Abstract
In this contribution, we will focus on problems arising in the context of biochemical process engineering. Many of these problems can be stated as the optimization of non-linear dynamic systems. Relevant classes in this domain are (i) optimal control problems (dynamic optimization), (ii) inverse problems (parameter estimation), and (iii) simultaneous design and control optimization problems. Most of these problems are, or can be transformed to, nonlinear programming problems subject to differential-algebraic constraints. It should be noted that their highly constrained and non-linear nature often causes non-convexity, thus global optimization methods are needed to find suitable solutions.
Here, we will present our experiences regarding the use of several stochastic, deterministic and hybrid global optimization methods to solve those problems. Several parallel versions of the most promising methods, which are able to run on standard clusters of PCs, will also be presented. Results for selected challenging case studies will be given.
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Abbreviations
- ACO:
-
ant colony optimization
- BVP:
-
boundary value problem
- CP:
-
complete (state and control) parameterization
- CVP:
-
control vector parameterization
- DAEs:
-
differential algebraic equations
- DE:
-
differential evolution
- EC:
-
evolutionary computation
- EP:
-
evolutionary programming
- ES:
-
evolution strategy
- GA:
-
genetic algorithm
- ICRS:
-
Integrated Controlled Random Search
- ISE:
-
integral square error
- LJ:
-
Luus-Jaakola
- MIOCP:
-
mixed integer optimal control problem
- NFL:
-
no free lunch (theorem)
- NLP:
-
nonlinear programming
- ODEs:
-
ordinary differential equations
- PDE:
-
partial differential equation
- PI:
-
proportional integral (controller)
- SA:
-
simulated annealing
- SQP:
-
sequential quadratic programming
- SRES:
-
Stochastic Ranking Evolution Strategy
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Banga, J.R., Moles, C.G., Alonso, A.A. (2004). Global Optimization of Bioprocesses using Stochastic and Hybrid Methods. In: Floudas, C.A., Pardalos, P. (eds) Frontiers in Global Optimization. Nonconvex Optimization and Its Applications, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0251-3_3
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