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Although the improvement in WM performance with cholinergic enhancement was a nonsignificant trend in the current study (P ϭ 0.07), in a previous study (9) with a larger sample (n ϭ 13) the effect was highly significant (P Ͻ 0.001). In the current study, we analyzed RT data for six of our seven subjects because the behavioral data for one subject were(More)
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(More)
SUMMARY Model based optimization can be successfully used to improve the design and operation of bioreactors. In this contribution, we present a review of this area, especially focusing on the dynamic optimization of fed-batch bioreactors, a class of problems which has attracted (and continues to receive) huge attention. Both classical and more novel(More)
Mathematical models of complex biological systems, such as metabolic or cell-signalling pathways, usually consist of sets of nonlinear ordinary differential equations which depend on several non-measurable parameters that can be hopefully estimated by fitting the model to experimental data. However, the success of this fitting is largely conditioned by the(More)
BACKGROUND Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's(More)
The general problem of dynamic optimization of bioprocesses with unspecified final time is considered. Several solution strategies, both deterministic and stochastic, are compared based on their results for three bioprocess case studies. A hybrid (stochastic-deterministic) method is also presented and evaluated, showing significant advantages in terms of(More)
A reduced order model based on a two-time scale separation of the solution of a PDE is developed for the heating of food products with microwaves. This approach is employed on a Model Predictive Control framework to achieve uniformity of different quality parameters. Due to the fact that predictive control requires measurements which are not always possible(More)
This paper presents a systematic approach to efficiently reconstruct the infinite dimensional field in distributed process systems from a limited, and usually reduced, number of sensors. To that purpose, two basic tools are employed: on the one hand, a reduced order representation of the system which, based on proper orthogonal decomposition (POD)(More)