Optimization for Chemical and Biochemical Engineering: Theory, Algorithms, Modeling and Applications

Vassilios S. Vassiliadis; Walter Kähm; Ehecatl Antonio del Rio Chanona; Ye Yuan

doi:10.1017/9781316227268

Discover the subject of optimization in a new light with this modern and unique treatment. Includes a thorough exposition of applications and algorithms in sufficient detail for practical use, while providing you with all the necessary background in a self-contained manner. Features a deeper consideration of optimal control, global optimization, optimization under uncertainty, multiobjective optimization, mixed-integer programming and model predictive control. Presents a complete coverage of formulations and instances in modelling where optimization can be applied for quantitative decision-making. As a thorough grounding to the subject, covering everything from basic to advanced concepts and addressing real-life problems faced by modern industry, this is a perfect tool for advanced undergraduate and graduate courses in chemical and biochemical engineering.

‘This book offers a very clear, uncluttered presentation of key ideas of optimisation in rigorous form and with plenty of examples from a decade of research and educational experience. It offers an exceptional resource for educators and students of optimisation methods, as well as a valuable reference text to practitioners.’

Alexei Lapkin - University of Cambridge

‘This excellent book brings together important and up-to-date elements of the theory and practice of optimisation with application to chemical and biochemical engineering. It’s an ideal reference for students on advanced courses or for researchers in the field.’

Nilay Shah - Imperial College

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Contents

Frontmatter
pp i-vi
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Contents
pp vii-xi
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Notation
pp xii-xii
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Preface
pp xiii-xiv
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Part I - Overview of Optimization:Applications and Problem Formulations
pp 1-2
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1 - Introduction to Optimization
pp 3-28
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Part II - From General Mathematical Background to General Nonlinear Programming Problems (NLP)
pp 29-30
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2 - General Concepts
pp 31-37
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3 - Convexity
pp 38-43
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4 - Quadratic Functions
pp 44-56
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5 - Minimization in One Dimension
pp 57-62
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6 - Unconstrained Multivariate Gradient-Based Minimization
pp 63-80
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7 - Constrained Nonlinear Programming Problems (NLP)
pp 81-90
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8 - Penalty and Barrier Function Methods
pp 91-100
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9 - Interior Point Methods (IPM’s):A Detailed Analysis
pp 101-112
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Part III - Formulation and Solution of Linear Programming (LP) Problems
pp 113-114
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10 - Introduction to LP Models
pp 115-122
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11 - Numerical Solution of LP Problems Using the Simplex Method
pp 123-132
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12 - A Sampler of LP Problem Formulations
pp 133-145
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13 - Regression Revisited: Using LP to Fit Linear Models
pp 146-150
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14 - Network Flow Problems
pp 151-160
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15 - LP and Sensitivity Analysis, in Brief
pp 161-167
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16 - Multiobjective Optimization
pp 168-194
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17 - Optimization under Uncertainty
pp 195-227
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18 - Mixed-Integer Programming Problems
pp 228-260
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19 - Global Optimization
pp 261-286
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20 - Optimal Control Problems (Dynamic Optimization)
pp 287-311
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21 - System Identification and Model Predictive Control
pp 312-335
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Index
pp 336-338
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Optimization for Chemical and Biochemical Engineering

Theory, Algorithms, Modeling and Applications

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