Optimization for Chemical and Biochemical Engineering

Part I. Overview of Optimization: 1. Introduction to optimization; Part II. From General Mathematical Background to General Nonlinear Programming Problems (NLP): 2. General concepts; 3. Convexity; 4. Quadratic functions; 5. Minimization in one dimension; 6. Unconstrained multivariate gradient-based minimization; 7. Constrained nonlinear programming problems (NLP); 8. Penalty and barrier function methods; 9. Interior point methods (IPMs), a detailed analysis; Part III. Formulation and Solution of Linear Programming (LP) Problem Models: 10. Introduction to LP models; 11. Numerical solution of LP problems using the simplex method; 12. A sampler of LP problem formulations; 13. Regression revisited, using LP to fit linear models; 14. Network flow problems; 15, LP and sensitivity analysis, in brief; Part IV. Further Topics in Optimization: 16. Multiobjective optimilzation problem (MOP); 17. Stochastic optimization problem (SOP); 18. Mixed integer programming; 19. Global optimization; 20. Optical control problems (dynamic optimization); 21. System identification and model predictive control.