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Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations

  • Juan Gabriel Segovia-Hernández , Maricruz Juárez-García and Eduardo Sánchez-Ramírez
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Optimization in Chemical Engineering
This chapter is in the book Optimization in Chemical Engineering

Abstract

This chapter delves into the critical role of mathematical optimization in chemical engineering, exploring the challenges and opportunities that shape process design, operation, and control. The intricate complexity of chemical processes, characterized by numerous variables, nonlinearity, high dimensionality, and uncertainty, poses significant challenges for optimization. Advanced optimization techniques, including mixed-integer nonlinear programming, dynamic optimization, and stochastic optimization, are essential for addressing these complexities. This chapter presents substantial opportunities for improving optimization efficiency, promoting sustainability, fostering innovation, and providing robust decision support. By optimizing resource allocation, production scheduling, and energy utilization, chemical engineers can achieve cost savings and operational improvements. Incorporating sustainability metrics into optimization models aids in minimizing environmental impact and enhancing resource efficiency. Furthermore, optimization fosters innovation by enabling novel process configurations and advanced control strategies, driving technological advancements in the field. The chapter also explores future directions in optimization within the context of circular economy, artificial intelligence (AI), and Industry 4.0. Integration of circular economy principles, advancements in AI and machine learning, and digitalization are revolutionizing chemical engineering processes. Multi-objective and multi-scale optimization approaches are increasingly crucial for addressing the complexity of modern chemical engineering systems. Collaborative and interdisciplinary research is emphasized as a key driver of innovation, enabling the development of cutting-edge optimization techniques and tools. Thus, this chapter highlights how optimization in chemical engineering is evolving to meet the demands of efficiency, sustainability, and innovation, paving the way for transformative changes in the industry.

Abstract

This chapter delves into the critical role of mathematical optimization in chemical engineering, exploring the challenges and opportunities that shape process design, operation, and control. The intricate complexity of chemical processes, characterized by numerous variables, nonlinearity, high dimensionality, and uncertainty, poses significant challenges for optimization. Advanced optimization techniques, including mixed-integer nonlinear programming, dynamic optimization, and stochastic optimization, are essential for addressing these complexities. This chapter presents substantial opportunities for improving optimization efficiency, promoting sustainability, fostering innovation, and providing robust decision support. By optimizing resource allocation, production scheduling, and energy utilization, chemical engineers can achieve cost savings and operational improvements. Incorporating sustainability metrics into optimization models aids in minimizing environmental impact and enhancing resource efficiency. Furthermore, optimization fosters innovation by enabling novel process configurations and advanced control strategies, driving technological advancements in the field. The chapter also explores future directions in optimization within the context of circular economy, artificial intelligence (AI), and Industry 4.0. Integration of circular economy principles, advancements in AI and machine learning, and digitalization are revolutionizing chemical engineering processes. Multi-objective and multi-scale optimization approaches are increasingly crucial for addressing the complexity of modern chemical engineering systems. Collaborative and interdisciplinary research is emphasized as a key driver of innovation, enabling the development of cutting-edge optimization techniques and tools. Thus, this chapter highlights how optimization in chemical engineering is evolving to meet the demands of efficiency, sustainability, and innovation, paving the way for transformative changes in the industry.

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/10.1515/9783111383439-001

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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