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In silico experimentation has opened new ways to analyze biological systems behavior under different conditions. The incorporation of an outer optimization loop may help to find the right operation conditions to achieve specific goals (maximization of a given product concentration, minimization of process energy/time, etc.). Mathematically, this is stated as a dynamic optimization problem being particularly challenging when the system is described by nonlinear sets of partial differential equations as well as when constraints are considered. These issues impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. In this work, the control vector parametrization approach is combined with reduced order methods and suitable hybrid global optimization methods to overcome such difficulties. The capabilities of this strategy are illustrated considering the solution of two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems

This work presents the theoretical design and the experimental validation of a real time optimisation logic for distributed parameter systems. This logic consists of a hierarchy of two layers. The upper layer is responsible of obtaining the optimal control profile by a suitable combination of the control vector parameterisation approach with a hybrid global-local optimiser and the use of reduced models for distributed parameter systems. The lower layer corresponds to a PID controller designed in the framework of Internal Model Control to keep tracking capabilities at short time scales. This logic was applied to the real time optimisation of the thermal processing of packaged foods in batch retorts at the pilot plant available at the IIM-CSIC. The objective was to maximise food product quality while satisfying safety constraints. The proposed scheme was able to optimally operate the system under standard plant perturbations and under a pressure drop

In this work, the dissipative nature of spatially distributed bioprocess systems is exploited to develop efficient state observers based on a low dimensional dynamic representation of the original set of partial differential equations. The approach we suggest combines standard observer design techniques for bioreactors with efficient model reduction methodologies based on projection of the original concentration fields on low dimensional subspaces capturing the slow dynamics of the process. Aspects related with the location of sensors and their influence on the ability to reconstruct concentration fields will also be considered. Finally, the different aspects of the methodology, as well as the efficiency of the resulting observers will be illustrated on a case study of industrial interest, namely a tubular bioreactor producing gluconic acid by Aspergillus Niger