Process Optimization

Name: Process Optimization | Decision-Making Tools for Chemical Engineers
Brand: De Gruyter
Price: 74.95 EUR
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Decision-Making Tools for Chemical Engineers

Edwin Zondervan(Author)

De Gruyter (Publisher)

1st Edition

Published on 2. February 2026

VIII, 243 pages

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978-3-11-134228-3 (ISBN)

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After finishing his bachelor with a specialization in process automation in Leeuwarden Edwin continued in Groningen with a M.Sc. in chemical engineering. Then he moved to Enschede and pursued a Ph.D. on modeling, optimization and control of dead-end membrane filtration of surface water. He defended his doctorate at Groningen in 2007.

From 2007 to 2015 he worked as an assistant professor at Eindhoven University of Technology; first joining the PSE group of Prof. Andre de Haan and later collaborating with the polymer reaction engineering group and the product design group of the late prof. Peter Bongers.

During this period he has worked also as associate researcher at the laboratories of Technical University of Catalonia, Carnegie Mellon University, Denmark Technical University and Imperial College .

Besides research Edwin has been very active in the educational area where he trained many generations of students in process design, process control and numerical methods at BA/MA/PDEng and Ph.D. Level. For the latter one Edwin published a textbook that was released in 2014: "A numerical primer for the chemical engineer". In 2019 a second edition was published and beginning of 2020 a new textbook on product-driven process design was released!

From 2015 till 2020 Edwin chaired a new research group on PSE at Bremen University in Germany. Within the excellence initiative he setup a new line of research and education in the energy transition; which resulted in the Advanced Energy Systems Institute of Bremen (AES). In Bremen Edwin was working especially on a large BMWi project on green aviation fuels and he played an important role in the development of an internationalization strategy of Bremen University. He renewed the chemical engineering curriculum in Bremen and designed elementary courses on product and process design, control, dynamics and optimization.

For a short period of time Edwin was active as a senior lecturer on engineering mathematics at Fontys University of applied sciences.

He currently acts as vice-chair of the CAPE WP and holds a delegate role to the QbD and Energy WP's of the EFCE. He also is a reservist at the Dutch military, acting as lieutenant and functional expert on water- and energy.

Recently Edwin joined the sustainable process technology group of Twente university (the Netherlands) and holds the new chair on process systems engineering! With this return he will revive PSE as educational and research discipline in the Netherlands!

Besides research (he published over 100 articles, chapters and patents) Edwin Zondervan has been very active in the educational gremials where he trained many generations of students in process design, process control and numerical methods. Edwin published several books and is the Editor-in-Chief of Physical Sciences Reviews.

Content

Intro
Contents
1 Introduction to Process Optimization
1.1 The Role of Optimization in Chemical Engineering
1.2 Key Objectives of Process Optimization
1.3 Types of Optimization Problems
1.4 Real-World Applications
1.5 Outline of the Book
1.6 Course Adaptability
1.6.1 Undergraduate Course (Introductory Optimization)
1.6.2 Graduate Course (Advanced Optimization)
Further Reading
2 Mathematical Foundations for Optimization
2.1 Review of Calculus for Optimization
2.1.1 Derivatives and Partial Derivatives
2.1.2 Gradients and Directional Derivatives
2.1.3 Hessian Matrix and Curvature
2.2 Linear Algebra Essentials
2.2.1 Matrices and Vectors
2.2.2 Linear Systems and Inverses
2.2.3 Eigenvalues and Positive Definiteness
2.3 Convexity and Concavity
2.3.1 Convex Sets and Functions
2.3.2 Tests for Convexity
2.3.3 Implications for Optimization
2.4 Introduction to Numerical Methods
2.4.1 Finite Differences and Error Analysis
2.4.2 Iterative Methods for Linear Systems
2.4.3 Newton's Method for Solving Nonlinear Equations
2.4.4 Convergence and Stability
2.5 Takeaway
2.6 Exercises
Further Reading
Group Project: Optimization of a Chemical Reactor Design
Project Overview
Project Tasks
Deliverables
3 Formulating Optimization Problems
3.1 Introduction
3.2 Components of an Optimization Problem
3.2.1 Objective Function
3.2.2 Decision Variables
3.2.3 Constraints
3.2.4 The Refinery Blending Problem
3.3 Step-by-Step Problem Formulation
3.3.1 Step 1: Define the System
3.3.2 Step 2: Identify Goals
3.3.3 Step 3: Select Decision Variables
3.3.4 Step 4: Model Relationships
3.3.5 Step 5: Specify Constraints
3.3.6 Step 6: Validate the Formulation
3.4 Practical Techniques for Effective Formulation
3.5 Case Study: Optimizing a Reactor System
3.5.1 Problem Statement
Objectives and Challenges
3.5.2 Formulating the Optimization Problem
3.5.3 Solution Methods
3.6 Common Mistakes and Remedies
3.7 Tools and Software for Formulation
3.8 Takeaway
Further Reading
3.9 Exercises
Tasks for the Team
Deliverables
4 Linear Programming (LP) Methods
4.1 Introduction to Linear Programming
4.2 Formulating LP Problems
4.3 Graphical Solution Method
4.4 The Algebraic Method
4.5 Simplex Method
4.6 Sensitivity Analysis
4.6.1 Shadow Prices (Dual Values)
4.6.2 Reduced Costs
4.7 Linear Programming in Excel
4.8 Exercises
4.9 Takeaway
Further Reading
5 Nonlinear Programming (NLP)
5.1 Introduction to NLP
5.2 Formulating NLP Problems
5.3 Solving NLP Problems
5.3.1 Unconstrained NLP
5.3.2 Constrained NLP
5.4 Challenges in NLP
5.5 Exercises
Group Project: Optimal Design of a Multiproduct Batch Plant
5.6 Takeaway
Further Reading
6 Integer and Mixed-Integer Programming
6.1 Introduction to Discrete Optimization
6.2 Problem Formulation
6.3 Solution Methods
6.3.1 The Outer Approximation Method
6.3.2 Benders Decomposition
6.3.3 Branch and Bound
6.3.4 Cutting Planes
6.4 Linearization
6.4.1 The Glover Linearization
6.4.2 The McCormick Envelope
6.4.3 The Lambda Method
6.5 Logic Inference
6.6 Computational Tools
6.7 Challenges and Workarounds
6.8 Superstructure Optimization
6.9 Takeaway
6.10 Exercises
Appendix A: Demand Table (D_{p,d} in kg)
Further Reading
7 Multi-objective Optimization
7.1 Introduction
7.2 Pareto Optimality and Dominance
7.2.1 The Concept of Pareto Optimality
7.2.2 Dominance: Comparing Solutions
7.2.3 Mathematical Formulation of MOO
7.2.4 Visualizing the Pareto Frontier
7.2.5 Limitations and Practical Nuances
7.2.6 Pareto Optimality, Dominance, and Space Mapping
7.2.6.1 The Production Planning Problem
7.3 Solution Methods for Multi-objective Optimization
7.3.1 Weighted Sum Method
7.3.2 e-Constraint Method
7.3.3 Goal Programming
7.3.4 Evolutionary Algorithms
7.4 Decision Making in MOO
7.5 Applications in Chemical Engineering
7.6 Practical Implementation
7.7 Challenges and Future Directions
7.8 Takeaways
Further Reading
7.9 Exercises
8 Decision-Making Under Uncertainty
8.1 Introduction
8.2 Sources of Uncertainty in Chemical Processes
8.3 Mathematical Frameworks for Uncertainty
8.3.1 Stochastic Programming
8.3.2 Robust Optimization
8.3.2.1 Comparative Analysis with Stochastic Programming
8.3.2.2 Adjustable Robust Optimization
8.3.2.3 Implementation Considerations
8.3.2.4 Extensions and Advanced Formulations
8.3.3 Chance-Constraint Programming
8.4 Solution Methods
8.4.1 Monte Carlo Optimization
8.4.2 Decomposition Techniques
Further Reading
8.5 Exercises
9 Optimization in Current and Future Applications
9.1 Introduction
9.1.1 Why This Chapter Matters?
9.2 Data Regression
9.3 Process Control
9.3.1 Optimal Control
9.3.2 Control System Design
9.4 Process Simulation and Flowsheet Optimization
9.4.1 Equation-Oriented Versus Sequential Modular Approaches
9.4.2 Handling Recycles and Tear Streams
9.4.3 Design Specifications and Sensitivity Analysis
9.4.4 Flowsheet Optimization
9.4.5 Challenges and Numerical Considerations
9.4.6 Future Directions
9.5 Batch Scheduling
9.5.1 Continuous Versus Batch Processing: Key Considerations
9.5.2 Batch Scheduling: Fundamentals and Strategies
9.5.3 Advanced Scheduling with State-Task Networks and Mathematical Optimization
9.5.4 Practical Implementation and Tools
9.6 Enterprise-Wide Optimization
9.6.1 Scheduling and Planning in EWO
9.6.2 Optimization Techniques and Emerging Challenges
9.6.3 Future Directions and Strategic Implications
9.7 Bayesian and Probabilistic Optimization
9.7.1 Why Chemical Engineers Use BO
Implementation Example
Key Applications
Limitations
9.8 Metaheuristic Methods
9.8.1 Core Principles
9.8.2 Genetic Algorithms (GA)
9.8.3 Particle Swarm Optimization (PSO)
9.8.4 Simulated Annealing (SA)
9.8.4.1 A Python Implementation: Catalyst Blend Optimization
9.9 Artificial Intelligence and Machine Learning
9.10 Quantum Computing in Chemical Engineering
9.10.1 Qubits and Superposition
9.10.2 Entanglement and Interference
9.10.3 Quantum Algorithms
9.10.4 Mapping PSE Problems to Quantum Formats
9.10.4.1 Qiskit Code (Python Simulation)
9.10.5 Why Qubits Matter for Chemical Engineers?
9.10.5.1 A Practical Example: Optimizing Reactor Scheduling
9.10.5.2 Current Challenges and Realistic Expectations
9.11 Emerging Frontiers
9.11.1 Quantum Computing for Molecular Design
9.11.2 Autonomous Self-Optimizing Processes
9.11.3 Physics-Informed Neural Networks
9.11.4 Edge AI for Distributed Optimization
9.11.5 Sustainable Process Intensification
9.11.6 Challenges and Horizons
Further Reading
10 GAMS Tutorial
10.1 Introduction
10.2 The General Algebraic Modeling System
10.3 An NLP Example
10.4 Solving an Integer Programming Problem
10.5 Takeaways
Further Reading
10.6 Exercises
Index

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Process Optimization

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