Nonlinear and Mixed-Integer Optimization: Fundamentals and ApplicationsFilling a void in chemical engineering and optimization literature, this book presents the theory and methods for nonlinear and mixed-integer optimization, and their applications in the important area of process synthesis. Other topics include modeling issues in process synthesis, and optimization-based approaches in the synthesis of heat recovery systems, distillation-based systems, and reactor-based systems. The basics of convex analysis and nonlinear optimization are also covered and the elementary concepts of mixed-integer linear optimization are introduced. All chapters have several illustrations and geometrical interpretations of the material as well as suggested problems. Nonlinear and Mixed-Integer Optimization will prove to be an invaluable source--either as a textbook or a reference--for researchers and graduate students interested in continuous and discrete nonlinear optimization issues in engineering design, process synthesis, process operations, applied mathematics, operations research, industrial management, and systems engineering. |
Contents
| 3 | |
PART 1 FUNDAMENTALS OF CONVEX ANALYSIS AND NONLINEAR OPTIMIZATION | 15 |
PART 2 FUNDAMENTALS OF MIXEDINTEGER OPTIMIZATION | 93 |
PART 3 APPLICATIONS IN PROCESS SYNTHESIS | 223 |
Bibliography | 435 |
| 453 | |
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Common terms and phrases
algorithm approach assumption binary variables branch and bound candidate subproblems cold process streams cold stream cold utilities components concave functions constraint qualification continuous variables convex function convex set CSTRs decomposition defined denoted discussed distillation columns dual subproblem EMAT equality constraints feasible solution finite Floudas flow rates formulation global optimum Grossmann heat exchangers heat integration heat loads HEN synthesis hence hot and cold hot utility HRAT illustrative example infimum inlet integer iteration Lagrange function Lagrange multipliers lower bound LP relaxation MILP transshipment model minimum number minimum utility cost MINLP model MINLP problem nonconvex nonlinear equality nonlinear optimization nonlinear programming nonsharp number of matches OA/ER objective function optimal solution optimization problems Outer Approximation primal problem process synthesis quasi-convex reactor network relaxed master problem representation result separation shown in Figure solve subnetwork synthesis problem temperature interval Theorem TIAT upper bound vector