Optimisation in Process and Energy Systems Engineering
Optimierung in der Verfahrens- und Energiesystemtechnik
6 CPWinterEN
Prerequisites
Recommended, as written
• Chemical Engineering • Thermodynamics • Process Systems Engineering
Contents
• Introduction: Definition, problem formulation, applications • Linear programming • Non-linear programming • Mixed integer non-linear programming • Dynamic optimization • Stochastic optimization
Learning outcomes
After participating in this module, the students master the basic knowledge, in terms of mathematical optimization methods and tools. Relevant examples from Energy and Process Engineering are used to enhance the understanding of the various tools and methods taught. The focus is on the formulation of the problems and the approaches for their mathematical solution. The methods covered are applied in accompanying calculation exercises.
Assessment
• Written examination (90 min)
Literature (5)
- T. F. Edgar, D. M. Himmelblau, Optimization of Chemical Processes, McGraw-Hill, New York, 2001
- L. T. Biegler, I. E. Grossmann, A. W. Westerberg, Systematic Methods of Chemical Process Design, Prentice Hall, New Jersey, 1997
- C. A. Floudas, Nonlinear and Mixed-Integer Optimization, Oxford University Press, 1995
- J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 2006
- R. Baldick, Applied Optimization, Formulation and Algorithms for Engineering Systems, Cambridge University Press, 2006
Source: BTU's module description · retrieved 29 Sep 2026Original