2024-03-28T12:15:10Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/558322022-12-20T08:52:56Zcom_10261_47com_10261_8col_10261_300
http://hdl.handle.net/10261/55832
10.1016/j.jbiotec.2005.02.013
58110
Dynamic optimization of bioprocesses: Efficient and robust numerical strategies
Elsevier
2005
artículo
Banga, Julio R.
rp14623
Balsa-Canto, Eva
rp13652
Moles, Carmen G.
Alonso, Antonio A.
Optimal control
Dynamic optimization
Non-linear bioprocesses
Global optimization
2005
13 páginas, 11 figuras
The dynamic optimization (open loop optimal control) of non-linear bioprocesses is considered in this contribution. These processes can be described by sets of non-linear differential and algebraic equations (DAEs), usually subject to constraints in the state and control variables. A review of the available solution techniques for this class of problems is presented, highlighting the numerical difficulties arising from the non-linear, constrained and often discontinuous nature of these systems.
In order to surmount these difficulties, we present several alternative stochastic and hybrid techniques based on the control vector parameterization (CVP) approach. The CVP approach is a direct method which transforms the original problem into a non-linear programming (NLP) problem, which must be solved by a suitable (efficient and robust) solver. In particular, a hybrid technique uses a first global optimization phase followed by a fast second phase based on a local deterministic method, so it can handle the nonconvexity of many of these NLPs.
The efficiency and robustness of these techniques is illustrated by solving several challenging case studies regarding the optimal control of fed-batch bioreactors and other bioprocesses. In order to fairly evaluate their advantages, a careful and critical comparison with several other direct approaches is provided. The results indicate that the two-phase hybrid approach presents the best compromise between robustness and efficiency
Journal of Biotechnology
2005
117
407
419