Simulation and Optimization in Process Engineering
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Simulation and Optimization in Process Engineering

The Benefit of Mathematical Methods in Applications of the Chemical Industry

1st Edition - April 16, 2022

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  • Editors: Michael Bortz, Norbert Asprion
  • Paperback ISBN: 9780323850438
  • eBook ISBN: 9780323850445

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Description

Simulation and Optimization in Process Engineering: The Benefit of Mathematical Methods in Applications of the Process Industry brings together examples where the successful transfer of progress made in mathematical simulation and optimization has led to innovations in an industrial context that created substantial benefit. Containing introductory accounts on scientific progress in the most relevant topics of process engineering (substance properties, simulation, optimization, optimal control and real time optimization), the examples included illustrate how such scientific progress has been transferred to innovations that delivered a measurable impact, covering details of the methods used, and more. With each chapter bringing together expertise from academia and industry, this book is the first of its kind, providing demonstratable insights.

Key Features

  • Recent mathematical methods are transformed into industrially relevant innovations.
  • Covers recent progress in mathematical simulation and optimization in a process engineering context with chapters written by experts from both academia and industry
  • Provides insight into challenges in industry aiming for a digitized world.

Readership

Chemical engineers, process engineers, research and development staff in the process industry. Mathematicians, physicists, computer scientists

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Preface
  • Chapter 1: Prediction and correlation of physical properties including transport and interfacial properties with the PC-SAFT equation of state
  • Abstract
  • 1: Model equations of PC-SAFT
  • 2: Parameterization
  • 3: Group-contribution methods for PC-SAFT
  • 4: Transport properties
  • 5: Interfacial properties
  • References
  • Chapter 2: Don’t search—Solve! Process optimization modeling with IDAES
  • Abstract
  • 1: Introduction
  • 2: Solution algorithms and optimization models
  • 3: Advanced optimization for differential-algebraic applications
  • 4: The IDAES optimization modeling software platform
  • 5: Carbon capture optimization case study
  • 6: Conclusions and future perspectives
  • References
  • Chapter 3: Thinking multicriteria—A jackknife when it comes to optimization
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Process design
  • 3: Model adjustment, model comparison and model-based design of experiments
  • 4: Decision support
  • References
  • Chapter 4: Integrated modeling and energetic optimization of the steelmaking process in electric arc furnaces: An industrial application
  • Abstract
  • 1: Introduction
  • 2: Electric arc furnace process model
  • 3: Dynamic optimization of the melting profiles
  • 4: Solution using control vector parametrization
  • 5: Results and discussions
  • 6: Conclusions
  • References
  • Chapter 5: Solvent recovery by batch distillation—Application of multivariate sensitivity studies to high dimensional multiobjective optimization problems
  • Abstract
  • 1: Introduction
  • 2: Problem definition
  • 3: Literature review
  • 4: Methodology
  • 5: Set up of the flowsheet simulation
  • 6: Results
  • 7: Summary
  • References
  • Chapter 6: Modeling and optimizing dynamic networks: Applications in process engineering and energy supply
  • Abstract
  • Acknowledgment
  • 1: Introduction
  • 2: AD-Net
  • 3: Applications in energy supply
  • 4: Applications in batch distillation
  • 5: Conclusion
  • References
  • Chapter 7: The use of digital twins to overcome low-redundancy problems in process data reconciliation
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Data reconciliation
  • 3: Clever mean and clever variance (cm and cv)
  • 4: Median and mad
  • 5: Industrial case study: Itelyum Regeneration amine washing unit
  • 6: Results
  • 7: Conclusions
  • References
  • Chapter 8: Real-time optimization of batch processes via optimizing feedback control
  • Abstract
  • 1: Introduction
  • 2: Representation of batch processes
  • 3: Numerical optimization of batch processes
  • 4: Feedback-based optimization of uncertain batchprocesses
  • 5: Illustrative example: Batch distillation column
  • 6: Conclusions
  • References
  • Chapter 9: On economic operation of switchable chlor-alkali electrolysis for demand-side management
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Operational mode switching of chlor-alkali electrolysis
  • 3: Mathematical formulation for optimal sizing and operation of switchable chlor-alkali electrolysis
  • 4: Case study
  • 5: Conclusion
  • References
  • Chapter 10: Optimal experiment design for dynamic processes
  • Abstract
  • 1: Introduction
  • 2: Optimal experiment design for model structure discrimination
  • 3: Optimal experiment design for parameter estimation
  • 4: Advanced developments in optimal experiment design
  • 5: Conclusions
  • References
  • Chapter 11: Characterization of reactions and growth in automated continuous flow and bioreactor platforms—From linear DoE to model-based approaches
  • Abstract
  • 1: Introduction
  • 2: Miniaturized platforms and applications
  • 3: Special aspects and challenges
  • 4: Industry view
  • 5: Discussion and conclusions
  • References
  • Chapter 12: Product development in a multicriteria context
  • Abstract
  • 1: Introduction
  • 2: Model fitting
  • 3: Multicriteria optimization and decision-making
  • 4: Approximating the set of efficient product designs
  • 5: Navigating the set of efficient product designs
  • 6: The role of Qritos in the design process
  • 7: Application: Designing an exterior paint recipe
  • 8: Outlook
  • References
  • Chapter 13: Dispatching for batch chemical processes using Monte-Carlo simulations—A practical approach to scheduling in operations
  • Abstract
  • 1: Introduction
  • 2: Proposed solution
  • 3: Implementation
  • 4: Beyond real-time operative scheduling
  • 5: Conclusions and outlook
  • References
  • Chapter 14: Applications of the RTN scheduling model in the chemical industry
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Review of RTN model
  • 3: Industry-led developments
  • 4: Industrial impact
  • 5: Conclusions
  • References
  • Index

Product details

  • No. of pages: 428
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: April 16, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780323850438
  • eBook ISBN: 9780323850445

About the Editors

Michael Bortz

Michael Bortz studied Physics in Dortmund and Grenoble. He received his Ph.D. in many particle physics at Dortmund University in 2003. After that, he worked as a PostDoc at universities in Wuppertal, Canberra (Australian National University), Oxford, and Kaiserslautern. In 2009 he joined the Fraunhofer Institute for Industrial Mathematics in Kaiserslautern, where he is now leading the department for optimization of technical processes. His research interests are in modeling, simulating, and optimizing processes in different application domains, including medical therapy planning, mechanical, and chemical engineering. He has realized numerous projects with cooperation partners from the chemical industry, leveraging the benefit of interactive decision support. Since 2021, he is associate professor at the Technical University Kaiserslautern.

Affiliations and Expertise

Head of Department, Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany and Associate Professor, Technical University, Kaiserslautern, Germany.

Norbert Asprion

Norbert Asprion studied mechanical engineering at University of Kaiserslautern, Germany and received his Ph.D. (Supervisor Prof. G. Maurer) in the field of thermodynamics of hydrogen-bonding solutions from Kaiserslautern University in 1996. Since then, he has been with BASF SE in Ludwigshafen. After several positions in process development and six years in technical marketing, he is now product manager for “Computational Engineering Solutions” in the department “Digitalization in Research & Development.” His research interest is focused on process modeling, simulation, and optimization to support decision making in process development. He is a Research Fellow at BASF, a member of the EFCE CAPE working party and the COLaN management board.

Affiliations and Expertise

Research Fellow, BASF SE, Ludwigshafen, Ludwigshafen am Rhein, Germany

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