The main physiological phenomena observed during the grape-must fermentation have been modelled based on a set of biological reactions in which nitrogen compounds such as hexose transporters play a central role. Moreover, a common practice in wine-making is the addition of nitrogen during the batch fermentation so as to boost and shorten the process duration and favour the aromatic-compounds synthesis. A tractable representation of this boost effect has therefore been developed and validated with experimental data.

 In this work NARX-ANN, NARMAX-ANN and NARX-SVM models are compared when acting as software sensors of a relevant state variable for a Solid-substrate cultivation (SSC) process. Results show that NARX-SVM outperforms the other models with an Index of Agreement close to 1.0 even under very noisy conditions thus confirming the claimed superiority of SVM over other black-box techniques for approximating non-linear functions. NARMAX-ANN outperforms NARX-ANN because of its better predictive capabilities.

 We develop a particle filter approximation of the optimal nonlinear filter in the context of the chemostat. We propose a stochastic model of the chemostat together with an observation model. One of the characteristics of applications in bioprocesses is that the time between two observations is relatively large. We account for this point in the development of the particle filter by refining the prediction step of the particulate filter. We present numerical tests on simulated measurements.

 We consider a model of a predator with q size-classes feeding on one resource. The resource is also modelled by a logistic function. This model may have sustained oscillations in dimension (q+1) around an unstable equilibrium. A possible control consists in harvesting every class. We show that (under good assumptions) this model can be reduced to a two-dimensional predator-prey model having sustained oscillations. We design positive controls to suppress the oscillations and to stabilize the equilibrium of the reduced and full model.

 In this paper we consider the optimal control problem consisting of feeding in minimal time a Sequential Batch Reactors (SBR) where several species compete for a single substrate, with the objective being to reach a given (low) level of the substrate. Following [8, Gajardo et al. Minimal Time Sequential Batch Reactors with Bounded and Impulse Controls for One or More Species. SIAM J. Control and Optimization, vol. 47, Issue 6, pp. 2827-2856, 2008], we allow controls to be bounded measurable functions of time plus possible impulses. A suitable modification of the dynamics leads to a slightly different optimal control problem, without impulsive controls, for which we apply different optimality conditions derived from the Pontryagin principle and the Hamilton-Jacobi-Bellman equation. We thus characterize the singular arcs of our problem as the extremal trajectories keeping the substrate at a constant level. We also establish conditions for which a "immediate one impulse" (IOI) strategy is optimal. Some numerical experiences are then included in order to show that those conditions are also necessary to ensure the optimality of the IOI strategy.