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Implementing Data Driven Modeling and Design of Experiments in Green Hydrogen Production Catalyst Discovery and Chemical Engineering Applications- [electronic resource]
Implementing Data Driven Modeling and Design of Experiments in Green Hydrogen Production Catalyst Discovery and Chemical Engineering Applications- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101924
- ISBN
- 9798380807593
- DDC
- 660
- 저자명
- Bhat, Maya.
- 서명/저자
- Implementing Data Driven Modeling and Design of Experiments in Green Hydrogen Production Catalyst Discovery and Chemical Engineering Applications - [electronic resource]
- 발행사항
- [S.l.]: : Carnegie Mellon University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(201 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
- 주기사항
- Advisor: Kitchin, John.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약In chemical engineering, scientific research has grown increasingly complex. Machine learning (ML) and data science offer tools to change how we conduct research by digitizing workflows, creating data-driven optimization models, making predictions for future research, and more. Despite recent rapid advances in these two fields, some research setups lack software infrastructure to leverage these tools. Bridging the gap with domain insights for both experimental setups and data science is essential to mutually benefit from their progress.This dissertation explores the intersection of data science, ML techniques and sequential experimentation guided by Design of Experiments (DoE) principles. The integration of computational techniques with experimental data, we can accelerate trend identification, multi-dimensional system optimization, and high-confidence decision boundary establishment, ultimately expediting experimental discoveries and conserving vital resources. The dissertation consists of six chapters, each contributing to the development of workflows to enhance experimental discovery.First, we layout the benefits and current limitations in incorporating modern machine learning and data science principles in fundamental scientific discovery. We discuss data sources, manipulation, model selection, and the importance of domain knowledge. This is critical in developing frameworks that account for experimental setup and data collection limitations while ensuring complete datasets that are machine readable for further analysis and modeling. Next, we apply these methods to a high throughput experimental setup measuring light driven H2 production from colloidal metallic heterogeneous catalysts. We take a data science approach to analyzing 96-well plate experiments and performing analytics on each experiment and the entire dataset. Given these findings, we study the in-situ catalyst formation, and optimize the resulting system for H2 production with DoE and subsequent analysis. We then extend these sampling methods towards identifying active multi-metallic catalysts containing Cu-Ru-Fe. The final section introduces novel methods of sequential sampling for different experimental goals - classification tasks. We aim to study different sequential sampling techniques to find divisions between desirable and undesirable regions with a high degree of certainty and few experimental samples.
- 일반주제명
- Chemical engineering.
- 일반주제명
- Energy.
- 키워드
- Catalysis
- 키워드
- Data science
- 키워드
- Hydrogen
- 키워드
- Machine learning
- 기타저자
- Carnegie Mellon University Chemical Engineering
- 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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■006m o d
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■020 ▼a9798380807593
■035 ▼a(MiAaPQ)AAI30693035
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aBhat, Maya.▼0(orcid)0000-0001-9750-9599
■24510▼aImplementing Data Driven Modeling and Design of Experiments in Green Hydrogen Production Catalyst Discovery and Chemical Engineering Applications▼h[electronic resource]
■260 ▼a[S.l.]:▼bCarnegie Mellon University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(201 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-05, Section: B.
■500 ▼aAdvisor: Kitchin, John.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aIn chemical engineering, scientific research has grown increasingly complex. Machine learning (ML) and data science offer tools to change how we conduct research by digitizing workflows, creating data-driven optimization models, making predictions for future research, and more. Despite recent rapid advances in these two fields, some research setups lack software infrastructure to leverage these tools. Bridging the gap with domain insights for both experimental setups and data science is essential to mutually benefit from their progress.This dissertation explores the intersection of data science, ML techniques and sequential experimentation guided by Design of Experiments (DoE) principles. The integration of computational techniques with experimental data, we can accelerate trend identification, multi-dimensional system optimization, and high-confidence decision boundary establishment, ultimately expediting experimental discoveries and conserving vital resources. The dissertation consists of six chapters, each contributing to the development of workflows to enhance experimental discovery.First, we layout the benefits and current limitations in incorporating modern machine learning and data science principles in fundamental scientific discovery. We discuss data sources, manipulation, model selection, and the importance of domain knowledge. This is critical in developing frameworks that account for experimental setup and data collection limitations while ensuring complete datasets that are machine readable for further analysis and modeling. Next, we apply these methods to a high throughput experimental setup measuring light driven H2 production from colloidal metallic heterogeneous catalysts. We take a data science approach to analyzing 96-well plate experiments and performing analytics on each experiment and the entire dataset. Given these findings, we study the in-situ catalyst formation, and optimize the resulting system for H2 production with DoE and subsequent analysis. We then extend these sampling methods towards identifying active multi-metallic catalysts containing Cu-Ru-Fe. The final section introduces novel methods of sequential sampling for different experimental goals - classification tasks. We aim to study different sequential sampling techniques to find divisions between desirable and undesirable regions with a high degree of certainty and few experimental samples.
■590 ▼aSchool code: 0041.
■650 4▼aChemical engineering.
■650 4▼aEnergy.
■653 ▼aCatalysis
■653 ▼aData science
■653 ▼aHydrogen
■653 ▼aMachine learning
■653 ▼aSequential sampling
■690 ▼a0542
■690 ▼a0791
■690 ▼a0800
■71020▼aCarnegie Mellon University▼bChemical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-05B.
■773 ▼tDissertation Abstract International
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935370▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
