Building from Concentration and Spectroscopic Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151349
- ISBN
- 9798382339344
- DDC
- 660
- 서명/저자
- An Optimization Framework for Kinetic Model Building from Concentration and Spectroscopic Data
- 발행사항
- [Sl] : Carnegie Mellon University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 163 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Biegler, Lorenz T.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2024.
- 초록/해제
- 요약This dissertation deals with the development of an optimization framework for kinetic model building from experimentally measured concentration and spectroscopic data. We develop mechanistic models based on first-principles and use a statistical criteria for model discrimination when more than one model is proposed. While many predictive model building methods exist using data-driven approaches, mechanistic models provide a physical understanding of resulting estimates and allow the investigator to elicit additional information about the underlying structure of the system. Tightly coupled with kinetic model building is parameter estimation, where degrees of freedom are related to unknown reaction rate parameters and other sources of measurement uncertainty such as unknown initial conditions and spectroscopic absorbance. Accurate estimation methods which maximize the information from experimentally collected data are imperative, but detailed physics-based models with multiple datasets present a computational challenges as the problem size and complexity increases. In this work, we present strategies to address these common obstacles. The model building framework is based on simultaneous full-discretization approaches and interior-point nonlinear programming (NLP) solvers which exploit problem structure and exact second derivatives resulting in favorable computational efficiency. First, we review relevant nonlinear optimization theory, which motivates the use of interior-point algorithms for kinetic model building. In addition, we discussion advantages and disadvantages of different approaches for parameter estimation from spectroscopic data, with special emphasis on the advantages of the simultaneous solution strategy. To present the flexibility and robustness of this framework, we investigate various reaction networks with real-world experimentally measured data. Chapter 3 describes an application of nonlinear mixed-effects models, an alternative modeling technique commonly used in pharmacometrics to capture batch-to-batch variation between experiments, to a single response hydrogenation reaction in a trickle-bed batch reactor system. Chapters 4, 5, and 6 examine different applications of our kinetic model building framework to obtain accurate predictions of rate constants, concentration profiles, and pure component absorbance profiles from in situ spectroscopic data. In Chapter 4 and Chapter 6, we develop population balance models for ring-opening polymerization reactions. Chapter 5 presents a challenging case study where temperature dependence and hydrogen-bonding effects play an important role. All modeling strategies use the state-of-the-art NLP solver IPOPT and the algebraic modeling language Pyomo.
- 일반주제명
- Chemical engineering
- 일반주제명
- Analytical chemistry
- 일반주제명
- Computational chemistry
- 기타저자
- Carnegie Mellon University Chemical Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798382339344
■035 ▼a(MiAaPQ)AAI31242989
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aKrumpolc, Thomas J.
■24513▼aAn Optimization Framework for Kinetic Model Building from Concentration and Spectroscopic Data
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a163 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Biegler, Lorenz T.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2024.
■520 ▼aThis dissertation deals with the development of an optimization framework for kinetic model building from experimentally measured concentration and spectroscopic data. We develop mechanistic models based on first-principles and use a statistical criteria for model discrimination when more than one model is proposed. While many predictive model building methods exist using data-driven approaches, mechanistic models provide a physical understanding of resulting estimates and allow the investigator to elicit additional information about the underlying structure of the system. Tightly coupled with kinetic model building is parameter estimation, where degrees of freedom are related to unknown reaction rate parameters and other sources of measurement uncertainty such as unknown initial conditions and spectroscopic absorbance. Accurate estimation methods which maximize the information from experimentally collected data are imperative, but detailed physics-based models with multiple datasets present a computational challenges as the problem size and complexity increases. In this work, we present strategies to address these common obstacles. The model building framework is based on simultaneous full-discretization approaches and interior-point nonlinear programming (NLP) solvers which exploit problem structure and exact second derivatives resulting in favorable computational efficiency. First, we review relevant nonlinear optimization theory, which motivates the use of interior-point algorithms for kinetic model building. In addition, we discussion advantages and disadvantages of different approaches for parameter estimation from spectroscopic data, with special emphasis on the advantages of the simultaneous solution strategy. To present the flexibility and robustness of this framework, we investigate various reaction networks with real-world experimentally measured data. Chapter 3 describes an application of nonlinear mixed-effects models, an alternative modeling technique commonly used in pharmacometrics to capture batch-to-batch variation between experiments, to a single response hydrogenation reaction in a trickle-bed batch reactor system. Chapters 4, 5, and 6 examine different applications of our kinetic model building framework to obtain accurate predictions of rate constants, concentration profiles, and pure component absorbance profiles from in situ spectroscopic data. In Chapter 4 and Chapter 6, we develop population balance models for ring-opening polymerization reactions. Chapter 5 presents a challenging case study where temperature dependence and hydrogen-bonding effects play an important role. All modeling strategies use the state-of-the-art NLP solver IPOPT and the algebraic modeling language Pyomo.
■590 ▼aSchool code: 0041.
■650 4▼aChemical engineering
■650 4▼aAnalytical chemistry
■650 4▼aComputational chemistry
■653 ▼aSpectroscopic data
■653 ▼aKinetic model building
■653 ▼aData-driven approaches
■653 ▼aSpectroscopic absorbance
■653 ▼aComputational efficiency
■690 ▼a0542
■690 ▼a0486
■690 ▼a0219
■71020▼aCarnegie Mellon University▼bChemical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161385▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
