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Computational Optimization of Chemical and Energy Systems

Computational Optimization of Chemical and Energy Systems

Fengqi You

ISBN: 978-3-527-34344-7

Aug 2018

550 pages

Select type: Hardcover

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A comprehensive textbook on optimization in chemical engineering and energy systems, with a unique balance between theory, computation and applications.
1. Introduction & optimization basics
1.1. Brief introduction of optimization methods and applications
1.2. Basic concepts in optimization
1.2.1. Objective function, constraints, variables, feasible region
1.2.2. Graphical solution approach
1.2.3. Convexity of functions and sets
1.3. Analytical solution of unconstrained optimization problems
1.3.1. Non-convexity and discrete variables
1.3.2. Modeling with integer variables and Boolean expressions
1.4. Logic proposition, modeling of disjunctions

2. Linear programming (LP)
2.1. Applications
2.1.1. Refinery planning
2.1.2. Flux balance analysis
2.1.3. Multi-period optimization
2.2. Theory & algorithms
2.2.1. Simplex algorithm
2.2.2. Duality
2.2.3. Reformulation
2.3. Computation
2.3.1. Introduction to optimization modeling/solution software
2.3.2. Modeling and solving LP problems

3. Mixed-integer linear programming (MILP)
3.1. Applications
3.1.1. Synthesis of distillation sequences
3.1.2. Scheduling of batch processes
3.1.3. Scheduling of electric power systems (unit commitment)
3.1.4. Chemical process network design and planning
3.2. Theory & algorithms
3.2.1. Modeling with discrete decisions
3.2.2. Branch-and-bound algorithm
3.2.3. Valid inequalities and cutting planes
3.2.4. Branch-and-cut method
3.2.5. Disjunctive programming and logic optimization
3.3. Computation
3.3.1. Introduction to MIP solvers
3.3.2. Modeling and solving MILP problems

4. Nonlinear programming (NLP) and dynamic optimization (DO)
4.1. Applications
4.1.1. Real-time optimization of a distillation column
4.1.2. Gasoline blending optimization
4.1.3. Parameter estimation
4.1.4. Optimal power flow problem
4.2. Theory & algorithms
4.2.1. Solution of nonlinear equations
4.2.2. Karush-Kuhn-Tucker optimality conditions
4.2.3. NLP algorithms
4.2.4. Modeling and solving NLP problems
4.2.5. Brief overview of dynamic optimization
4.3. Computation
4.3.1. State-of-the-art NLP solvers
4.3.2. Modeling and solving NLP problems

5. Mixed-integer nonlinear programming (MINLP) and global optimization (GO)
5.1. Applications
5.1.1. Superstructure optimization of energy systems
5.1.2. Life cycle optimization
5.1.3. Energy network synthesis
5.2. Theory & algorithms
5.2.1. MINLP algorithms, including branch & bound, outer approximation, generalized Benders decomposition and extended cutting plane
5.2.2. Reformulation and convexification of nonconvex NLP/MINLP problems
5.2.3. McCormick convex envelope
5.2.4. Spatial branch-and-bound
5.3. Computation
5.3.1. State-of-the-art MINLP solvers
5.3.2. Global optimization software
5.3.3. Modeling and solving MINLP problems with GAMS/DICOPT, SBB
5.3.4. Modeling and solving global optimization problems with GAMS/BARON

6. Special topics on multi-objective optimization, decomposition methods, optimization under uncertainty, and data-driven robust optimization