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Chapter 5 Surrogate-based optimization techniques for process systems engineering

  • Mathias Neufang , Emma Pajak , Damien van de Berg , Ye Seol Lee and Ehecatl Antonio Del Rio Chanona
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Optimization in Chemical Engineering
This chapter is in the book Optimization in Chemical Engineering

Abstract

Optimization plays an important role in chemical engineering, impacting cost-effectiveness, resource utilization, product quality, and process sustainability metrics. This chapter broadly focuses on data-driven optimization, particularly on modelbased derivative-free techniques, also known as surrogate-based optimization. The chapter introduces readers to the theory and practical considerations of various algorithms, complemented by a performance assessment across multiple dimensions, test functions, and two chemical engineering case studies: a stochastic high-dimensional reactor control study and a low-dimensional constrained stochastic reactor optimization study. This assessment sheds light on each algorithm’s performance and suitability for diverse applications. Additionally, each algorithm is accompanied by background information, mathematical foundations and algorithm descriptions. Among the discussed algorithms are Bayesian optimization (BO), including state-of-the-art trust region BO (TuRBO), constrained optimization by linear approximation (COBYLA), the ensemble tree model optimization tool (ENTMOOT) that uses decision trees as surrogates, stable noisy optimization by branch and fit (SNOBFIT), methods that use radial basis functions (RBFs) such as dynamic coordinate search (DYCORS) and stochastic RBFs (SRBFStrategy), constrained optimization by quadratic approximations (COBYQA), as well as a few others recognized for their effectiveness in surrogate-based optimization. By combining theory with practice, this chapter equips readers with the knowledge to integrate surrogatebased optimization techniques into chemical engineering. The overarching aim is to highlight the advantages of surrogate-based optimization, introduce state-of-the-art algorithms, and provide guidance for successful implementation within process systems engineering.

Abstract

Optimization plays an important role in chemical engineering, impacting cost-effectiveness, resource utilization, product quality, and process sustainability metrics. This chapter broadly focuses on data-driven optimization, particularly on modelbased derivative-free techniques, also known as surrogate-based optimization. The chapter introduces readers to the theory and practical considerations of various algorithms, complemented by a performance assessment across multiple dimensions, test functions, and two chemical engineering case studies: a stochastic high-dimensional reactor control study and a low-dimensional constrained stochastic reactor optimization study. This assessment sheds light on each algorithm’s performance and suitability for diverse applications. Additionally, each algorithm is accompanied by background information, mathematical foundations and algorithm descriptions. Among the discussed algorithms are Bayesian optimization (BO), including state-of-the-art trust region BO (TuRBO), constrained optimization by linear approximation (COBYLA), the ensemble tree model optimization tool (ENTMOOT) that uses decision trees as surrogates, stable noisy optimization by branch and fit (SNOBFIT), methods that use radial basis functions (RBFs) such as dynamic coordinate search (DYCORS) and stochastic RBFs (SRBFStrategy), constrained optimization by quadratic approximations (COBYQA), as well as a few others recognized for their effectiveness in surrogate-based optimization. By combining theory with practice, this chapter equips readers with the knowledge to integrate surrogatebased optimization techniques into chemical engineering. The overarching aim is to highlight the advantages of surrogate-based optimization, introduce state-of-the-art algorithms, and provide guidance for successful implementation within process systems engineering.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. List of contributing authors VII
  4. Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
  5. Chapter 2 Deterministic optimization of distillation processes 25
  6. Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
  7. Chapter 4 Metaheuristics for the optimization of chemical processes 113
  8. Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
  9. Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
  10. Chapter 7 Applications of Bayesian optimization in chemical engineering 255
  11. Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
  12. Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
  13. Chapter 10 Optimization under uncertainty in process systems engineering 343
  14. Chapter 11 Optimal control of batch processes in the continuous time domain 379
  15. Chapter 12 Supply chain optimization for chemical and biochemical processes 401
  16. Chapter 13 Future insights for optimization in chemical engineering 425
  17. Index 445
https://doi.org/10.1515/9783111383439-005

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. List of contributing authors VII
  4. Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
  5. Chapter 2 Deterministic optimization of distillation processes 25
  6. Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
  7. Chapter 4 Metaheuristics for the optimization of chemical processes 113
  8. Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
  9. Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
  10. Chapter 7 Applications of Bayesian optimization in chemical engineering 255
  11. Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
  12. Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
  13. Chapter 10 Optimization under uncertainty in process systems engineering 343
  14. Chapter 11 Optimal control of batch processes in the continuous time domain 379
  15. Chapter 12 Supply chain optimization for chemical and biochemical processes 401
  16. Chapter 13 Future insights for optimization in chemical engineering 425
  17. Index 445
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