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Design Optimization of Highly Uncertain Processes: Applications to Papermaking System

Research output: Book/ReportDoctoral thesisCollection of Articles

Standard

Design Optimization of Highly Uncertain Processes: Applications to Papermaking System. / Ropponen, Aino.

Tampere University of Technology, 2013. 89 p. (Tampere University of Technology. Publication; Vol. 1106).

Research output: Book/ReportDoctoral thesisCollection of Articles

Harvard

Ropponen, A 2013, Design Optimization of Highly Uncertain Processes: Applications to Papermaking System. Tampere University of Technology. Publication, vol. 1106, Tampere University of Technology.

APA

Ropponen, A. (2013). Design Optimization of Highly Uncertain Processes: Applications to Papermaking System. (Tampere University of Technology. Publication; Vol. 1106). Tampere University of Technology.

Vancouver

Ropponen A. Design Optimization of Highly Uncertain Processes: Applications to Papermaking System. Tampere University of Technology, 2013. 89 p. (Tampere University of Technology. Publication).

Author

Ropponen, Aino. / Design Optimization of Highly Uncertain Processes: Applications to Papermaking System. Tampere University of Technology, 2013. 89 p. (Tampere University of Technology. Publication).

Bibtex - Download

@book{1f0526c4919f4ae1ae37459267e96e2c,
title = "Design Optimization of Highly Uncertain Processes: Applications to Papermaking System",
abstract = "In process design, the goal is to find a process structure that satisfies the desired targets and constraints. A typical task involves decision making related to the process flow-sheet and equipment. This dissertation examines design optimization of papermaking process. The main emphasis is on the development of an optimal design procedure for highly uncertain processes with non-Gaussian uncertainties. The design problem is studied as a multiobjective task in which the most effective process structure is sought by maximizing the process long-term performance and minimizing the investment cost. As the assessment of the long-term performance requires that the process be operated optimally, the optimization of the process operation is studied as a subtask of the design problem. Paper manufacturing is a complex process in which paper is produced from wood, water, and chemicals. The task is to manufacture uniform quality paper while minimizing the costs. If the paper web breaks, all the production is discarded. The unpredictable web breaks strongly disturb the paper production. As a result, the process has two separate operating points: normal operation and operation during web breaks. That poses challenges to the process operation as the transition between the operating points is somewhat random and the future evolution of the process is not completely predictable. In model-based process optimization, the uncertainty related to the models affects the reliability of the results. The modelling uncertainty is associated with both the incom-plete understanding of the process and the approximation due to computational reasons. In papermaking, the unpredictable web breaks are the largest source of uncertainty, but incomplete understanding is also related to e.g. the quality models of the paper. Besides modelling uncertainty, also the uncertainty about the available information, i.e. the measurement accuracy, affects the reliability of the optimization. In this thesis, schedul-ing of the measurement resources is studied as a part of the process optimization. This dissertation proposes a procedure to systematically optimize the design and operation of a papermaking process. The procedure is presented at six stages, including problem formulation, modelling, operational optimization, design optimization, robustness analysis, and validation. The main focus is at the operational and design optimization stages, but the purpose of all stages is discussed. The proposed procedure is demonstrated with case studies. The studied cases deal with two types of problems: discrete state systems with uncertain state information and continuous state systems with two operating points. In both groups, non-Gaussian uncertainty plays an important role.",
author = "Aino Ropponen",
note = "Awarding institution:Tampereen teknillinen yliopisto - Tampere University of Technology<br/>Submitter:Submitted by Aino Ropponen (aino.ropponen@tut.fi) on 2013-03-13T09:47:20Z No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)<br/>Submitter:Approved for entry into archive by Kaisa Kulkki (kaisa.kulkki@tut.fi) on 2013-03-13T13:32:06Z (GMT) No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)<br/>Submitter:Made available in DSpace on 2013-03-13T13:32:07Z (GMT). No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)",
year = "2013",
month = "1",
day = "25",
language = "English",
isbn = "978-952-15-2999-3",
series = "Tampere University of Technology. Publication",
publisher = "Tampere University of Technology",

}

RIS (suitable for import to EndNote) - Download

TY - BOOK

T1 - Design Optimization of Highly Uncertain Processes: Applications to Papermaking System

AU - Ropponen, Aino

N1 - Awarding institution:Tampereen teknillinen yliopisto - Tampere University of Technology<br/>Submitter:Submitted by Aino Ropponen (aino.ropponen@tut.fi) on 2013-03-13T09:47:20Z No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)<br/>Submitter:Approved for entry into archive by Kaisa Kulkki (kaisa.kulkki@tut.fi) on 2013-03-13T13:32:06Z (GMT) No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)<br/>Submitter:Made available in DSpace on 2013-03-13T13:32:07Z (GMT). No. of bitstreams: 1 2_manu0701b.pdf: 1875081 bytes, checksum: ae2e6ca0e1416de9f3df7a99929d4674 (MD5)

PY - 2013/1/25

Y1 - 2013/1/25

N2 - In process design, the goal is to find a process structure that satisfies the desired targets and constraints. A typical task involves decision making related to the process flow-sheet and equipment. This dissertation examines design optimization of papermaking process. The main emphasis is on the development of an optimal design procedure for highly uncertain processes with non-Gaussian uncertainties. The design problem is studied as a multiobjective task in which the most effective process structure is sought by maximizing the process long-term performance and minimizing the investment cost. As the assessment of the long-term performance requires that the process be operated optimally, the optimization of the process operation is studied as a subtask of the design problem. Paper manufacturing is a complex process in which paper is produced from wood, water, and chemicals. The task is to manufacture uniform quality paper while minimizing the costs. If the paper web breaks, all the production is discarded. The unpredictable web breaks strongly disturb the paper production. As a result, the process has two separate operating points: normal operation and operation during web breaks. That poses challenges to the process operation as the transition between the operating points is somewhat random and the future evolution of the process is not completely predictable. In model-based process optimization, the uncertainty related to the models affects the reliability of the results. The modelling uncertainty is associated with both the incom-plete understanding of the process and the approximation due to computational reasons. In papermaking, the unpredictable web breaks are the largest source of uncertainty, but incomplete understanding is also related to e.g. the quality models of the paper. Besides modelling uncertainty, also the uncertainty about the available information, i.e. the measurement accuracy, affects the reliability of the optimization. In this thesis, schedul-ing of the measurement resources is studied as a part of the process optimization. This dissertation proposes a procedure to systematically optimize the design and operation of a papermaking process. The procedure is presented at six stages, including problem formulation, modelling, operational optimization, design optimization, robustness analysis, and validation. The main focus is at the operational and design optimization stages, but the purpose of all stages is discussed. The proposed procedure is demonstrated with case studies. The studied cases deal with two types of problems: discrete state systems with uncertain state information and continuous state systems with two operating points. In both groups, non-Gaussian uncertainty plays an important role.

AB - In process design, the goal is to find a process structure that satisfies the desired targets and constraints. A typical task involves decision making related to the process flow-sheet and equipment. This dissertation examines design optimization of papermaking process. The main emphasis is on the development of an optimal design procedure for highly uncertain processes with non-Gaussian uncertainties. The design problem is studied as a multiobjective task in which the most effective process structure is sought by maximizing the process long-term performance and minimizing the investment cost. As the assessment of the long-term performance requires that the process be operated optimally, the optimization of the process operation is studied as a subtask of the design problem. Paper manufacturing is a complex process in which paper is produced from wood, water, and chemicals. The task is to manufacture uniform quality paper while minimizing the costs. If the paper web breaks, all the production is discarded. The unpredictable web breaks strongly disturb the paper production. As a result, the process has two separate operating points: normal operation and operation during web breaks. That poses challenges to the process operation as the transition between the operating points is somewhat random and the future evolution of the process is not completely predictable. In model-based process optimization, the uncertainty related to the models affects the reliability of the results. The modelling uncertainty is associated with both the incom-plete understanding of the process and the approximation due to computational reasons. In papermaking, the unpredictable web breaks are the largest source of uncertainty, but incomplete understanding is also related to e.g. the quality models of the paper. Besides modelling uncertainty, also the uncertainty about the available information, i.e. the measurement accuracy, affects the reliability of the optimization. In this thesis, schedul-ing of the measurement resources is studied as a part of the process optimization. This dissertation proposes a procedure to systematically optimize the design and operation of a papermaking process. The procedure is presented at six stages, including problem formulation, modelling, operational optimization, design optimization, robustness analysis, and validation. The main focus is at the operational and design optimization stages, but the purpose of all stages is discussed. The proposed procedure is demonstrated with case studies. The studied cases deal with two types of problems: discrete state systems with uncertain state information and continuous state systems with two operating points. In both groups, non-Gaussian uncertainty plays an important role.

M3 - Doctoral thesis

SN - 978-952-15-2999-3

T3 - Tampere University of Technology. Publication

BT - Design Optimization of Highly Uncertain Processes: Applications to Papermaking System

PB - Tampere University of Technology

ER -