Chapter 7 Applications of Bayesian optimization in chemical engineering
-
Antonio Flores-Tlacuahuac
Abstract
This study examines the utilization of Bayesian optimization in the field of chemical engineering, with a specific focus on the optimization of process design and dynamic product transition tasks. Through the introduction of a data-driven Bayesian approach, this research addresses the challenges inherent in dealing with complex system dynamics and uncertain measurements within the processing industry. The Bayesian optimization algorithm provides a computationally efficient solution that does not rely on intricate mathematical models, rendering it well-suited for real-world applications characterized by the prevalence of noisy data. Through the analysis of three case studies, the proposed approach demonstrates its efficacy in identifying optimal transition trajectories and fulfilling product composition requirements while ensuring the implementation of smooth control actions. The results obtained underscore the value of Bayesian optimization as a valuable tool for optimizing various aspects of chemical processes, including catalyst design, drug formulations, bioprocess parameters, materials design, energy system efficiency, and the reduction of environmental impact. In summary, this work highlights the potential of Bayesian optimization to enhance decision-making processes and drive innovation in the realm of chemical engineering applications.
Abstract
This study examines the utilization of Bayesian optimization in the field of chemical engineering, with a specific focus on the optimization of process design and dynamic product transition tasks. Through the introduction of a data-driven Bayesian approach, this research addresses the challenges inherent in dealing with complex system dynamics and uncertain measurements within the processing industry. The Bayesian optimization algorithm provides a computationally efficient solution that does not rely on intricate mathematical models, rendering it well-suited for real-world applications characterized by the prevalence of noisy data. Through the analysis of three case studies, the proposed approach demonstrates its efficacy in identifying optimal transition trajectories and fulfilling product composition requirements while ensuring the implementation of smooth control actions. The results obtained underscore the value of Bayesian optimization as a valuable tool for optimizing various aspects of chemical processes, including catalyst design, drug formulations, bioprocess parameters, materials design, energy system efficiency, and the reduction of environmental impact. In summary, this work highlights the potential of Bayesian optimization to enhance decision-making processes and drive innovation in the realm of chemical engineering applications.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributing authors VII
- Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
- Chapter 2 Deterministic optimization of distillation processes 25
- Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
- Chapter 4 Metaheuristics for the optimization of chemical processes 113
- Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
- Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
- Chapter 7 Applications of Bayesian optimization in chemical engineering 255
- Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
- Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
- Chapter 10 Optimization under uncertainty in process systems engineering 343
- Chapter 11 Optimal control of batch processes in the continuous time domain 379
- Chapter 12 Supply chain optimization for chemical and biochemical processes 401
- Chapter 13 Future insights for optimization in chemical engineering 425
- Index 445
Chapters in this book
- Frontmatter I
- Contents V
- List of contributing authors VII
- Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
- Chapter 2 Deterministic optimization of distillation processes 25
- Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
- Chapter 4 Metaheuristics for the optimization of chemical processes 113
- Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
- Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
- Chapter 7 Applications of Bayesian optimization in chemical engineering 255
- Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
- Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
- Chapter 10 Optimization under uncertainty in process systems engineering 343
- Chapter 11 Optimal control of batch processes in the continuous time domain 379
- Chapter 12 Supply chain optimization for chemical and biochemical processes 401
- Chapter 13 Future insights for optimization in chemical engineering 425
- Index 445
