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Whether it is in finance, supply chain or electricity production, finding the optimal way of designing, operating and maintaining our assets and processes is a key feature of the data-driven world we live in today. Especially in chemical and biochemical industries, a more competitive landscape combined with needs for shorter processing times while maintaining high product quality requires practitioners and researchers alike to learn and apply optimization techniques in their day-to-day work.
This course will provide the participants with the necessary theoretical and practical toolkit to tackle these challenges. We will start with unconstrained optimization, then move through the linear and quadratic programming cases, and cover extensively the modelling of decision making together with dynamic optimization, and finally optimization under uncertainty. These cornerstones of optimization are paired with extensive practical demonstrations and hands-on tutorials, where the participants will get to know how to approach today’s real-life optimization problems and their solutions.”
The course starts with an introduction to general concepts of optimization and unconstrained optimization, i.e. unconstrained optimization (convexity), solution of unconstrained problems (convex/non-convex) and mathematical programming solvers. Moreover, concepts and theory combined with examples on each of the following topics will be covered:
- Linear programming
- Nonlinear programming (convex/non-convex functions/regions; KKT conditions; duality (basics); algorithms and implementations for different type of problems; modelling techniques)
- Mixed-integer programming (modelling with binary variables, logical constraints etc.); algorithms for different types of problems (branch and bound, evolutionary algorithms and links to implementations)
- Special cases of optimization problems (Markov chains, metabolic networks, flow problems etc.)
- Graph theory, dynamic programming, assignment problems
- Computational methods (Differential-algebraic equations (DAEs); numerical methods for DAEs; finite difference methods and variants; method of lines; orthogonal collocation on finite elements
- Dynamic optimization (Dynamic optimization methods; sequential method; simultaneous method)
Jens Abildskov, Associate Professor, Technical University of Denmark
Richard Oberdieck, Numerical Specialist, PhD, Ørsted, Denmark
Luis A. Ricardez-Sandoval, Associate Professor, University of Waterloo
Seyed Soheil Mansouri, Assistant Professor, Technical University of Denmark
Knowledge of Matlab/Python at the start of the course is an advantage, but not a requirement. Assuming that you have access to Matlab/Python, an introduction to these tools with exercises will be given during the course.
Fees and credits
Student: 2000 DKK
Industrial participants: 10,000 DKK
The course is equivalent to 5 ECTS
The participants are expected to cover their own expenses related to the travelling and accommodation.
Registration takes place via course website (contact Gitte Læssøe - firstname.lastname@example.org) and will be confirmed once the payment is done. There is space for 25 persons in the course and the deadline to register is July 30, 2018.
Register here: http://www.conferencemanager.dk/Advanced-Process-Optimization
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